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page you requested cannot be found (perhaps it was moved or renamed). - -You may want to try searching to find the page's new location. diff --git a/README.md b/README.md new file mode 100644 index 0000000..1bc1d94 --- /dev/null +++ b/README.md @@ -0,0 +1,5 @@ +```sh +quarto preview . --port 3000 --no-browser +``` + +https://coolors.co/fefeff-c74042-bc2021-803ba1-b690ca-6cad5f-2a8a14 \ No newline at end of file diff --git a/_extensions/pat-alt/julia/_extension.yml b/_extensions/pat-alt/julia/_extension.yml deleted file mode 100644 index 9de9fcd..0000000 --- a/_extensions/pat-alt/julia/_extension.yml +++ /dev/null @@ -1,13 +0,0 @@ -title: Julia -author: pat-alt -version: 1.0.0 -quarto-required: ">=1.3" -contributes: - formats: - html: - toc: true - theme: [default, custom.scss] - revealjs: - theme: [default, custom.scss, revealjs.scss] - - diff --git a/_extensions/pat-alt/julia/custom.scss b/_extensions/pat-alt/julia/custom.scss deleted file mode 100644 index 52f299e..0000000 --- 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gsoc\n - AI/ML\n - imaging\n - gpu\n - analysis\n---\n\n# πŸ“πŸ©»πŸ“ŽπŸ“‰ ➑️ πŸ—ƒοΈπŸ“šβ™»οΈπŸ§‘β€πŸ« ➑️ πŸ€–πŸ‘οΈπŸ“ˆ ➑️ β€οΈβ€πŸ©Ή \n*These emoticons may resemble **hieroglyphics**, but very soon you will realize that they **mean more than 1000s** of lines of code.*\n\n
\n Description of the emojis used in the title\n \n\n
\n\n
\nIn this post, I'd like to summarize what I did this summer and everything I learned along the way, rebuilding the MedPipe3D medical imaging pipeline. I will not start typically, but so that anyone even a novice can visualize what this project has achieved, while the latter part is intended for more experienced readers. It will be easiest to divide it into 4 steps separated by ➑️ in the title above. Each emoji stands for a different piece of pipeliner and will be described below.\n\nπŸ“πŸ©»πŸ“ŽπŸ“‰ **What we need from the user**\n\nMedPipe3D requires four essential inputs from the user to get started: a clear action plan πŸ“, 3D medical images like MRI scans 🩻, an AI model πŸ“Ž, and a loss function πŸ“‰.\n\nπŸ—ƒοΈπŸ“šβ™»οΈπŸ§‘β€πŸ« **The Pipeline essential AI manufacturing line**\n\nFollowing the plan πŸ“, MedPipe3D loads data, pre-processes, and organizes it πŸ—ƒοΈ. Allowing data to be easily split πŸ“š, and efficiently augmented ♻️ in many ways for learning AI πŸ§‘β€πŸ« model effectively. In the end, performing testing and post-processing for better determination of AI skills. \nIt's designed to transform raw medical data into a format that your AI can learn from, segmenting meaningful patterns and structures.\n\nπŸ€–πŸ‘οΈπŸ“ˆ **Results and Insights**\n\nMedPipe3D is a tool for researchers and for that, it cannot do without analysis, testing, and evaluation. The result of the pipeline is a model πŸ€– as well as data πŸ‘οΈ and logs πŸ“ˆ needed in MedEval3D that are ready for visualization and further analysis with MedEye3D. In a nutshell, it makes visualizing results easy, tumor locations or other medical features directly as masks on the scans.\n\nβ€οΈβ€πŸ©Ή **Purpose-Driven Technology**\n\nMedPipe3D's mission goes beyond technology. It's about providing the tools to create AIs that support healthcare professionals in making faster, more accurate decisions, with the ultimate goal of saving lives.\n\nThis four-part journey captures the heart of the MedPipe3D toolkit for advancing medical AI, from raw data to life-saving insight.\n\n## Introduction\n\n**MedPipe3D** is a framework created from hundreds of hours over summer vacation, thousands of lines of code, hundreds of mistakes, and most importantly the guidance of my mentor and author of all of these libraries Dr. [Jakub Mitura](https://www.linkedin.com/in/jakub-mitura-7b2013151/).\nAt its core, MedPipe3D combines sophisticated data handling from **MedImage** thanks to the hard work of [Divyansh Goyal](https://www.linkedin.com/in/divyansh-goyal-34654b200/). Newly developed pipeline for model training, validation, and testing with existing **MedEval3D**, and result visualization with **MedEye3D**.\nUnfortunately, not all of the project's goals have been fully achieved, and thereby there is one section ➑️ too many. Hopefully not for long. My name is [Jan Zubik](https://www.linkedin.com/in/janzubik/), and I wrote this entire library from scratch, which is currently my most complex project.\n\nIf you are a data scientist, programmer, or code enthusiast, I invite you to read the next section where I go into detail and present **version 1** of this tool in detail.\n\nI'm a 3rd-year student of BSc in Data Science and Machine Learning, I know that many things can be done better, expanded, debugged, and optimized. Now it just works, **but don't hesitate to write to me personally** on [LinkedIn](https://www.linkedin.com/in/janzubik/), [Julia's Slack](https://julialang.slack.com/team/U06L685B6TD) or [GitHub](https://github.com/JanZubik)!\nWith your comments, and direct critique **you will help me** to be a better programmer and one day MedPipe3D will contribute in a tiny way to save someone's life!\n\nExact work from the Google Summer of Code project you will find in [GitHub the repository.](https://github.com/JuliaHealth/MedPipe3D.jl/tree/GSoC-'24-MedPipe3D)\n\n\n# Project Goals\n\nThe primary goal was to develop MedPipe3D and enhance MedImage, a Julia package designed to streamline the process of GPU-accelerated medical image segmentation. The project aimed to merge existing librariesβ€”MedEye3D, MedEval3D, and MedImageβ€”into a cohesive pipeline that facilitates advanced data handling, preprocessing, augmentation, model training, validation, testing with post-processing and visualization for medical imaging applications.\n\n\n\n# Tasks\n\n- πŸ†™ - Fully finished, with great potential for further development\n- βœ… - Fully completed\n- ⚠️ - Partially uncompleted\n- ❌ - Unreached\n\nFull list of all major parts and minor tasks (all tasks set up in the original GSOC plan were completed at least minimum level, and many additional improvements above minimum were implemented)\n
\n\n1. **Helpful functions to support the MedImage format βœ…**\n - Debugging rotations βœ…\n - Crop MedImage or 3D array βœ…\n - Pad MedImage or 3D array βœ…\n - Pad with edge values βœ…\n - Calculating the average of the edges of the picture πŸ†™\n\n2. **Integrate Augmentations for Medical Data βœ…**\n - Brightness transform βœ…\n - Contrast augmentation transform βœ…\n - Gamma Transform βœ…\n - Gaussian noise transform βœ…\n - Rician noise transform βœ…\n - Mirror transform βœ…\n - Scale transform πŸ†™\n - Gaussian blur transform βœ…\n - Simulate low-resolution transform πŸ†™\n - Elastic deformation transform πŸ†™\n\n3. **Develop a Pipeline ⚠️**\n - Structured configuration of all hyperparameters πŸ†™\n - Interactive creation of configuration βœ…\n - Creating a structured configuration of hyperparameters in JSON πŸ†™\n - Loading data into HDF5 βœ…\n - Cropping and padding to real coordinates of the main picture βœ…\n - Calculate Median and Mean Spacing with resampling πŸ†™\n - Cropping and padding to specific or average dimensions βœ…\n - Standardization and normalization βœ…\n - Managing index groups (channels) for batch requirements in HDF5 βœ…\n - Divide into train, validation, test specified as % βœ…\n - Divide with a specific division specified in JSON βœ…\n - Equal distribution when there are multiple classes βœ…\n - Extracting data and creating 5-dimensional tensors for batched learning βœ…\n - Hole images data loading βœ…\n - Patch-based data loading with probabilistic oversampling βœ…\n - Obtaining the necessary elements for learning βœ…\n - Get optimizer, loss function, and performance metrics βœ…\n - Apply augmentations βœ…\n - Train βœ…\n - Initializing model βœ…\n - The learning epoch βœ…\n - Epoch with early stopping functionality βœ…\n - Inferring βœ…\n - Validation βœ…\n - Evaluate metric βœ…\n - Evaluate validation loss βœ…\n - Validation with largest connected componentβœ…\n - Testing βœ…\n - Evaluate test set βœ…\n - Invertible augmentations evaluation βœ…\n - Patch-based invertible augmentations evaluation βœ…\n - Logging ⚠️\n - Returning the necessary results ⚠️\n - Logging connection to TensorBoard ❌\n - Logging errors and warnings ❌\n - Visualization ⚠️\n - Returning data in Nifti format βœ…\n - Automated visualization in MedEye3D ❌\n\n4. **Optimize Performance with GPU Acceleration**\n - Augmentations βœ…\n - Learning, Validation, Testing βœ…\n - Largest connected component βœ…\n\n5. **Documentation ⚠️**\n - Comments in important places in the code ⚠️\n - Documentation of the function ⚠️\n - Read me ⚠️\n - Documentation on juliahealth.org ❌\n\n
\n\n## Integrate augmentations for medical data πŸ†™\nAugmenting medical data is a crucial step for enhancing model robustness, especially given the variations in imaging conditions and patient anatomy. \n\n- This pipeline currently supports multiple augmentation techniques:\n - Brightness transform βœ…\n - Contrast augmentation transform βœ…\n - Gamma Transform βœ…\n - Gaussian noise transform βœ…\n - Rician noise transform βœ…\n - Mirror transform βœ…\n - Scale transform πŸ†™\n - Gaussian blur transform βœ…\n - Simulate low-resolution transform πŸ†™\n - Elastic deformation transform πŸ†™\n\nWhich have been fully integrated. Each of these methods helps the model generalize better by simulating diverse imaging scenarios.\n\n![](./Augmentations.png)\n\nComments:\n\nAugmentations such as scaling, and low-resolution simulation use interpolation that is not yet GPU-accelerated.\n\nElastic deformation with simulation of different tissue elasticities is a potential development opportunity that would further improve the model's adaptability by mimicking more complex variations found in medical imaging.\n\n## Invertible augmentations and support test time augmentations πŸ†™\nThis section focuses on the ability to apply reversible augmentations to test data, allowing the model to be evaluated with different transformations. Only rotation is available at this time. The function `evaluate_patches` performs this evaluation by applying specified augmentations, dividing the test data into patches, and reconstructing the full image from the patches. During testing, one can choose to use of largest connected component post-processing. Metrics are calculated and results are saved for analysis.\n\n
\nevaluate_test:\n\n```julia\n# ...\nfor test_group in test_groups\n test_data, test_label, attributes = fetch_and_preprocess_data([test_group], h5, config)\n results, test_metrics = evaluate_patches(test_data, test_label, tstate, model, config)\n y_pred, metr = process_results(results, test_metrics, config)\n save_results(y_pred, attributes, config)\n push!(all_test_metrics, metr)\nend\n# ...\n```\n\n```julia\nfunction evaluate_patches(test_data, test_label, tstate, model, config, axis, angle)\n println(\"Evaluating patches...\")\n results = []\n test_metrics = []\n tstates = [tstate]\n test_time_augs = []\n\n for i in config[\"learning\"][\"n_invertible\"]\n data = rotate_mi(test_data, axis, angle)\n for tstate_curr in tstates\n patch_results = []\n patch_size = Tuple(config[\"learning\"][\"patch_size\"])\n idx_and_patches, paded_data_size = divide_into_patches(test_data, patch_size)\n coordinates = [patch[1] for patch in idx_and_patches]\n patch_data = [patch[2] for patch in idx_and_patches]\n for patch in patch_data\n y_pred_patch, _ = infer_model(tstate_curr, model, patch)\n push!(patch_results, y_pred_patch)\n end\n idx_and_y_pred_patch = zip(coordinates, patch_results)\n y_pred = recreate_image_from_patches(idx_and_y_pred_patch, paded_data_size, patch_size, size(test_data))\n if config[\"learning\"][\"largest_connected_component\"]\n y_pred = largest_connected_component(y_pred, config[\"learning\"][\"n_lcc\"])\n end\n metr = evaluate_metric(y_pred, test_label, config[\"learning\"][\"metric\"])\n push!(test_metrics, metr)\n end\n end\n return results, test_metrics\nend\n```\n\n```julia\nfunction divide_into_patches(image::AbstractArray{T, 5}, patch_size::Tuple{Int, Int, Int}) where T\n println(\"Dividing image into patches...\")\n println(\"Size of the image: \", size(image)) \n\n # Calculate the required padding for each dimension (W, H, D)\n pad_size = (\n (size(image, 1) % patch_size[1]) != 0 ? patch_size[1] - size(image, 1) % patch_size[1] : 0,\n (size(image, 2) % patch_size[2]) != 0 ? patch_size[2] - size(image, 2) % patch_size[2] : 0,\n (size(image, 3) % patch_size[3]) != 0 ? patch_size[3] - size(image, 3) % patch_size[3] : 0\n )\n\n # Pad the image if necessary\n padded_image = image\n if any(pad_size .> 0)\n padded_image = crop_or_pad(image, (size(image, 1) + pad_size[1], size(image, 2) + pad_size[2], size(image, 3) + pad_size[3]))\n end\n\n # Extract patches\n patches = []\n for x in 1:patch_size[1]:size(padded_image, 1)\n for y in 1:patch_size[2]:size(padded_image, 2)\n for z in 1:patch_size[3]:size(padded_image, 3)\n patch = view(\n padded_image,\n x:min(x+patch_size[1]-1, size(padded_image, 1)),\n y:min(y+patch_size[2]-1, size(padded_image, 2)),\n z:min(z+patch_size[3]-1, size(padded_image, 3)),\n :,\n :\n )\n push!(patches, [(x, y, z), patch])\n end\n end\n end\n println(\"Size of padded image: \", size(padded_image))\n return patches, size(padded_image)\nend\n\nfunction recreate_image_from_patches(\n coords_with_patches,\n padded_size,\n patch_size,\n original_size\n)\n println(\"Recreating image from patches...\")\n reconstructed_image = zeros(Float32, padded_size...)\n \n # Place patches back into their original positions\n for (coords, patch) in coords_with_patches\n x, y, z = coords\n reconstructed_image[\n x:x+patch_size[1]-1,\n y:y+patch_size[2]-1,\n z:z+patch_size[3]-1,\n :,\n :\n ] = patch\n end\n\n # Crop the reconstructed image to remove any padding\n final_image = reconstructed_image[\n 1:original_size[1],\n 1:original_size[2],\n 1:original_size[3],\n :,\n :\n ]\n println(\"Size of the final image: \", size(final_image))\n return final_image\nend\n```\n
\n\nComment:
\nIn this section, there is significant potential to incorporate additional types of invertible augmentations.\n\n## Patch-based data loading with probabilistic oversampling βœ…\nIn this section, patches are extracted using `extract_patch` from the medical images for model training, with a probability-based method to decide between a random patch or a patch with non-zero labels.\nHelper functions like `get_random_patch` and `get_centered_patch` determine the starting indices and dimensions for the patches based on given configurations, while padding methods ensure consistency even if the patch exceeds the original image dimensions. Probabilistic oversampling, as configured, allows for more balanced and informative data sampling, which improves the model's ability to detect specific medical features.\n\n\n
\nextract_patch:\n\n```julia\nfunction extract_patch(image, label, patch_size, config)\n # Fetch the oversampling probability from the config\n println(\"Extracting patch.\")\n oversampling_probability = config[\"learning\"][\"oversampling_probability\"]\n # Generate a random number to decide which patch extraction method to use\n random_choice = rand()\n\n if random_choice <= oversampling_probability\n return extract_nonzero_patch(image, label, patch_size)\n else\n\n return get_random_patch(image, label, patch_size)\n end\nend\n#Helper function, in case the mask is emptyClick to apply\nfunction extract_nonzero_patch(image, label, patch_size)\n println(\"Extracting a patch centered around a non-zero label value.\")\n indices = findall(x -> x != 0, label)\n if isempty(indices)\n # Fallback to random patch if no non-zero points are found\n return get_random_patch(image, label, patch_size)\n else\n # Choose a random non-zero index to center the patch around\n center = indices[rand(1:length(indices))]\n return get_centered_patch(image, label, center, patch_size)\n end\nend\n# Function to get a patch centered around a specific index\nfunction get_centered_patch(image, label, center, patch_size)\n center_coords = Tuple(center)\n half_patch = patch_size .Γ· 2\n start_indices = center_coords .- half_patch\n end_indices = start_indices .+ patch_size .- 1\n\n # Calculate padding needed\n pad_beg = (\n max(1 - start_indices[1], 0),\n max(1 - start_indices[2], 0),\n max(1 - start_indices[3], 0)\n )\n pad_end = (\n max(end_indices[1] - size(image, 1), 0),\n max(end_indices[2] - size(image, 2), 0),\n max(end_indices[3] - size(image, 3), 0)\n )\n\n # Adjust start_indices and end_indices after padding\n start_indices_adj = start_indices .+ pad_beg\n end_indices_adj = end_indices .+ pad_beg\n\n # Convert padding values to integers\n pad_beg = Tuple(round.(Int, pad_beg))\n pad_end = Tuple(round.(Int, pad_end))\n\n # Pad the image and label using pad_mi\n image_padded = pad_mi(image, pad_beg, pad_end, 0)\n label_padded = pad_mi(label, pad_beg, pad_end, 0)\n\n # Extract the patch\n image_patch = image_padded[\n start_indices_adj[1]:end_indices_adj[1],\n start_indices_adj[2]:end_indices_adj[2],\n start_indices_adj[3]:end_indices_adj[3]\n ]\n label_patch = label_padded[\n start_indices_adj[1]:end_indices_adj[1],\n start_indices_adj[2]:end_indices_adj[2],\n start_indices_adj[3]:end_indices_adj[3]\n ]\n\n return image_patch, label_patch\nend\n\nfunction get_random_patch(image, label, patch_size)\n println(\"Extracting a random patch.\")\n # Check if the patch size is greater than the image dimensions\n if any(patch_size .> size(image))\n # Calculate the needed size to fit the patch\n needed_size = map(max, size(image), patch_size)\n # Use crop_or_pad to ensure the image and label are at least as large as needed_size\n image = crop_or_pad(image, needed_size)\n label = crop_or_pad(label, needed_size)\n end\n\n # Calculate random start indices within the new allowable range\n start_x = rand(1:size(image, 1) - patch_size[1] + 1)\n start_y = rand(1:size(image, 2) - patch_size[2] + 1)\n start_z = rand(1:size(image, 3) - patch_size[3] + 1)\n start_indices = [start_x, start_y, start_z]\n end_indices = start_indices .+ patch_size .- 1\n\n # Extract the patch directly when within bounds\n image_patch = image[start_indices[1]:end_indices[1], start_indices[2]:end_indices[2], start_indices[3]:end_indices[3]]\n label_patch = label[start_indices[1]:end_indices[1], start_indices[2]:end_indices[2], start_indices[3]:end_indices[3]]\n\n return image_patch, label_patch\nend\n\n```\n
\n\n## Calculate Median and Mean Spacing with resampling πŸ†™\nThis part ensures that all images in the dataset have consistent real coordinates, spacing, and shape. It's a critical factor in medical imaging for accurate analysis. Calculating and applying set values, median or mean across images ensures uniformity.\n\n#### Resample images to target image πŸ†™\nThis step aligns each image to the reference coordinates of the main image, ensuring that all images share a common spatial alignment. The `resample_to_image` function from MedImage.jl is used here, applying interpolation to adjust each image.\n\n\n
\nresample_images_to_target:\n\n```julia\nif resample_images_to_target && !isempty(Med_images)\n println(\"Resampling $channel_type files in channel '$channel_folder' to the first $channel_type in the channel.\")\n reference_image = Med_images[1]\n Med_images = [resample_to_image(reference_image, img, interpolator) for img in Med_images]\nend\n```\n
\n\nComment:
\n`Resample_to_image` uses interpolation that is not yet GPU-accelerated in this implementation, this step slows down the data preparation phase significantly.\n\n#### Ensure uniform spacing across the entire dataset πŸ†™\nThis step brings all images to a consistent voxel spacing across the dataset using `resample_to_spacing` from MedImage.jl. This uniform spacing is crucial for creating a standardized dataset where each image voxel represents the same physical volume.\n\n\n
\nesample_to_spacing:\n\n```julia\nif resample_images_spacing == \"set\"\n println(\"Resampling all $channel_type files to target spacing: $target_spacing\")\n target_spacing = Tuple(Float32(s) for s in target_spacing)\n channels_data = [[resample_to_spacing(img, target_spacing, interpolator) for img in channel] for channel in channels_data]\nelseif resample_images_spacing == \"avg\"\n println(\"Calculating average spacing across all $channel_type files and resampling.\")\n all_spacings = [img.spacing for channel in channels_data for img in channel]\n avg_spacing = Tuple(Float32(mean(s)) for s in zip(all_spacings...))\n println(\"Average spacing calculated: $avg_spacing\")\n channels_data = [[resample_to_spacing(img, avg_spacing, interpolator) for img in channel] for channel in channels_data]\nelseif resample_images_spacing == \"median\"\n println(\"Calculating median spacing across all $channel_type files and resampling.\")\n all_spacings = [img.spacing for channel in channels_data for img in channel]\n median_spacing = Tuple(Float32(median(s)) for s in all_spacings)\n println(\"Median spacing calculated: $median_spacing\")\n channels_data = [[resample_to_spacing(img, median_spacing, interpolator) for img in channel] for channel in channels_data]\nelseif resample_images_spacing == false\n println(\"Skipping resampling of $channel_type files.\")\n # No resampling will be applied, channels_data remains unchanged.\nend\n```\n
\n\nComment:
\n`Resample_to_spacing` uses interpolation that is not yet GPU-accelerated in this implementation, this step slows down the data preparation phase significantly.\n\n#### Resizing all channel files to average or target size βœ…\nTo create a cohesive 5D tensor, all images in each channel are resized to a uniform shape, either the average size of all images or a specific target size. This resizing process uses `crop_or_pad`, ensuring that all images match the specified dimensions, making them suitable for model input.\n\n
\ncrop_or_pad:\n\n```julia\nif resample_size == \"avg\"\n sizes = [size(img.voxel_data) for img in channels_data for img in img] # Get sizes from all images\n avg_dim = map(mean, zip(sizes...))\n avg_dim = Tuple(Int(round(d)) for d in avg_dim)\n println(\"Resizing all $channel_type files to average dimension: $avg_dim\")\n channels_data = [[crop_or_pad(img, avg_dim) for img in channel] for channel in channels_data]\nelseif resample_size != \"avg\"\n target_dim = Tuple(resample_size)\n println(\"Resizing all $channel_type files to target dimension: $target_dim\")\n channels_data = [[crop_or_pad(img, target_dim) for img in channel] for channel in channels_data]\nend\n```\n
\n\n## Basic Post-processing operations\nPost-processing operations involve the algorithm `largest_connected_components`. It is achieved by label initialization and propagation in the segmented mask.\nThe `initialize_labels_kernel` function assigns unique labels to different regions.\n\n
\ninitialize_labels_kernel:\n\n```julia\n@kernel function initialize_labels_kernel(mask, labels, width, height, depth)\n idx = @index(Global, Cartesian)\n i = idx[1]\n j = idx[2]\n k = idx[3]\n \n if i >= 1 && i <= width && j >= 1 && j <= height && k >= 1 && k <= depth\n if mask[i, j, k] == 1\n labels[i, j, k] = i + (j - 1) * width + (k - 1) * width * height\n else\n labels[i, j, k] = 0\n end\n end\nend\n```\n
\nPropagate_labels_kernel iteratively updates the labels to maintain connected regions.\npropagate_labels_kernel:\n
\n\n```julia\n@kernel function propagate_labels_kernel(mask, labels, width, height, depth)\n idx= @index(Global, Cartesian)\n i = idx[1]\n j = idx[2]\n k = idx[3]\n\n if i >= 1 && i <= width && j >= 1 && j <= height && k >= 1 && k <= depth\n if mask[i, j, k] == 1\n current_label = labels[i, j, k]\n for di in -1:1\n for dj in -1:1\n for dk in -1:1\n if di == 0 && dj == 0 && dk == 0\n continue\n end\n ni = i + di\n nj = j + dj\n nk = k + dk\n if ni >= 1 && ni <= width && nj >= 1 && nj <= height && nk >= 1 && nk <= depth\n if mask[ni, nj, nk] == 1 && labels[ni, nj, nk] < current_label\n labels[i, j, k] = labels[ni, nj, nk]\n end\n end\n end\n end\n end\n end\n end\nend\n```\n
\nThis process facilitates the identification of the largest connected components in 3D space, helping to isolate relevant medical structures, such as tumors, in the segmented mask. Allowing determining how many such areas are to be returned.\n\n
\nlargest_connected_components:\n\n```julia\nfunction largest_connected_components(mask::Array{Int32, 3}, n_lcc::Int)\n width, height, depth = size(mask)\n mask_gpu = CuArray(mask)\n labels_gpu = CUDA.fill(0, size(mask))\n dev = get_backend(labels_gpu)\n ndrange = (width, height, depth)\n workgroupsize = (3, 3, 3)\n\n # Initialize labels\n initialize_labels_kernel(dev)(mask_gpu, labels_gpu, width, height, depth, ndrange = ndrange)\n CUDA.synchronize()\n\n # Propagate labels iteratively\n for _ in 1:10 \n propagate_labels_kernel(dev, workgroupsize)(mask_gpu, labels_gpu, width, height, depth, ndrange = ndrange)\n CUDA.synchronize()\n end\n\n # Download labels back to CPU\n labels_cpu = Array(labels_gpu)\n \n # Find all unique labels and their sizes\n unique_labels = unique(labels_cpu)\n label_sizes = [(label, count(labels_cpu .== label)) for label in unique_labels if label != 0]\n\n # Sort labels by size and get the top n_lcc\n sort!(label_sizes, by = x -> x[2], rev = true)\n top_labels = label_sizes[1:min(n_lcc, length(label_sizes))]\n\n # Create a mask for each of the top n_lcc components\n components = [labels_cpu .== label[1] for label in top_labels]\n return components\nend\n```\n
\n\n## Structured configuration of all hyperparameters πŸ†™\n\nHyperparameters for the entire pipeline are stored in a JSON configuration file, enabling straightforward adjustments for experimentation (just swap values, save and resume the study). This structured setup allows easy modification of key parameters, such as data set preparation, training settings, data augmentation, and resampling options.\n\n\n
\nExample configuration:\n\n```JSON\n{\n \"model\": {\n \"patience\": 10,\n \"early_stopping_metric\": \"val_loss\",\n \"optimizer_name\": \"Adam\",\n \"loss_function_name\": \"l1\",\n \"early_stopping\": true,\n \"early_stopping_min_delta\": 0.01,\n \"optimizer_args\": \"lr=0.001\",\n \"num_epochs\": 10\n },\n \"data\": {\n \"batch_complete\": false,\n \"resample_size\": [200,101,49],\n \"resample_to_target\": false,\n \"resample_to_spacing\": false,\n \"batch_size\": 3,\n \"standardization\": false,\n \"target_spacing\": null,\n \"channel_size\": 1,\n \"normalization\": false,\n \"has_mask\": true\n },\n \"augmentation\": {\n \"augmentations\": {\n \"Brightness transform\": {\n \"mode\": \"additive\",\n \"value\": 0.2\n }\n },\n \"p_rand\": 0.5,\n \"processing_unit\": \"GPU\",\n \"order\": [\n \"Brightness transform\"\n ]\n },\n \"learning\": {\n \"Train_Val_Test_JSON\": false,\n \"largest_connected_component\": false,\n \"n_lcc\": 1,\n \"n_folds\": 3,\n \"invertible_augmentations\": false,\n \"n_invertible\": true,\n \n \"class_JSON_path\": false,\n \"additional_JSON_path\": false,\n \"patch_size\": [50,50,50],\n \"metric\": \"dice\",\n \"n_cross_val\": false,\n \"patch_probabilistic_oversampling\": false,\n \"oversampling_probability\": 1.0,\n \"test_train_validation\": [\n 0.6,\n 0.2,\n 0.2\n ],\n \"shuffle\": false\n }\n}\n\n```\n
\n\nComments:
\nThe current configuration is loaded as a dictionary, which simplifies access and modification. This setup presents a strong foundation for integrating automated search algorithms for hyperparameter tuning, enabling more efficient model optimization.
\nThe configuration structure could be reorganized and re-named to improve readability, making it easier for users to locate and adjust specific parameters.\n\n## Visualization of algorithm outputs ⚠️\nThis module provides basic visualization functionality by saving output masks and images first to MedImage format and then to Nifti format. The `create_nii_from_medimage` function from MedImage.jl generates Nifti files, which can be loaded into MedEye3D for 3D visualization.\n\nComments:
\nIntegrating this visualization module more fully with the pipeline could eliminate unnecessary steps. By automatically loading output masks and images as raw data into MedEye3D for 3D visualization and supporting a more efficient end-to-end workflow. \n\n## K-fold cross-validation functionality βœ…\nK-fold cross-validation is implemented to evaluate model performance more robustly. The data is split into multiple folds, with each fold serving as a validation set once, while the others form the training set. This functionality provides a better assessment of model performance across different subsets of the data.\n\n
\nK-fold cross-validation functionality:\n\n```julia\n...\n tstate = initialize_train_state(rng, model, optimizer)\n if config[\"learning\"][\"n_cross_val\"]\n n_folds = config[\"learning\"][\"n_folds\"]\n all_tstate = []\n combined_indices = [indices_dict[\"train\"]; indices_dict[\"validation\"]]\n shuffled_indices = shuffle(rng, combined_indices)\n for fold in 1:n_folds\n println(\"Starting fold $fold/$n_folds\")\n train_groups, validation_groups = k_fold_split(combined_indices, n_folds, fold, rng)\n \n tstate = initialize_train_state(rng, model, optimizer)\n final_tstate = epoch_loop(num_epochs, train_groups, validation_groups, h5, model, tstate, config, loss_function, num_classes)\n \n push!(all_tstate, final_tstate)\n end\n else\n final_tstate = epoch_loop(num_epochs, train_groups, validation_groups, h5, model, tstate, config, loss_function, num_classes)\n end\n return final_tstate\n... \n```\n
\n\nThe `k_fold_split` function organizes the indices for each fold, ensuring comprehensive coverage of the dataset during training.\n\n
\nk_fold_split\n\n```julia\nfunction k_fold_split(data, n_folds, current_fold)\n fold_size = length(data) Γ· n_folds\n validation_start = (current_fold - 1) * fold_size + 1\n validation_end = validation_start + fold_size - 1\n validation_indices = data[validation_start:validation_end]\n train_indices = [data[1:validation_start-1]; data[validation_end+1:end]]\n return train_indices, validation_indices\nend\n```\n
\n\n# Conclusions and Future Development\nI have successfully established a foundation for a medical imaging pipeline, addressing significant challenges in data handling, model training, and augmentation integration. The integration of dataset-wide functions has significantly enhanced the reproducibility and handling of batched data with GPU support enabling scalability of experiments, making it easier for researchers and practitioners to produce better results.\n\n# Future Development\nAs we look to the future, there are several areas where MedPipe3D can be expanded and improved to better serve the medical AI community. These include:\n\n## Necessary Enhancements\n\nComprehensive Logging: Develop detailed logging mechanisms that capture a wide range of events, including system statuses, model performance metrics, and user activities, to facilitate debugging and system optimization. This is currently executed as a simple `println` function.\n\nTensorBoard Integration: Implement an interface for TensorBoard to allow users to visualize training dynamics in real time, providing insights into model behavior and performance trends.\n\nError and Warning Logs: Introduce advanced error and warning logging capabilities to alert users of potential issues before they affect the pipeline's performance, ensuring smoother operations and maintenance.\n\nAutomated Visualization: Integrate MedEye3D directly into MedPipe3D to enable automated visualization of outputs, such as segmentation masks or other relevant medical imaging features. This feature would provide users with real-time visual feedback on model performance and data quality.\nCode-Level Documentation: Due to needed changes in the fundamental structure of the pipeline in the final phase of the project, it is necessary to reevaluate all documentation.\n\nOfficial JuliaHealth Documentation: Extend the documentation efforts to include official entries on juliahealth.org, providing a centralized and authoritative resource for users seeking to learn more about MedPipe3D and its capabilities with examples shown\n\n## Potential Enhancements\nGPU support for interpolation will allow for significant acceleration of such functions as Scale transform, Simulate, Low-resolution transform, Elastic deformation transform, and Resampling spacing.\n\nAdd more reversible augmentations to test time.\n\nCalculating the average of the edges of the picture: checking the type of photo and calculating more correctly on this basis\n\nElastic deformation transforms with the simulation of different tissue elasticities.\n\n# Acknowledgments πŸ™‡β€β™‚οΈ\n\nI would like to express my deepest gratitude to my mentor Dr. [Jakub Mitura](https://www.linkedin.com/in/jakub-mitura-7b2013151/) for his invaluable guidance and support throughout this project. His expertise and encouragement were instrumental in overcoming challenges and achieving project milestones.\n\n", - "supporting": [ - "GSoC_Jan_Zubik_MedPipe3D_files/figure-html" - ], - "filters": [], - "includes": {} - } -} \ No newline at end of file diff --git a/_freeze/posts/divyansh-gsoc/gsoc-2024-fellows/execute-results/html.json b/_freeze/posts/divyansh-gsoc/gsoc-2024-fellows/execute-results/html.json deleted file mode 100644 index 7e29bbd..0000000 --- a/_freeze/posts/divyansh-gsoc/gsoc-2024-fellows/execute-results/html.json +++ /dev/null @@ -1,12 +0,0 @@ -{ - "hash": "f865f6b24acb146c30a159e6e0d16fdc", - "result": { - "engine": "julia", - "markdown": "---\ntitle: \"GSoC '24: Adding functionalities to medical imaging visualizations\"\ndescription: \"A summary of my project for Google Summer of Code - 2024\"\nauthor: \"Divyansh Goyal\"\ndate: \"11/1/2024\"\nbibliography: ./references.bib\ncsl: ./../../ieee-with-url.csl\ntoc: true\nengine: julia\nimage: false\ncategories:\n - gsoc\n - openGl\n - imaging\n - neuro\n---\n\n\n# Hello Everyone! πŸ‘‹\n\nI am Divyansh, an undergraduate student from Guru Gobind Singh Indraprastha university, majoring in Artificial Intelligence and Machine Learning. Stumbling upon projects under the Juliahealth sub-ecosystem of medical imaging packages, the intricacies of imaging modalities and file formats, reflected in their relevant project counterparts, captured my interest. Working with standards such as NIfTI (Neuroimaging Informatics Technology Initiative) and DICOM (Digital Imaging and Communications in Medicine) with MedImages.jl, I became interested in the visualization routines of such imaging datasets and their integration within the segmentation pipelines for modern medical-imaging analysis.\n\nIn this post, I’d like to summarize what I did this summer and everything I learned along the way, contributing to MedEye3d.jl medical imaging visualizer under GSOC-2024!\n\n> If you want to learn more about me, you can connect with me on [**LinkedIn**](https://www.linkedin.com/in/divyansh-goyal-34654b200/) and follow me on [**GitHub**](https://github.com/divital-coder)\n\n# Background\n\n## What is MedEye3d.jl?\n\n[MedEye3D.jl](https::/github.com/Juliahealth/MedEye3d.jl) is a package under the Julia language ecosystem designed to facilitate the visualization and annotation of medical images. Tailored specifically for medical applications, it offers a range of functionalities to enhance the interpretation and analysis of medical images. MedEye3D aims to provide an essential tool for 3D medical imaging workflow within Julia. The underlying combination of [Rocket.jl](https://github.com/ReactiveBayes/Rocket.jl) and [ModernGL.jl](https://github.com/JuliaGL/ModernGL.jl) ensures the high-performance robust visualizations that the package has to offer.\n\nMedEye3d.jl is open-source and comes with an intuitive user interface (To learn more about MedEye3d, you can read the paper introducing it [here](https://doi.org/10.26348/znwwsi.25.57) [@Mitura2021]).\n\n## What features does this project encompass?\n\nThis project covers implementation of several tasks that will enable the establishment of additional important functionalities within the MedEye3D package, facilitating enhancements within the visualization’s windowing for MRI and PET data, support for super voxels (sv), improved load times, high-level functionality implementation and robust viewing for multiple images.\n\n# Project Goals\n\nThe goals outlined by Dr. Jakub Mitura (my project mentor) and I, beginning of this summer were:\n\n1. Migration of package reliance from [Rocket.jl](https://github.com/reactivebayes/Rocket.jl) to base Julia channel and macros: The first decision that was made was to fix the issue of screen tearing and flicker, resulting from the Rocket.jl's actor-subscription mechanism present at the core of MedEye3d.jl's event-driven programming. Here, Julia's threadsafe and asynchronous [channels](https://docs.julialang.org/en/v1/manual/asynchronous-programming/) provided a way to introduce reactive programming and state management within MedEye3d without the tradeoffs resulting from external packages such as Rocket\n\n2. Implementation of high level functions with simplified basic usage: Prior to this, MedEye3d involved initialization of data, texture specifications and text display for a final visualization. To reduce complexity, methods to abstract such chores were devised and implemented which resulted in the exposure of functions for loading images, accessing display data and modification of display data. This also encompassed the loading of images via [MedImages.jl](https://github.com/juliahealth/MedImages.jl) which required prior work for the integration of C++ [ITK](https://github.com/InsightSoftwareConsortium/ITK) backend for image I/O.\n\n3. Improved precompilation with decreased outputs to reduce start time\n\n4. Automatic windowing for most common MRI and PET modalities: This task is a step in the direction of maintaining consistent visualizations across MRI and PET’s most common modalities, to mimic images similar to what is displayed within [3dSlicer](https://www.slicer.org/) for the same.\n\n5. Adding support for multi-image viewing with crosshair marker for image registration\n\n6. Adding support for the display of [SuperVoxels](https://doi.org/10.1016/j.cagd.2022.102080) sv with borders within the image slices to better understand anatomical regions within slices: Supervoxels, described either through indicator masks or meshes, encapsulate regions of interest with distinct image characteristics.\n\nAdditionally, we had a few stretch goals which are going to be a work in progress:\n\n1. Visualization of structures by 3D rendering using OpenGL,\n\n2. Support for MedVoxelHD visualization by voxel-based Hausdorff distance computation.\n\n3. Support for OSX users\n\n# Tasks\n\n## 1. Migration of package from Rocket to Julia's Base.Channel\n\nInitially, there was significant screen-tearing evident from the pixelated display of the rendered text and main image which, furthermore exhibited flickering upon scrolling through the slices in the relevant displayed image's planar views i.e (Transversal, Coronal and Saggital). Troubleshooting along the way, we narrowed down the issue within the Rocket's actor-subscription mechanism and decided to integrate Julia's Base.Channel within [MedEye3d.jl](https://github.com/Juliahealth/MedEye3d.jl) for handling the event and state management routine. Julia has asynchronous, threadsafe [channels](https://docs.julialang.org/en/v1/manual/asynchronous-programming/#Communicating-with-Channels) which facilitate in asynchronous programming with the help of a producer-consumer mechanism. An example usage of Base.Channel is as follows:\n\n```julia\nfunction consumer(channel::Base.Channel)\n while(true)\n channelData::String = take!(channel)\n println(\"Channel got \" * channelData)\n end\nend\n\nnewChannel = Base.Channel(100)\n\n@async consumer(newChannel)\nput!(newChannel, \"apples\")\n```\n\nJulia’s multiple dispatch made for the architectural setup of MedEye3d, facilitated fixing the issue of screen tearing. Below is how the `on_next!` function, invokes different reactive components based on the types of arguments it is dealing with.\n\n> Dump data in channel -> fetch data from the channel in an event loop -> invoke `on_next!(state, channelData)` -> invoke relevant functionality based on the type of arguments passed\n\n![](./multiple_dispatch_code.png)\n\nThe end result was a visualizer with a seamless display of a CT image without any pixelating artifacts.\n\n![](./fixed_screen_tear.png)\n\n## 2. Implementation of high level functions with simplified basic usage\n\nImplementing a bare-bones image visualization required a lot of function calls and definitions, in order to execute the following phases:\n\n1. Rendering an image-plane with OpenGL\n\n2. Loading data slices from the image\n\n3. Creating texture specifications for modalities\n\n4. Producing the final segmentation display\n\nIn order to simplify basic usage, high-level abstractions were put in place with the help of [MedImages.jl](https://github.com/MedImages.jl) (under ongoing development) library to load images in the form of MedImage objects to formulate a single display function for the user. Further simplifications were made to accommodate options for the user to manipulate the imaging data that is displayed currently in the visualizer i.e retrieval of voxel arrays and their modification. Taking this in mind, the following relevant functions were exposed:\n\n```julia\nMedEye3d.SegmentationDisplay.displayImage()\n```\n\n```julia\nMedEye3d.DisplayDataManag.getDisplayedData()\n```\n\n```julia\nMedEye3d.DisplayDataManag.setDisplayedData()\n```\n\nPutting all of the above functions to use together, we can launch the visualizer, retrieve the displayed voxel data and modify it to our liking. A sample script to achieve the former, is highlighted below:\n\n```julia\nusing MedEye3d\nctNiftiImage = \"/home/hurtbadly/Downloads/ct_soft_study.nii.gz\"\nmedEyeStruct = MedEye3d.SegmentationDisplay.displayImage(ctNiftiImage)\ndisplayData = MedEye3d.DisplayDataManag.getDisplayedData(medEyeStruct, [Int32(1), Int32(2)]) #passing the active texture number\n\n# We need to check if the return type of the displayData is a single Array{Float32,3} or a vector{Array{Float32,3}}\n# Now in this case we are setting Gaussian noise over the manualModif Texture voxel layer, and the manualModif texture defaults to 2 for active number\n\ndisplayData[2][:, :, :] = randn(Float32, size(displayData[2]))\nMedEye3d.DisplayDataManag.setDisplayedData(medEyeStruct, displayData)\n```\n\nThe result of this [Gaussian noise](https://www.sfu.ca/sonic-studio-webdav/handbook/Gaussian_Noise.html) within the annotation layer, made for an outcome like the following:\n\n![](./gaussian_noise_annotation.png)\n\n## 3. Improved precompilation with decreased outputs to reduce start time\n\nPreviously, the package's precompilation was failing in Julia v1.9 and v1.10 due to pattern matching errors arising after the usage of match macros from the [Match.jl](https://github.com/JuliaServices/Match.jl) pkg in MedEye3d's keymapping workflow between GLFW callbacks from mouse and keyboard. The relevant equivalent native conditional (if-else) statements, resolved the issue and facilitated in successful precompilation of the package. Further, only following minimal outputs were produced during precompilation:\n\n![](./precompilation_outputs.png)\n\nChanges highlighted within the following pull-request:\n\n[https://github.com/JuliaHealth/MedEye3d.jl/pull/12](https://github.com/JuliaHealth/MedEye3d.jl/pull/12)\n\n## 4. Automatic [windowing](https://youtu.be/HaL-G43kwKA) for most common MRI and PET modalities\n\nWindowing is a crucial aspect of medical imaging, particularly in MRI (Magnetic Resonance Imaging) and PET (Positron Emission Tomography) modalities. It enables radiologists to enhance the contrast of images, highlighting specific features and improving the overall diagnostic accuracy. Windowing involves controlling the display range of pixel values to optimize the contrast between different tissues or structures. The display range is defined by two values: the minimum (min) and maximum (max) values that contribute to the final range of pixels that are displayed. By adjusting these values, radiologists can enhance or suppress specific features in the image, facilitating a more accurate diagnosis.\n\nThe `setTextureWindow` function utilizes a set of predefined keymap controls to simplify the windowing process. The F1-F7 keys are designated for controlling windowing in MRI and PET modalities. The keymap controls are as follows:\n\n* F1: Display wide window for bone (CT) or increase minimum value for PET\n\n* F2: Display window for soft tissues (CT) or increase minimum value for PET\n\n* F3: Display wide window for lung viewing (CT) or increase minimum value for PET\n\n* F4: Decrease minimum value for display\n\n* F5: Increase minimum value for display\n\n* F6: Decrease maximum value for display\n\n* F7: Increase maximum value for display\n\nImplementation of `setTextureWindow` Function\n\nThe `setTextureWindow` function is designed to update the texture window settings based on the input keymap control. The function takes three arguments:\n\n* `activeTextur`: The current texture specification\n* `stateObject`: The state data fields\n* `windowControlStruct`: The window control structure containing the letter code for the keymap control\n\nThe function performs the following steps:\n\n1. Checks the letter code of the keymap control and updates the minimum and maximum values of the texture specification accordingly.\n2. Updates the uniforms for the texture specification using the `controlMinMaxUniformVals` function.\n\n```julia\nfunction setTextureWindow(activeTextur::TextureSpec, stateObject::StateDataFields, windowControlStruct::WindowControlStruct)\n activeTexturName = activeTextur.name\n displayRange = activeTextur.minAndMaxValue[2] - activeTextur.minAndMaxValue[1]\n activeTexturStudyType = activeTextur.studyType\n if windowControlStruct.letterCode == \"F1\"\n if activeTexturStudyType == \"CT\"\n #Bone windowing in CT\n activeTextur.minAndMaxValue = Float32.([400, 1000])\n elseif activeTexturStudyType == \"PET\"\n activeTextur.minAndMaxValue[1] += 0.10 * displayRange #windowing for pet, in the case of PET simply increase the minimum by 20% , doing the same in f1,f2 and f3\n end\n elseif windowControlStruct.letterCode == \"F2\"\n if activeTexturStudyType == \"CT\"\n activeTextur.minAndMaxValue = Float32.([-40, 350])\n elseif activeTexturStudyType == \"PET\"\n activeTextur.minAndMaxValue[1] += 0.10 * displayRange\n end\n elseif windowControlStruct.letterCode == \"F3\"\n if activeTexturStudyType == \"CT\"\n activeTextur.minAndMaxValue = Float32.([-426, 1000])\n elseif activeTexturStudyType == \"PET\"\n activeTextur.minAndMaxValue[1] += 0.10 * displayRange\n end\n elseif windowControlStruct.letterCode == \"F4\"\n activeTextur.minAndMaxValue[1] -= 0.20 * displayRange\n elseif windowControlStruct.letterCode == \"F5\"\n activeTextur.minAndMaxValue[1] += 0.20 * displayRange\n elseif windowControlStruct.letterCode == \"F6\"\n activeTextur.minAndMaxValue[2] -= 0.20 * displayRange\n elseif windowControlStruct.letterCode == \"F7\"\n activeTextur.minAndMaxValue[2] += 0.20 * displayRange\n elseif windowControlStruct.letterCode == \"F8\"\n activeTextur.uniforms.maskContribution -= 0.10\n elseif windowControlStruct.letterCode == \"F9\"\n activeTextur.uniforms.maskContribution += 0.10\n end\n\n stateObject.mainForDisplayObjects.listOfTextSpecifications = map(texture -> texture.name == activeTexturName ? activeTextur : texture, stateObject.mainForDisplayObjects.listOfTextSpecifications)\n coontrolMinMaxUniformVals(activeTextur)\nend\n```\n> Bone windowing in CT\n\n![](./ct_windowing.png)\n\n> Bone windowing in PET\n\n![](./pet_windowing.png)\n\n## 5. Adding support for multi-image viewing with crosshair marker for image registration\n\nFollowing the mid-term evaluation, MedEye3d.jl underwent a significant enhancement, whereby a multi-image display capability was implemented through a series of refinements. Specifically, a novel approach was adopted, whereby separate OpenGL [fragment shaders](https://www.khronos.org/opengl/wiki/Fragment_Shader) were introduced to concurrently render images on either side of the visualizer, namely the left and right views. Prior to integrating voxel data into the fragment shaders, an initial series of tests involved evaluating individual colors to validate the integrity of the double image display. A screenshot from one of these critical testing phases is presented below:\n![](./multi_fragment_shader.png)\n\nThe shaders were further manipulated to automatically initialize for each of the images separately. Further, the reactive aspect of the visualizer in multi-image display mode was iterated upon and now, instead of a single state management struct, a vector of states was being passed around, facilitating the user to scroll each of the images separately just by simply hovering their mouse over either of the image, activating its relevant associated state struct.\n\nDown below, is the struct for state that handles all of the things currently related with an image:\n\n```julia\n@with_kw mutable struct StateDataFields\n currentDisplayedSlice::Int = 1 # stores information what slice number we are currently displaying\n mainForDisplayObjects::forDisplayObjects = forDisplayObjects() # stores objects needed to display using OpenGL and GLFW\n onScrollData::FullScrollableDat = FullScrollableDat()\n textureToModifyVec::Vector{TextureSpec} = [] # texture that we want currently to modify - if list is empty it means that we do not intend to modify any texture\n isSliceChanged::Bool = false # set to true when slice is changed set to false when we start interacting with this slice - thanks to this we know that when we start drawing on one slice and change the slice the line would star a new on new slice\n textDispObj::ForWordsDispStruct = ForWordsDispStruct()# set of objects and constants needed for text diplay\n currentlyDispDat::SingleSliceDat = SingleSliceDat() # holds the data displayed or in case of scrollable data view for accessing it\n calcDimsStruct::CalcDimsStruct = CalcDimsStruct() #data for calculations of necessary constants needed to calculate window size , mouse position ...\n valueForMasToSet::valueForMasToSetStruct = valueForMasToSetStruct() # value that will be used to set pixels where we would interact with mouse\n lastRecordedMousePosition::CartesianIndex{3} = CartesianIndex(1, 1, 1) # last position of the mouse related to right click - usefull to know onto which slice to change when dimensions of scroll change\n forUndoVector::AbstractArray = [] # holds lambda functions that when invoked will undo last operations\n maxLengthOfForUndoVector::Int64 = 15 # number controls how many step at maximum we can get back\n fieldKeyboardStruct::KeyboardStruct = KeyboardStruct()\n displayMode::DisplayMode = SingleImage\n imagePosition::Int64 = 1\n switchIndex::Int = 1\n mainRectFields::GlShaderAndBufferFields = GlShaderAndBufferFields()\n crosshairFields::GlShaderAndBufferFields = GlShaderAndBufferFields()\n textFields::GlShaderAndBufferFields = GlShaderAndBufferFields()\n spacingsValue::Union{Vector{Tuple{Float64,Float64,Float64}},Tuple{Float64,Float64,Float64}} = [(1.0, 1.0, 1.0)]\n originValue::Union{Vector{Tuple{Float64,Float64,Float64}},Tuple{Float64,Float64,Float64}} = [(1.0, 1.0, 1.0)]\n supervoxelFields::GlShaderAndBufferFields = GlShaderAndBufferFields()\nend\n```\n\nAfter the integrity of the fragment shaders was verified in multi-image, voxel data for the images was integrated and further modifications to the high-level functions were made and eventually the following script produced a rather appealing result.\n\nScript for loading the same NIFTI image twice in the visualizer for side-by-side display:\n\n```julia\nusing MedEye3d\nctNiftiImage = \"/home/hurtbadly/Downloads/ct_soft_study.nii.gz\"\nMedEye3d.SegmentationDisplay.displayImage([[ctNiftiImage],[ctNifitImage]])\n```\n>Results in :\n\n![](./multi_image_ct.png)\n\nCrosshair marker for image registration are displayed in the relevant passive image to hightlight the same anatomical regions based on the spatial meta-data of the images i.e spacing, origin and direction. In order to achive the crosshair rendering in the passive image, the following action items were devised:\n\n(a) Retrieval of GLFW Mouse Callbacks for x and y position of the cursor in window coordinates (0 to window-width) from the active image\n\n(b) Conversion of these x and y window coordinates into their relevant active image x and y texture coordinates\n\n(c) Conversion of these texture coordinates into real space point with the help of spatial metadata\n\n(d) Conversion of the real space point into the texture coordinates of the passive image\n\n(e) Conversion of the passive image texture coordinates into their relevant OpenGL coordinate system values (-1 to 1)\n\n(f) Rendering of crosshair on OpenGL coordinate in passive image\n\nConversion between different coordinate systems and accounting for the image's spatial metadata during calculating proved to be challenging at first, but with multiple revisions, a final solution was achieved with seemingly no noticeable amount of lag or delay. One such frame of [CT] images with crosshair display in multi-image is depicted below:\n\n![](./multi_image_ct_crosshair.png)\n\n>Another frame from the openGL rendering cycle, highlighting PET images with crosshair display in multi-image mode:\n\n![](./pet_multi_image.png)\n\n## 6. Adding support for the display of [SuperVoxels](https://doi.org/10.1016/j.cagd.2022.102080) sv with borders within the image slices to better understand anatomical regions within slices\n\nIn enhancing MedEye3d's functionality, supporting super voxels (sv) with boundaries becomes paramount. The sv rendering, effectively capturing gradients, serves as the cornerstone for detecting these boundaries within both MRI and PET volumes. Supervoxels, described either through indicator masks or meshes, encapsulate regions of interest with distinct image characteristics.\nBy integrating boundary detection for super-voxels, MedEye3d can offer enhanced segmentation capabilities, enabling more precise delineation and analysis of anatomical structures and pathological regions within medical imaging data.\n\n[Supervoxels](https://www.sciencedirect.com/topics/computer-science/superpixel) are basically a collection of voxels that share similar image properties. For example: in MRI scans of the brain cortex, super voxels could represent clusters of voxels corresponding to specific anatomical regions or functional areas. The main objective of this task was to add support for the display of super voxel-based segmentation of images, followed by some janitorial tasks:\n\n1. Display of the borders of super-voxels (sv), extracted using the machine learning algorithms.\n\n2. Checking image gradient agreement with super-voxel borders.\n\nThis initial workflow involved, the initialization of relevant buffers in OpenGL for dynamic rendering of lines over the image display, namely vertex array buffers (vao), vertex buffers (vbo) and edge buffers (ebo). Further, these buffers are updated on a scroll event, where the information from the currently displayed slice is passed to the event handler, which invokes a function that updates the vertex buffer (vbo) with new vertices pertaining to the relevant slice number and planar view, precalculated from an [HDF5](https://www.neonscience.org/resources/learning-hub/tutorials/about-hdf5) file during initialization of the visualizer. For instance, if the user is scrolling in the 3rd axis (transversal plane) and is currently on slice 40, the supervoxel display will pertain to edges specifically calculated for that specific slice in that plane.\n\nEventually, with ever so increasing number of attempts and a few hurdles along the way, one of which particularly stood out since it marked our first step towards a good direction:\n\n> Challenges in rendering\n\n![](./supervoxel_rendering_issue.png)\n\nAt last, an appealing result hit our sight.\n\n> Final result\n\n> *Note:* The image borders are intentional to emphasize the size of the visualizer which is currently defaulted to a certain width and height.\n\n![](./supervoxel_rendering_fixed.png)\n\n> *Note:* However, There are a few things left to cover here, most of which revolve around MedImages.jl and documentation for the same. List of PRs that facilitated the completion of the tasks highlighted above:\n\n(a) [https://github.com/JuliaHealth/MedEye3d.jl/pull/21](https://github.com/JuliaHealth/MedEye3d.jl/pull/21)\n\n(b) [https://github.com/JuliaHealth/MedEye3d.jl/pull/20](https://github.com/JuliaHealth/MedEye3d.jl/pull/20)\n\n(c) [https://github.com/JuliaHealth/MedEye3d.jl/pull/16](https://github.com/JuliaHealth/MedEye3d.jl/pull/16)\n\n(d) [https://github.com/JuliaHealth/MedEye3d.jl/pull/14](https://github.com/JuliaHealth/MedEye3d.jl/pull/14)\n\n(e) [https://github.com/JuliaHealth/MedEye3d.jl/pull/13](https://github.com/JuliaHealth/MedEye3d.jl/pull/13)\n\n(f) [https://github.com/JuliaHealth/MedEye3d.jl/pull/12](https://github.com/JuliaHealth/MedEye3d.jl/pull/12)\n\n# Contributions Beyond Coding\n\n## 1. Mentoring and Guidance\n\nI regularly organized meetings with my mentor to seek guidance on project direction and troubleshooting issues in the visualizer. This ensured that I stayed on track, received timely feedback, and addressed any challenges that arose.\n\n## 2. Package Documentation and Community Contribution\n\nI contributed to other medical imaging sub-ecosystem packages in JuliaHealth, including [MedImages.jl](https://github.com/Juliahealth/MedImages.jl) and [MedEval3D.jl](https://github.com/Juliahealth/MedEval3D.jl). Specifically, I set up documentation for these packages using DocuementerVitepress.jl. This not only enhanced the functionality of these packages but also helped maintain a coherent and organized package ecosystem.\n\n## 3. Multirepo Management and Collaboration\n\nIn addition to my work on the MedEye3d visualizer, I made significant contributions to other JuliaHealth repositories, including [MedImages.jl](https://github.com/JuliaHealth/MedImages.jl) and worked over an [Insight Toolkit](https://github.com/InsightSoftwareConsortium/ITK) wrapper library [ITKIOWrapper.jl](https://github.com/JuliaHealth/ITKIOWrapper.jl) for support in image I/O down the road in MedImages.jl. I also maintained relevant documentation and ensured continuous collaboration and synchronization across these packages.\n\n# Conclusions and Future Development\n\nWithin the scope of this 350-hour project, a comprehensive range of objectives were successfully addressed. Noteworthy achievements include:\n\n1. Fixed screen tear and flicker within the visualizer. Integration of threadsafe Julia channels.\n\n2. Achieved multi-image display over CT and PET modalities with crosshair rendering (Although, only one modality can be visualize at a time, i.e either CT | CT or PET | PET).\n\n3. Achieved supervoxel display in single image display mode.\n\n4. Achieved automatic windowing of MRI and PET most common modalities.\n\nFuture work would include:\n\n- Support for the users on Darwin (Apple-based platforms).\n\n- Apart from that, we would need to add a function that dynamically allocates the texture number to the manual modification mask, regardless of the number of images passed for display, which is currently defaulted to 2.\n\n- Also, in the future, we would explore the stretch goals a bit more rigorously, particularly the implementation of [MedVoxelHD](https://doi.org/10.1016/j.softx.2024.101744) within MedEye3d.\n\n# Acknowledgements πŸ™‡β€β™‚οΈ\n\n1. [Jakub Mitura](https://orcid.org/0000-0003-1823-6823): aka, [Dr. Jakub Mitura](https://github.com/jakubMitura14)\n\n2. [Carlos Castillo Passi](https://scholar.google.com/citations?user=WzleS8YAAAAJ&hl=en): aka, [cncastillo](https://github.com/cncastillo)\n\nI would like to thank my mentor Dr. Jakub Mitura, for his help through out every phase of this project. The troubleshooting routines around problems would have rendered the project unsuccessful, if not for the support and guidance of my mentor throughout each part of this project. I would also like to thank Jacob Zelko, for leading the Juliahealth community with such vast expertise and leading efforts for engagement amongst the members through monthly meetings. My sincere gratitude towards your support, help and guidance through out the fellowship.\n\n", - "supporting": [ - "gsoc-2024-fellows_files/figure-html" - ], - "filters": [], - "includes": {} - } -} \ No newline at end of file diff --git a/_freeze/posts/dummy/index/execute-results/html.json b/_freeze/posts/dummy/index/execute-results/html.json deleted file mode 100644 index 3b95f27..0000000 --- a/_freeze/posts/dummy/index/execute-results/html.json +++ /dev/null @@ -1,12 +0,0 @@ -{ - "hash": "618860a13f9698fab47bfadeda85813f", - "result": { - "engine": "julia", - "markdown": "---\ntitle: \"Dummy Post\"\ndescription: \"Post description\"\nauthor: \"Foobar\"\ndate: \"6/22/2024\"\ntoc: true\nengine: julia\ncategories:\n - news\n - code\n - analysis\n---\n\n# Seciton 1\n\nSmall dummy blog post\n\n::: {#2 .cell execution_count=1}\n``` {.julia .cell-code}\n2 + 2\n```\n\n::: {.cell-output .cell-output-display execution_count=1}\n```\n4\n```\n:::\n:::\n\n\n\n::: {#4 .cell execution_count=1}\n``` {.julia .cell-code}\nprintln(2 + 2)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n4\n```\n:::\n:::\n\n\n\n# Section 2\n\n# Section 3\n\n", - "supporting": [ - "index_files" - ], - "filters": [], - "includes": {} - } -} \ No newline at end of file diff --git a/_freeze/posts/google-summer-of-code-fellows/gsoc-2024-fellows/execute-results/html.json b/_freeze/posts/google-summer-of-code-fellows/gsoc-2024-fellows/execute-results/html.json deleted file mode 100644 index 62b93ab..0000000 --- a/_freeze/posts/google-summer-of-code-fellows/gsoc-2024-fellows/execute-results/html.json +++ /dev/null @@ -1,12 +0,0 @@ -{ - "hash": "974862240d6a092c05b2a0d1a57f707f", - "result": { - "engine": "julia", - "markdown": "---\ntitle: \"GSoC '24: Developing Tooling for Observational Health Research in Julia\"\nproject mentor: \"Jacob Scott Zelko & Mounika Thakkallapally\"\ndescription: \"A brief summary of my project for Google Summer of Code - 2024\"\nauthor: \"Jay Sanjay Landge\"\ndate: \"9/5/2024\"\ntoc: true\nengine: julia\nimage: false\ncategories:\n - gsoc\n - mlj\n - sql\n - health-research\n - data analysis\n---\n\n\n# Hi Everyone! πŸ‘‹\n\nI am Jay Sanjay, and I am pursuing a bachelor's degree in computational sciences and engineering at the Indian Institute of Technology (IIT) in Hyderabad, India. Coming from a mathematics and data analysis background, I was initially introduced to Julia at my university lecture. Later, I dwelled more into it and the same across the JuliaHealth community - an intersection of Julia, Health Research, Data Sciences, and Informatics. Here, I met some of the great folks in Health Research and Data Informatics, and I decided to take on it as a full-fledged summer project. \nIn this blog, I will briefly describe what my project is and what I did as a part of it.\n\n\n> If you want to know more about me, you can connect with on [**LinkedIn**](https://www.linkedin.com/in/jay-landge-589439260/) and follow me on [**GitHub**](https://github.com/Jay-sanjay)\n\n\n\n# What is observational health research?\n\nObservational Health Research refers to studies that analyze real-world data (such as patient medical claims, electronic health records, etc.) to understand patient health. These studies often encompass a vast amount of data concerning patient care. An outstanding challenge here is that these datasets can become very complex and grow large enough to require advanced computing methods to process this information.\n\n\n\n# What is Patient Pathways ?\n\nPatient pathways refer to the journey that patients with specific medical conditions undergo in terms of their treatment. This concept goes beyond simple drug uptake statistics and looks at the sequence of treatments patients receive over time, including first-line treatments and subsequent therapies. Understanding patient pathways is essential for analyzing treatment patterns, adherence to clinical guidelines, and the rational use of drugs.\nTo analyze patient pathways, one would typically use real-world data from sources such as electronic health records, claims data, and registries. However, barriers such as data interoperability and resource requirements have hindered the full utilization of real-world data for this purpose.\n\nSo to address these challenges we wanna introduce to a set of tool to extract and analyze these patient pathways. These set of tool are based on the Observational Medical Outcomes Partnership (OMOP) common data model, which standardizes health care data to promote interoperability.\n\n\n# Project Description\nAs part of this project with JuliaHealth, I developed a new package called [**OMOPCDMPathways.jl**](https://github.com/JuliaHealth/OMOPCDMPathways.jl). This package is designed for deployment in research projects, particularly those related to health and medical data analysis. This project takes much inspiration from [this](https://www.sciencedirect.com/science/article/pii/S016926072200462X?via%3Dihub) paper and explores the implementation of some of those ideas to develop new tools within the JuliaHealth Observational Health Subecosystem for exploring patient pathways. Additional new features and approaches were added and explored within this project. Additionally, I have authored a developer guide for the package, providing instructions on its use and contribution. This project provided me with hands-on experience in developing production-level code and exposed me to open-source software development practices. I had the opportunity to work in a team, under my mentor, and ensured the integration of the package with the rest of JuliaHealth, facilitating its adoption and usability within the community. \n\n# Project Goals\n\nAs a part of the development, I was majorly engaged with crafting the following functionalities:\n\n1. Selecting treatments of interest: The first decision that was made was to decide the time from which the desired treatments of interest should be included in the treatment pathway study. Here the [periodPriorToIndex](https://github.com/JuliaHealth/OMOPCDMPathways.jl/issues/1) specifies the period (i.e. number of days) prior to the index date from which treatments should be included.\n\n2. Find Treatment History of Patients: Create the [treatment history](https://github.com/JuliaHealth/OMOPCDMPathways.jl/issues/4) of a patient based on target, event, and exit cohorts. Then filter patient events based on the start and end dates of the target cohort. Third, Calculate the duration of treatment eras and gap between treatments.\n\n3. Filters: Filter the treatment history based on the [minEraDuration](https://github.com/JuliaHealth/OMOPCDMPathways.jl/issues/5) parameter and [EraCollapse](https://github.com/JuliaHealth/OMOPCDMPathways.jl/issues/2) parameter.\n\n4. Create Continuous integration and Continuous Development pipeline for the package. \n\n5. Implement the combinationWindow function, that combines treatments with various overlapping strategies.\n\n**Stretch Goals:**\n\n(a) Composing with JuliaStats Ecosystem\n(b) Novel Visualizations for Pathways\n\n\n# Work Details\n\n**1. Setting up the package in JuliaHealth Channel**\n\nInitially, there was no package as such for pathways synthesis, so I had to build it from scratch. Firstly, the repository by the name [OMOPCDMPathways.jl](https://github.com/JuliaHealth/OMOPCDMPathways.jl). Once the repository was created, we needed to have a skeleton for a standard Julia repository. For this, we used the [PkgTemplate.jl](https://juliaci.github.io/PkgTemplates.jl/stable/user/) this provided a basic skeleton for the repository that included - folders for test suites, documentation, src [code files], GitHub files, Readme and Licensing file, toml and citation files. All this we can further edit and modify as per our work. By default, PkgTemplate.jl uses [Documenter.jl](https://documenter.juliadocs.org/stable/) for the documentation part but as suggested and discussed with my mentor we decided to shift to [DocumenterVitepress.jl](https://luxdl.github.io/DocumenterVitepress.jl/dev/) for the documentation part. However, we still faced some deployment issues in the new documentation due to a few mistakes in the `make.jl` file, thanks to [Anshul](https://github.com/asinghvi17) for helping fix the [Deployment issues with DocumenterVitepress](https://github.com/JuliaHealth/OMOPCDMPathways.jl/issues/15). With this, we were ready with the documentation set up and fully functional. After we had shifted to DocumenterVitepress the main task now was to host the documentation, this was done using Github-Actions, detailed steps for hosting are provided at [this](https://documenter.juliadocs.org/stable/man/hosting/#Hosting-Documentation) page. Then we added the codecov to our package by triggering it via a dummy function and a corresponding test case for it. Also, the CI for the package was set up with it. And, now finally the repository was ready with test coverage, CI, and documentation fully functional repository ready.\nInitial documentations: ![](./image.png)\nNew documentations using DocumenterVitepress: ![](./img2.png)\nSo, as a part of it I created a this [documentations](https://luxdl.github.io/DocumenterVitepress.jl/dev/documenter_to_vitepress_docs_example) which provides detailed steps for converting docs from Documenter to DocumenterVitepress.\n\n\n**2. Loading the PostgreSQL Database**\n\nThe main database we worked on/built analysis was the freely available OMOPCDM Database. The Database was PostgreSQL [here](https://www.devart.com/dbforge/postgresql/how-to-install-postgresql-on-linux/) are some instructions on how to set up Postgres in a linux machine. However, I was provided with some more extra synthetic data from my mentor for further testing of the functionalities. Being a very large database we had to strategically download it further, my mentor helped me in setting up the Postgres on my local machine. Once, the database was set up proper testing was performed on it to check if things were as expected. With this, we were done with the database setup as well and could finally dive into the actual code logic for the Pathways synthesis.\n\n**3. Testing and Development setup on my local computer**\n \nTo get a proper environment for functionality creation and concurrent testing we required a proper testing setup so that we could test the new functions made at the same time. This was done using [Revise.jl](https://timholy.github.io/Revise.jl/stable/), which helps to keep Julia sessions running without frequent restarts when making changes to code. It allowed me to edit my code, update packages, or switch git branches during a session, with changes applied immediately in the next command. My mentor helped me set it up, added Revise.jl to the global Julia environment, also PackageCompatUI that provides a terminal text interface to the [compat] section of a Julia Project.toml file, finally made a Julia script by the name β€œstartup.jl” out of it. This script was then added it to β€œ/home/jay-sanjay/.julia/config/” path in my local computer. \n\nHere is the sample for the startup.jl file:\n```julia\n\nusing PackageCompatUI\nusing PkgTemplates\nusing Revise\n\n\n###################################\n# HELPER FUNCTIONS\n###################################\nfunction template()\n Template(;\n user=\"jay-sanjay\",\n dir=\"~/FOSS\",\n authors=\"jaysanjay and contributors\",\n julia=v\"1.6\",\n plugins=[\n ProjectFile(; version=v\"0.0.1\"),\n Git(),\n Readme(),\n License(; name=\"MIT\"),\n GitHubActions(; extra_versions=[\"1.6\", \"1\", \"nightly\"]),\n TagBot(),\n Codecov(),\n Documenter{GitHubActions}(),\n Citation(; readme = true),\n RegisterAction(),\n BlueStyleBadge(),\n Formatter(;style = \"blue\")\n ],\n )\nend\n\n```\n\n\n**4. Selecting treatments of interest**\n\nSo, as a part of this, we used the previously mentioned research paper and discussion with the mentors we came up with logic for it. The first thing to do was to determine the moment in time from which selected treatments of interest should be included in the treatment pathway. The default is all treatments starting after the index date of the target cohort. For example, for a target cohort consisting of newly diagnosed patients, treatments after the moment of first diagnosis are included. However, it would also be desirable to include (some) treatments prior to the index date, for instance in case a specific disease diagnosis is only confirmed after initiating treatment. Therefore, periodPriorToIndex specifies the period (i.e. number of days) prior to the index date from which treatments should be included. We have created two dispatches for this function.\nAfter that proper testing and documentation are also added.\n\nA basic implementation for it is:\n(1) Construct a SQL query to select cohort_definition_id, subject_id, and cohort_start_date from a specified table, filtering by cohort_id.\n(2) The SQL query construction and execution was done using the [FunSQL.jl](https://mechanicalrabbit.github.io/FunSQL.jl/stable/) library, in the below shown manner:\n```julia\nsql = From(tab) |>\n Where(Fun.in(Get.cohort_definition_id, cohort_id...)) |>\n Select(Get.cohort_definition_id, Get.subject_id, Get.cohort_start_date) |>\n q -> render(q, dialect=dialect)\n```\n(3) Executes the constructed SQL query using a database connection, fetching the results into a data frame.\n(4) If the DataFrame is not empty, convert cohort_start_date to DateTime and subtract date_prior from each date, then return the modified DataFrame.\n\nThis was then be called like this:\n```julia\nperiod_prior_to_index(\n cohort_id = [1, 1, 1, 1, 1], \n conn; \n date_prior = Day(100), \n tab=cohort\n )\n```\n\n\n**5. Filters applied**\n\nAfter this, we where needed to get the patient's database filtered more finely so that there are minimal variations that can be ignored. The duration of the above extracted event eras may vary a lot and it can be preferable to limit to only treatments exceeding a minimum duration. Hence, minEraDuration specifies the minimum time an event era should last to be included in the analysis. All these implementations were more of Dataframe manipulation were I used [DataFrames.jl](https://dataframes.juliadata.org/stable/) package.\n\nAfter that proper testing and documentation are also added.\n\nA basic implementation for the minEraDuration is:\nIt filters the treatment history `DataFrame` to retain only those rows where the duration between `drug_exposure_end` and `drug_exposure_start` is at least `minEraDuration`.\nThis function can be used as follows:\n```julia\n#| eval: false \njulia> calculate_era_duration(test_df, 920000)\n4Γ—3 DataFrame\n Row β”‚ person_id drug_exposure_start drug_exposure_end \n β”‚ Int64 Float64 Int64 \n─────┼───────────────────────────────────────────────────\n 1 β”‚ 1 -3.7273e8 -364953600\n 2 β”‚ 1 2.90304e7 31449600\n 3 β”‚ 1 -8.18208e7 -80006400\n 4 β”‚ 1 1.32918e9 1330387200\n```\n\n\nAnother filter we worked on is the EraCollapse. So, let's suppose a case where an individual receives the same treatment for a long period\nof time (e.g. need for chronic treatment). Then it's highly likely that the person would require refills. Now as patients are not 100% adherent, there might be a gap between two subsequent event eras. Usually, these eras are still considered as one treatment episode, and the eraCollapseSize deals with the maximum gap within which two eras of the same event cohort would be collapsed into one era (i.e. seen as a continuous treatment instead of a stop and re-initiation of the same treatment).\nAfter that proper testing and documentation are also added.\n\nA basic implementation for the eraCollapseSize is:\n(a) Sorts the dataframe by event_start_date and event_end_date.\n(b) Calculates the gap between each era and the previous era.\n(c) Filters out rows with gap_same > eraCollapseSize.\n\nThis functions can be used as follows:\n```julia\njulia> EraCollapse(treatment_history = test_df, eraCollapseSize = 400000000)\n4Γ—4 DataFrame\n Row β”‚ person_id drug_exposure_start drug_exposure_end gap_same \n β”‚ Int64 Float64 Int64 Float64 \n─────┼───────────────────────────────────────────────────────────────\n 1 β”‚ 1 -5.33347e8 -532483200 -1.86373e9\n 2 β”‚ 1 -3.7273e8 -364953600 1.59754e8\n 3 β”‚ 1 -8.18208e7 -80006400 2.83133e8\n 4 β”‚ 1 2.90304e7 31449600 1.09037e8\n```\n\n\n**6. Treatment History of the Patients**\n\nThe `create_treatment_history` function constructs a detailed treatment history for patients in a target cohort by processing and filtering event cohort data from a given DataFrame. It begins by isolating the target cohort based on its `cohort_id`, adding a new column for the `index_year` derived from the cohort’s start date. Then, it selects relevant event cohorts based on a provided list of cohort IDs and merges them with the target cohort on the `subject_id` to associate events with individuals in the target group. The function applies different filtering criteria depending on whether the user is interested in treatments starting or ending within a specified period before the target cohort's start date (defined by `periodPriorToIndex`). It keeps only the event cohorts that match the filtering condition, ensuring that only relevant treatments are considered. After filtering, the function calculates time gaps between consecutive cohort events for each patient, adding these gaps to the DataFrame. The final DataFrame provides a history of treatments, including the dates of events and the time intervals between them, offering a clear timeline of treatment for each patient. After that proper testing and documentations are also added.\n\n\n**7. CombinationWindow Functionality to combine overlapping treatments**\n\nNow once we had the filtering of the treatments done, next we need to combine the overlapping treatments based on some set of rules. The combinationWindow which specifies the time that two event eras need to overlap to be considered a combination treatment. If there are more than two overlapping event eras, we sequentially combine treatments, starting from the first two overlapping event eras. \n\nThe `combination_Window` function processes a patient's treatment history by identifying overlapping treatment events and combining them into continuous treatment periods based on certain rules. It first converts `event_cohort_id` into strings and sorts the treatment data by `person_id`, `event_start_date`, and `event_end_date`. The helper function `selectRowsCombinationWindow` calculates gaps between consecutive treatments, marking rows where treatments overlap or occur too closely. In the main loop, the function checks these overlaps and gaps against a specified `combinationWindow`. If treatments overlap (or nearly overlap), the function adjusts the treatment periods by either merging adjacent rows or splitting rows to create continuous treatment periods. The process continues until all overlapping treatments are combined into one, creating an updated and accurate treatment history. The function ensures the final output reflects realistic treatment windows by handling special cases where gaps between treatments are smaller than the treatment durations themselves.\n\nIt mainly covers the three cases as mentioned in the R-research paper:\n\n1. Switch Case:\n\n*Condition*: If the gap between the two treatment events is smaller than the combinationWindow, but the gap is not equal to the duration of either event.\n*Action*: The event_end_date of the previous treatment is set to the event_start_date of the current treatment. This effectively \"shifts\" the previous treatment’s end date to eliminate the gap, merging the treatments into one continuous period.\n*Purpose*: This ensures that treatment gaps that are too small (less than combinationWindow) are treated as part of the same treatment window.\n\n```julia\nif -gap_previous < combinationWindow && !(-gap_previous in [duration_era, prev_duration_era])\n treatment_history[i-1, :event_end_date] = treatment_history[i, :event_start_date]\n```\nHere is the pictorial representation for the same:\n![](./case1.png)\n\n2. FRFS (First Row, First Shortened):\n\n*Condition*: If the gap is larger than or equal to the combinationWindow, or the gap equals the duration of one of the two treatments, and the first treatment ends before or on the same date as the second treatment.\n*Action*: A new row is created where the second treatment’s event_end_date is set to the end date of the first treatment. This preserves the overlap but ensures that the earlier treatment period stays intact.\n*Purpose*: This prevents unnecessary truncation of the first treatment if it spans the entire overlap window.\n\n```julia\nelseif -gap_previous >= combinationWindow || -gap_previous in [duration_era, prev_duration_era]\n if treatment_history[i-1, :event_end_date] <= treatment_history[i, :event_end_date]\n new_row = deepcopy(treatment_history[i, :])\n new_row.event_end_date = treatment_history[i-1, :event_end_date]\n append!(treatment_history, DataFrame(new_row'))\n```\nHere is the pictorial representation for the same:\n![](./case2.png)\n\n3. LRFS (Last Row, First Shortened):\n\n*Condition*: If the gap is larger than or equal to the combinationWindow, or the gap equals the duration of one of the treatments, and the first treatment ends after the second treatment.\n*Action*: The current treatment’s event_end_date is adjusted to match the event_end_date of the previous treatment.\n*Purpose*: This handles cases where the second treatment's window should be shortened to prevent overlap with the previous treatment, merging them into a single continuous window.\n\n```julia\nelse\n treatment_history[i, :event_end_date] = treatment_history[i-1, :event_end_date]\n```\nHere is the pictorial representation for the same:\n![](./case3.png)\n\n\n> *Note:* However, There are a few things left to cover here, most of which is the documentations and writing the test-suite for the same.\n\n# Contributions Beyond Coding\n\n**1. Organizing Meetings and Communication**\n\nThroughout the project, I regularly met with my mentor, [Jacob Zelko], and co-mentor, [Mounika], via weekly Zoom calls to discuss progress and seek guidance. During these meetings, we reviewed my work, identified areas where I needed help, and set clear goals for the upcoming weeks. We used Trello to organize and track these goals, ensuring that nothing was overlooked. My mentors provided detailed insights into specific technical aspects and guided me through the logic behind various functions. Outside of our scheduled meetings, they were always available for quick queries via Slack, ensuring constant support.\n\n**2. Personal Documentation**\n\nIn addition to the notes from our meetings, I maintained personal documentation where I recorded every step I took, including the challenges I faced and the mistakes I made. This helped me reflect on my progress and stay organized throughout the fellowship. Following my selection for GSoC 2024, I also published a blog post on [Medium](https://medium.com/@landgejay124/gsoc-24-the-julia-language-62b809bbec49) to share my journey and experiences with the Julia Language community.\n\n**3. Contributions to the rest of the JuliaHealth repositories**\n\nEarlier I have contributed a lot to the [OMOPCDMCohortCreator.jl](https://github.com/JuliaHealth/OMOPCDMCohortCreator.jl) including adding new functionalities writing test suites, adding blogs including - [Patient Pathways within JuliaHealth](https://github.com/JuliaHealth/juliahealth.github.io/pull/124). Apart from that I also initiated 3 new releases of this package.\n\n\n\n# Conclusions and Future Development\n\nThis project was a 350-hour large project since there were many goals to accomplish. Here is what we accomplished:\n\n1. Built a new repository in JuliaHealth land dedicated especially to treatment pathways synthesis.\n\n2. CI/CD for the Package and Documentation hosting.\n\n3. All required basic functionalities required to build the pathways.\n\n4. Documentations and test suites added for each.\n\nFuture work would include - Finish this [PR](https://github.com/JuliaHealth/OMOPCDMPathways.jl/pull/63) test-suites and documentation are remaining for this PR. Apart from that, we would need to add a [function](https://github.com/JuliaHealth/OMOPCDMPathways.jl/issues/9) that sews up all the functionalities of the package so that the user can run the complete pathways analysis by running just one function instead of running each of the functions manually. Also, in the future, we would explore what statistical functionalities we would need to further explore pathways. Then, we could explore how to compose JuliaHealth with packages from ecosystems like [JuliaStats](https://juliastats.org/) and [JuliaDSP](https://docs.juliadsp.org/stable/contents/) (for time series analysis) that is mentioned in this [PR](https://github.com/JuliaHealth/OMOPCDMPathways.jl/issues/8). And finally work on creating novel visualizations for the Pathways. Commonly used visualizations for treatment pathways (such as sunburst or icicle plots) showing which patients fall under what treatment pathways could be developed as plotting recipes to rapidly visualize various aspects of a patient’s care pathway.\n\n# Acknowledgements πŸ™‡β€β™‚οΈ\n\n1. Jacob Zelko: aka, ”TheCedarPrince”\n2. Mounika Thakkallapally\n\n\nFor there continuous help and support throughout the fellowship.\n\n\n\n", - "supporting": [ - "gsoc-2024-fellows_files/figure-html" - ], - "filters": [], - "includes": {} - } -} \ No newline at end of file diff --git a/_freeze/posts/jay-gsoc/gsoc-2024-fellows/execute-results/html.json b/_freeze/posts/jay-gsoc/gsoc-2024-fellows/execute-results/html.json deleted file mode 100644 index fdb731b..0000000 --- a/_freeze/posts/jay-gsoc/gsoc-2024-fellows/execute-results/html.json +++ /dev/null @@ -1,12 +0,0 @@ -{ - "hash": "324bd489afc82dd6b096e80510af90c7", - "result": { - "engine": "julia", - "markdown": "---\ntitle: \"GSoC '24: Developing Tooling for Observational Health Research in Julia\"\ndescription: \"A summary of my project for Google Summer of Code - 2024\"\nauthor: \"Jay Sanjay Landge\"\ndate: \"9/7/2024\"\nbibliography: ./references.bib\ncsl: ./../../ieee-with-url.csl\ntoc: true\nengine: julia\nimage: false\ncategories:\n - gsoc\n - sql\n - observational health \n - analysis\n---\n\n\n\n\n# Hi Everyone! πŸ‘‹\n\nI am Jay Sanjay, and I am pursuing a Bachelor's degree in Computational Sciences and Engineering at the Indian Institute of Technology (IIT) in Hyderabad, India. Coming from a mathematics and data analysis background, I was initially introduced to Julia at my university lectures. Later, I delved more into the language and the JuliaHealth community - an intersection of Julia, Health Research, Data Sciences, and Informatics. Here, I met some of the great folks in JuliaHealth and I decided to take it on as a full-fledged summer project. \nIn this blog, I will briefly describe what my project is and what I did as a part of it.\n\n\n1. You can find my [**GSoC project archive link**](https://summerofcode.withgoogle.com/archive/2024/projects/ZXVIYAXG)\n\n2. You can also find the related publication of my work on [**Zenodo**](https://zenodo.org/records/14674051)\n\n3. If you want to know more about me, you can connect with me on [**LinkedIn**](https://www.linkedin.com/in/jay-landge-589439260/) and follow me on [**GitHub**](https://github.com/Jay-sanjay)\n\n\n# Background\n\n## What Is Observational Health Research?\n\nObservational Health Research refers to studies that analyze real-world data (such as patient medical claims, electronic health records, etc.) to understand patient health. These studies often encompass a vast amount of data concerning patient care. An outstanding challenge here is that these datasets can become very complex and grow large enough to require advanced computing methods to process this information.\n\n## What Are Patient Pathways?\n\nPatient pathways refer to the journey that patients with specific medical conditions undergo in terms of their treatment. This concept goes beyond simple drug uptake statistics and looks at the sequence of treatments patients receive over time, including first-line treatments and subsequent therapies. Understanding patient pathways is essential for analyzing treatment patterns, adherence to clinical guidelines, and the disbursement of drugs.\nTo analyze patient pathways, one would typically use real-world data from sources such as electronic health records, claims data, and registries. However, barriers such as data interoperability and resource requirements have hindered the full utilization of real-world data for this purpose.\n\nSo to address these challenges we (the JuliaHealth organization and I) want to develop a set of tools to extract and analyze these patient pathways. These sets of tools are based on the Observational Medical Outcomes Partnership (OMOP) Common Data Model, which standardizes healthcare data to promote interoperability.\n\n\n# Project Description\n\nAs part of this project with JuliaHealth, I developed a new package called [**OMOPCDMPathways.jl**](https://github.com/JuliaHealth/OMOPCDMPathways.jl). This package is designed for deployment in research projects, particularly those related to health and medical data analysis. This project takes much inspiration from the paper [_TreatmentPatterns: An r package to facilitate the standardized development and analysis of treatment patterns across disease domains_](https://www.sciencedirect.com/science/article/pii/S016926072200462X?via%3Dihub) [@markus2022treatmentpatterns] and explores the implementation of some of those ideas to develop new tools within the JuliaHealth Observational Health Subecosystem for exploring patient pathways. Additional new features and approaches were added and explored within this project. Additionally, I have authored a developer guide for the package, providing instructions on its use and contribution. This project provided me with hands-on experience in developing production-level code and exposed me to open-source software development practices. I had the opportunity to work in a team, under my mentors, and ensured the integration of the package with the rest of JuliaHealth, facilitating its adoption and usability within the community. \n\n# Project Goals\n\nAs a part of the development, I was majorly engaged in crafting the following functionalities:\n\n1. Selecting treatments of interest: The first decision that was made was to decide the time from which the desired treatments of interest should be included in the treatment pathway study. Here the [periodPriorToIndex](https://github.com/JuliaHealth/OMOPCDMPathways.jl/issues/1) specifies the period (i.e. number of days) before the index date from which treatments should be included.\n\n2. Find Treatment History of Patients: Create the [treatment history](https://github.com/JuliaHealth/OMOPCDMPathways.jl/issues/4) of a patient based on target, event, and exit cohorts. Then filter patient events based on the start and end dates of the target cohort. Third, Calculate the duration of treatment eras and the gap between treatments.\n\n3. Filters: Filter the treatment history based on the [minEraDuration](https://github.com/JuliaHealth/OMOPCDMPathways.jl/issues/5) parameter and [EraCollapse](https://github.com/JuliaHealth/OMOPCDMPathways.jl/issues/2) parameter.\n\n4. Create a Continuous Integration and Continuous Development pipeline for the package. \n\n5. Implement the combinationWindow function, which combines treatments with various overlapping strategies.\n\nAdditionally, we had a few stretch goals which were:\n\n1. Composing with JuliaStats Ecosystem\n\n2. Novel Visualizations for Pathways\n\n# Tasks\n\n## 1. Setting Up the Package in JuliaHealth Channel\n\nInitially, there was no package as such for generating pathways, so I had to build it from scratch. First, I created the repository with the name [OMOPCDMPathways.jl](https://github.com/JuliaHealth/OMOPCDMPathways.jl). Once the repository was created, we needed to have a skeleton for a standard Julia repository. For this, we used the [PkgTemplates.jl](https://juliaci.github.io/PkgTemplates.jl/stable/user/) this provided a basic skeleton for the repository that included - folders for test suites, documentation, src code files, GitHub files, README and LICENSE file, TOML and citation files. All this we can further edit and modify as per our work. By default, PkgTemplate.jl uses [Documenter.jl](https://documenter.juliadocs.org/stable/) for the documentation part but as suggested and discussed with my mentor we decided to shift to [DocumenterVitepress.jl](https://luxdl.github.io/DocumenterVitepress.jl/dev/) for the documentation part. However, we still faced some deployment issues in the new documentation due to a few mistakes in the `make.jl` file, thanks to [Anshul Singhvi](https://github.com/asinghvi17) for helping fix the [Deployment issues with DocumenterVitepress](https://github.com/JuliaHealth/OMOPCDMPathways.jl/issues/15). With this, we were ready with the documentation set up and fully functional. After we had shifted to DocumenterVitepress the main task now was to host the documentation, this was done using Github-Actions, detailed steps for hosting are provided at [this](https://documenter.juliadocs.org/stable/man/hosting/#Hosting-Documentation) page. Then we added the CodeCov to our package by triggering it via a dummy function and a corresponding test case for it. Also, the CI for the package was set up with it. And, now finally the repository was ready with test coverage, CI, and documentation fully functional repository ready. Here's some snapshots of the documentation set-up:\n\n![](./image.png)\n\n> Initial documentation with Documenter.jl\n\n![](./img2.png)\n\n> New documentation using DocumenterVitepress.jl\n\nSo, as a part of it, I created this [documentation](https://luxdl.github.io/DocumenterVitepress.jl/dev/documenter_to_vitepress_docs_example) which provides detailed steps for converting docs from Documenter to DocumenterVitepress.\n\n## 2. Loading the PostgreSQL Database\n\nThe main database we worked on/built analysis was the freely available OMOPCDM Database. The Database was formatted within a PostgreSQL database with [installation instructions here](https://www.devart.com/dbforge/postgresql/how-to-install-postgresql-on-linux/) are some instructions on how to set up Postgres in a Linux machine. However, I was provided with some more extra synthetic data from my mentor for further testing of the functionalities. Being a very large database we had to strategically download it further, my mentor helped me in setting up the Postgres on my local machine. Once, the database was set up proper testing was performed on it to check if things were as expected. With this, we were done with the database setup as well and could finally dive into the actual code logic for the Pathways synthesis.\n\n## 3. Testing and Development setup on my local computer\n \nTo get a proper environment for functionality creation and concurrent testing we required a proper testing setup so that we could test the new functions made at the same time. This was done using [Revise.jl](https://timholy.github.io/Revise.jl/stable/), which helps to keep Julia sessions running without frequent restarts when making changes to code. It allowed me to edit my code, update packages, or switch git branches during a session, with changes applied immediately in the next command. My mentor helped me set it up, added Revise.jl to the global Julia environment, also [PackageCompatUI](https://github.com/GunnarFarneback/PackageCompatUI.jl) that provides a terminal text interface to the [compat] section of a Julia Project.toml file, and finally made a Julia script by the name β€œstartup.jl” out of it. This script was then added to `/home/jay-sanjay/.julia/config/` path in my local computer. \n\nHere is the sample for the startup.jl file:\n\n```julia\nusing PackageCompatUI\nusing PkgTemplates\nusing Revise\n\n###################################\n# HELPER FUNCTIONS\n###################################\nfunction template()\n Template(;\n user=\"jay-sanjay\",\n dir=\"~/FOSS\",\n authors=\"jaysanjay and contributors\",\n julia=v\"1.6\",\n plugins=[\n ProjectFile(; version=v\"0.0.1\"),\n Git(),\n Readme(),\n License(; name=\"MIT\"),\n GitHubActions(; extra_versions=[\"1.6\", \"1\", \"nightly\"]),\n TagBot(),\n Codecov(),\n Documenter{GitHubActions}(),\n Citation(; readme = true),\n RegisterAction(),\n BlueStyleBadge(),\n Formatter(;style = \"blue\")\n ],\n )\nend\n\n```\n\n\n## 4. Selecting Treatments of Interest\n\nSo, as a part of this, we used the previously mentioned research paper and discussion with the mentors we came up with logic for it. The first thing to do was to determine the moment in time from which selected treatments of interest should be included in the treatment pathway. The default is all treatments starting after the index date of the target cohort. For example, for a target cohort consisting of newly diagnosed patients, treatments after the moment of first diagnosis are included. However, it would also be desirable to include (some) treatments before the index date, for instance in case a specific disease diagnosis is only confirmed after initiating treatment. Therefore, periodPriorToIndex specifies the period (i.e. number of days) before the index date from which treatments should be included. We have created two dispatches for this function.\nAfter that proper testing and documentation are also added.\n\nA basic implementation for it is:\n\n1. Construct a SQL query to select cohort_definition_id, subject_id, and cohort_start_date from a specified table, filtering by cohort_id.\n\n2. The SQL query construction and execution was done using the [FunSQL.jl](https://mechanicalrabbit.github.io/FunSQL.jl/stable/) library, in the below-shown manner:\n\n```julia\nsql = From(tab) |>\n Where(Fun.in(Get.cohort_definition_id, cohort_id...)) |>\n Select(Get.cohort_definition_id, Get.subject_id, Get.cohort_start_date) |>\n q -> render(q, dialect=dialect)\n```\n3. Executes the constructed SQL query using a database connection, fetching the results into a data frame.\n\n4. If the DataFrame is not empty, convert cohort_start_date to DateTime and subtract date_prior from each date, then return the modified DataFrame.\n\nThis was then be called this:\n```julia\nperiod_prior_to_index(\n cohort_id = [1, 1, 1, 1, 1], \n conn; \n date_prior = Day(100), \n tab=cohort\n )\n```\n\n\n## 5. Filters Applied\n\nAfter this, we where needed to get the patient's database filtered more finely so that there are minimal variations that can be ignored. The duration of the above extracted event eras may vary a lot and it can be preferable to limit to only treatments exceeding a minimum duration. Hence, minEraDuration specifies the minimum time an event era should last to be included in the analysis. All these implementations were more of Dataframe manipulation where I used [DataFrames.jl](https://dataframes.juliadata.org/stable/) package.\n\nAfter that proper testing and documentation are also added.\n\nA basic implementation for the minEraDuration is:\nIt filters the treatment history `DataFrame` to retain only those rows where the duration between `drug_exposure_end` and `drug_exposure_start` is at least `minEraDuration`.\nThis function can be used as follows:\n```julia\n#| eval: false \n\ncalculate_era_duration(test_df, 920000)\n\n#= ... =#\n\n4Γ—3 DataFrame\n Row β”‚ person_id drug_exposure_start drug_exposure_end \n β”‚ Int64 Float64 Int64 \n─────┼───────────────────────────────────────────────────\n 1 β”‚ 1 -3.7273e8 -364953600\n 2 β”‚ 1 2.90304e7 31449600\n 3 β”‚ 1 -8.18208e7 -80006400\n 4 β”‚ 1 1.32918e9 1330387200\n```\n\n\nAnother filter we worked on is the EraCollapse. So, let's suppose a case where an individual receives the same treatment for a long period\nof time (e.g. need for chronic treatment). Then it's highly likely that the person would require refills. Now as patients are not 100% adherent, there might be a gap between two subsequent event eras. Usually, these eras are still considered as one treatment episode, and the eraCollapseSize deals with the maximum gap within which two eras of the same event cohort would be collapsed into one era (i.e. seen as a continuous treatment instead of a stop and re-initiation of the same treatment).\nAfter that proper testing and documentation are also added.\n\nA basic implementation for the eraCollapseSize is:\n(a) Sorts the data frame by event_start_date and event_end_date.\n(b) Calculates the gap between each era and the previous era.\n(c) Filters out rows with gap_same > eraCollapseSize.\n\nThese functions can be used as follows:\n```julia\n#| eval: false \n\n#= ... =#\n\nEraCollapse(treatment_history = test_df, eraCollapseSize = 400000000)\n4Γ—4 DataFrame\n Row β”‚ person_id drug_exposure_start drug_exposure_end gap_same \n β”‚ Int64 Float64 Int64 Float64 \n─────┼───────────────────────────────────────────────────────────────\n 1 β”‚ 1 -5.33347e8 -532483200 -1.86373e9\n 2 β”‚ 1 -3.7273e8 -364953600 1.59754e8\n 3 β”‚ 1 -8.18208e7 -80006400 2.83133e8\n 4 β”‚ 1 2.90304e7 31449600 1.09037e8\n```\n\n\n## 6. Treatment History of the Patients\n\nThe `create_treatment_history` function constructs a detailed treatment history for patients in a target cohort by processing and filtering event cohort data from a given DataFrame. It begins by isolating the target cohort based on its `cohort_id`, adding a new column for the `index_year` derived from the cohort’s start date. Then, it selects relevant event cohorts based on a provided list of cohort IDs and merges them with the target cohort on the `subject_id` to associate events with individuals in the target group. The function applies different filtering criteria depending on whether the user is interested in treatments starting or ending within a specified period before the target cohort's start date (defined by `periodPriorToIndex`). It keeps only the event cohorts that match the filtering condition, ensuring that only relevant treatments are considered. After filtering, the function calculates time gaps between consecutive cohort events for each patient, adding these gaps to the DataFrame. The final DataFrame provides a history of treatments, including the dates of events and the time intervals between them, offering a clear timeline of treatment for each patient. After that proper testing and documentation are also added.\n\n\n## 7. CombinationWindow Functionality To Combine Overlapping Treatments\n\nNow once we have the filtering of the treatments done, we need to combine the overlapping treatments based on some set of rules. The combinationWindow specifies the time that two event eras need to overlap to be considered a combination treatment. If there are more than two overlapping event eras, we sequentially combine treatments, starting from the first two overlapping event eras. \n\nThe `combination_Window` function processes a patient's treatment history by identifying overlapping treatment events and combining them into continuous treatment periods based on certain rules. It first converts `event_cohort_id` into strings and sorts the treatment data by `person_id`, `event_start_date`, and `event_end_date`. The helper function `selectRowsCombinationWindow` calculates gaps between consecutive treatments, marking rows where treatments overlap or occur too closely. In the main loop, the function checks these overlaps and gaps against a specified `combinationWindow`. If treatments overlap (or nearly overlap), the function adjusts the treatment periods by either merging adjacent rows or splitting rows to create continuous treatment periods. The process continues until all overlapping treatments are combined into one, creating an updated and accurate treatment history. The function ensures the final output reflects realistic treatment windows by handling special cases where gaps between treatments are smaller than the treatment durations themselves.\n\nIt mainly covers the three cases mentioned in the R-research paper:\n\n### Switch Case:\n\n*Condition*: If the gap between the two treatment events is smaller than the combinationWindow, but the gap is not equal to the duration of either event.\n*Action*: The event_end_date of the previous treatment is set to the event_start_date of the current treatment. This effectively \"shifts\" the previous treatment’s end date to eliminate the gap, merging the treatments into one continuous period.\n*Purpose*: This ensures that treatment gaps that are too small (less than combinationWindow) are treated as part of the same treatment window.\n\n```julia\n#| eval: false \n\n#= ... =#\n\nif -gap_previous < combinationWindow && !(-gap_previous in [duration_era, prev_duration_era])\n treatment_history[i-1, :event_end_date] = treatment_history[i, :event_start_date]\n```\nHere is the pictorial representation for the same:\n![](./case1.png)\n\n### FRFS (First Row, First Shortened):\n\n*Condition*: If the gap is larger than or equal to the combinationWindow, or the gap equals the duration of one of the two treatments, and the first treatment ends before or on the same date as the second treatment.\n*Action*: A new row is created where the second treatment’s event_end_date is set to the end date of the first treatment. This preserves the overlap but ensures that the earlier treatment period stays intact.\n*Purpose*: This prevents unnecessary truncation of the first treatment if it spans the entire overlap window.\n\n```julia\n#| eval: false \n\n#= ... =#\n\nelseif -gap_previous >= combinationWindow || -gap_previous in [duration_era, prev_duration_era]\n if treatment_history[i-1, :event_end_date] <= treatment_history[i, :event_end_date]\n new_row = deepcopy(treatment_history[i, :])\n new_row.event_end_date = treatment_history[i-1, :event_end_date]\n append!(treatment_history, DataFrame(new_row'))\n```\nHere is the pictorial representation for the same:\n![](./case2.png)\n\n### LRFS (Last Row, First Shortened):\n\n*Condition*: If the gap is larger than or equal to the combinationWindow, or the gap equals the duration of one of the treatments, and the first treatment ends after the second treatment.\n*Action*: The current treatment’s event_end_date is adjusted to match the event_end_date of the previous treatment.\n*Purpose*: This handles cases where the second treatment's window should be shortened to prevent overlap with the previous treatment, merging them into a single continuous window.\n\n```julia\n#| eval: false \n\n#= ... =#\n\nelse\n treatment_history[i, :event_end_date] = treatment_history[i-1, :event_end_date]\n```\nHere is the pictorial representation for the same:\n![](./case3.png)\n\n\n> *Note:* However, There are a few things left to cover here, most of which are the documentation and writing the test suite for the same.\n\n# Contributions Beyond Coding\n\n## 1. Organizing Meetings and Communication\n\nThroughout the project, I regularly met with my mentor, [Jacob Zelko], and co-mentor, [Mounika], via weekly Zoom calls to discuss progress and seek guidance. During these meetings, we reviewed my work, identified areas where I needed help, and set clear goals for the upcoming weeks. We used Trello to organize and track these goals, ensuring that nothing was overlooked. My mentors provided detailed insights into specific technical aspects and guided me through the logic behind various functions. Outside of our scheduled meetings, they were always available for quick queries via Slack, ensuring constant support.\n\n## 2. Personal Documentation\n\nIn addition to the notes from our meetings, I maintained personal documentation where I recorded every step I took, including the challenges I faced and the mistakes I made. This helped me reflect on my progress and stay organized throughout the fellowship. Following my selection for GSoC 2024, I also published a blog post on [Medium](https://medium.com/@landgejay124/gsoc-24-the-julia-language-62b809bbec49) to share my journey and experiences with the Julia Language community.\n\n## 3. Contributions To the Rest of the JuliaHealth Repositories\n\nEarlier I have contributed a lot to the [OMOPCDMCohortCreator.jl](https://github.com/JuliaHealth/OMOPCDMCohortCreator.jl) including adding new functionalities writing test suites, adding blogs including - [Patient Pathways within JuliaHealth](https://github.com/JuliaHealth/juliahealth.github.io/pull/124). Apart from that I also initiated 3 new releases of this package.\n\n# Conclusions and Future Development\n\nThis project was a 350-hour large project since there were many goals to accomplish. Here is what we accomplished:\n\n1. Built a new repository in JuliaHealth land dedicated especially to treatment pathways synthesis.\n\n2. CI/CD for the Package and Documentation hosting.\n\n3. All required basic functionalities required to build the pathways.\n\n4. Documentation and test suites added for each.\n\nFuture work would include:\n\n- Finish this [PR](https://github.com/JuliaHealth/OMOPCDMPathways.jl/pull/63) test-suites and documentation are remaining for this PR. \n\n- Apart from that, we would need to add a [function](https://github.com/JuliaHealth/OMOPCDMPathways.jl/issues/9) that sews up all the functionalities of the package so that the user can run the complete pathways analysis by running just one function instead of running each of the functions manually. \n\n- Also, in the future, we would explore what statistical functionalities we would need to further explore pathways. \n\n- Then, we could explore how to compose JuliaHealth with packages from ecosystems like [JuliaStats](https://juliastats.org/) and [JuliaDSP](https://docs.juliadsp.org/stable/contents/) (for time series analysis) that are mentioned in this [PR](https://github.com/JuliaHealth/OMOPCDMPathways.jl/issues/8). \n\n- And finally work on creating novel visualizations for the Pathways. Commonly used visualizations for treatment pathways (such as sunburst or icicle plots) showing which patients fall under what treatment pathways could be developed as plotting recipes to visualize various aspects of a patient’s care pathway rapidly.\n\n# Acknowledgements πŸ™‡β€β™‚οΈ\n\n1. [Jacob S. Zelko](https://jacobzelko.com): aka, [TheCedarPrince](https://github.com/TheCedarPrince)\n\n2. [Mounika Thakkallapally](https://www.linkedin.com/in/mounika-thakkallapally/)\n\nThank you for your continuous help and support throughout the fellowship.\n_Note: This blog post was also written with the assistance of LLM technologies to help with grammar, flow, and spelling!_\n\n\n", - "supporting": [ - "gsoc-2024-fellows_files" - ], - "filters": [], - "includes": {} - } -} \ No newline at end of file diff --git a/_freeze/posts/michela-gsoc/Michela_JSoC/execute-results/html.json b/_freeze/posts/michela-gsoc/Michela_JSoC/execute-results/html.json deleted file mode 100644 index 6cb9163..0000000 --- a/_freeze/posts/michela-gsoc/Michela_JSoC/execute-results/html.json +++ /dev/null @@ -1,12 +0,0 @@ -{ - "hash": "55d28893470821c6ce2b9a35078cfa9b", - "result": { - "engine": "julia", - "markdown": "---\ntitle: \"GSoC '24: IPUMS.jl Small Project\"\ndescription: \"A summary of my project for Google Summer of Code\"\nauthor: \"Michela Rocchetti\"\ndate: \"8/26/2024\"\ntoc: true\nengine: julia\nimage: false\ncategories:\n - gsoc\n - geospatial \n - census\n---\n\n# Hello! πŸ‘‹\n\nHi! I am Michela, I have a Master's degree in Physics of Complex Systems and I am currently working as a software engineer in Rome, where I am from. \nDuring my studies, I became interested in the use of modeling and AI methods to improve healthcare and how these tools can be used to better understand how cultural and social backgrounds influence the health of individuals. \nI am also interested in the computational modeling of the brain and the human body and its implications for a better understanding of certain pathological conditions. \n\nWith these motivations in mind, I heard about Google Summer of Code. \nSince I had studied Julia in some courses and given that the language is expanding rapidly, I decided to find a project within Julia. \nAs a result, I found the project of [Jacob Zelko (@TheCedarPrince)](https://jacobzelko.com) to start this experience. \n\n> If you want to learn more about me, you can connect with me here: [**LinkedIn**](https://www.linkedin.com/in/michela-rocchetti-261793218/), [**GitHub**](https://github.com/MichelaRocchetti)\n\n# Project Description \n\n*IPUMS* is the \"world's largest available single database of census microdata\", providing survey and census data from around the world. \nIt includes several projects that provide a wide variety of datasets.\nThe information and data collected by *IPUMS* are useful for comparative research, as well as for the analysis of individuals in their life contexts.\nThese data can be used to create a more comprehensive dataset that will facilitate research on the social determinants of health for different types of diseases, social communities, and geographical areas. \n\n![](./IPUMS_grid_logo.png)\n\n> To learn more about IPUMS, visit the [website](https://www.ipums.org) \n\n# Tasks and Goals\nThe primary objectives of this proposal are to:\n\n1. Develop a native Julia package to interact with the APIs available around the datasets *IPUMS* provides.\n\n2. Provide useful utilities within this package for manipulating *IPUMS* datasets.\n\n3. Compose this package with the wider Julia ecosystem to enable novel research in health, economics, and more. \n\nTo achieve this, the work was distributed as follows:\n\n1. Expand some of the functionality developed in `ipumsr` *IPUMS* NHGIS\n - Create a link between OpenAPI documentation and the functions internally used in IPUMS.jl:\n updating already present functions, determining if updating is needed, and testing them\n - Develop functionality similar to the get_metadata_nghis function present in ipumsr\n\n2. Update *IPUMS* documentation\n - Set up and deploy DocumenterVitepress.jl \n - Write a blog post on how IPUMS.jl can be composed within the ecosystem.\n\n\n# How the work was done\n\nThe first task was to migrate documents from Documenter to DocumenterVitepress.This issue aims to support the significant refactoring underway across JuliaHealth, aimed at improving the discoverability and cohesion of the JuliaHealth ecosystem, particularly about documentation. This issue is intended to create a more attractive entry point for new Julia users interested in health research within the Julia community.\nTo accomplish this task, a dependency of DocumenterVitepress was added to the docs directory of the IPUMS.jl repository. \nOnce this was done, the Documenter.jl make.jl file was migrated into a DocumenterVitepress.jl make.jl file. Working on the make.jl file, the pages structure were added to the web page explaining the IPUMS.jl package. With this in mind, those were added:\n 1. Home: to explain the main purpose of the package\n 2. Workflows: to explain the working process\n 3. How to: to give general information \n 4. Tutorials: to show how to use IPUMS.jl \n 5. Examples: some examples of activities\n 6. Mission: to explain why the package is useful for the community\n 7. References: references used to write the pages.\n\nThis first task takes some time, especially setting up GitHub and cloning the repository locally. At this point, my experience with GitHub was really limited and I had to learn how to use the Git environment from scratch, for example how to do continuous integration (to commit code to a shared repository), documentation release and merge, and local testing. I found the support of my mentors and searching for material online was really helpful. \n\nThe second task was to update the documentation of IPUMS.jl by modifying the functionality within the model folder in the IPUMS.jl folder. The main aim of this task was to\na description of the function and its attributes, an example of possible implementation and result, and finally to show how to use it. The documentation to be updated as of several types of functions:\n 1. Data extract\n 2. Data set\n 3. Data Table\n 4. Time series table\n 5. Error\n 6. Shapefile.\n Each of these macro-categories (from 1 to 4) contains a set of functions, each signaling the different expected output and specific purpose.\n Information about what each function does, and the meaning of each specific input variable, has been found on the *IPUMS* website and references have been made in the written documentation.\n\n# How to work with IPUMS\n After writing down the description of the function and the inputs, examples were formulated, starting from the *IPUMS* website: when you register at [IPUMS](https://uma.pop.umn.edu/usa/user/new), an API key is given. \nwhich is used, among other things, to run pre-written code on the website. This code contains examples of these functions, and these examples \nhave been adapted by changing some input values and adapting them to work in the Julia framework. The latter task was done by simply rewriting some structures, such as dictionaries, maps, or lists, in the\nJulia language. \nHere is a small guide on how to set up working with the API:\n1. Create an *IPUMS* account\n2. Log in to your account \n3. Copy the API key, which can be obtained from the [website](https://account.ipums.org/api_keys)\n4. Use the key to run the code that is already available on the [*IPUMS* Developer Portal](https://developer.ipums.org/docs/v2/reference/), where you will also find information about the variables and packages.\n\n# Functions testing\n\n A final task was to test the functions in the 'api_IPUMSAPI.jl' file. In this file, the function to be tested and other functions are defined and the most important ones are extracted to be available in the\n available throughout the framework. Some of the functions to be tested were the following:\n \n 1. `metadata_nhgis_data_tables_get`\n 2. `metadata_nhgis_datasets_dataset_data_tables_data_table_get`\n 3. `metadata_nhgis_datasets_dataset_get`\n 4. `metadata_nhgis_datasets_get`\n\n Before working on the Julia files, testing and understanding the original R function was done using R studio. \n\n![](./rstudio.png)\n\nEach function was then tested using the API key from the *IPUMS* registration as well as other input examples taken from the documentation or the *IPUMS* website. \nor from the *IPUMS* website. All functions were displayed successfully, giving the expected result, so it can be concluded that the translation from R to Julia is successful.\n\n::: {#2 .cell execution_count=0}\n``` {.julia .cell-code}\nusing IPUMS\nusing OpenAPI\n\napi_key = \"insert your key here\"\n\nversion = \"2\"\npage_number = 1\npage_size = 2500\n#media_type = \n\napi = IPUMSAPI(\"https://api.ipums.org\", Dict(\"Authorization\" => api_key));\n\nres1 = metadata_nhgis_data_tables_get(api, version)\n\nres2 = metadata_nhgis_datasets_dataset_get(api, \"2022_ACS1\", \"2\");\n\nres3 = metadata_nhgis_datasets_dataset_data_tables_data_table_get(api, \"2022_ACS1\",\"B01001\", \"2\");\n\nres4 = metadata_nhgis_datasets_get(api, \"2\");\n```\n:::\n\n\n\nAn example of the output is: \n\n```{json}\n. . .\n\n{\n \"name\": \"NT1\",\n \"nhgisCode\": \"AAA\",\n \"description\": \"Total Population\",\n \"universe\": \"Persons\",\n \"sequence\": 1,\n \"datasetName\": \"1790_cPop\",\n \"nVariables\": [\n 1\n ]\n}\n\n. . .\n```\n\n# Accomplished Goals and Future Development\n\nThe project was a 90-hour small project and during this time the documentation was completed and the testing of the metadata function was done, as well as the migration from Documenter.jl to DocumenterVitepress.jl.\nDuring these months some things took longer than I expected because of some problems that occurred, so some things were missing in relation to the original plan. However, this time was useful for learning new things: \n - I saw how to work with a package under development, how to work with large datasets, and how to write documentation \n - I had the opportunity to better understand how to work with Git and GitHub\n - I learned some new things about R, which was a completely unknown language to me. \n - I deepened my knowledge of Julia, a language I had worked with during my time at university.\n - I had the chance to work on a large open-source project, to be part of a large community, and to learn how to communicate with it efficiently. \n\nA special thanks goes to my mentors, Jacob Zelko and Krishna Bhogaonker, for helping me through this process.\n\nFuture developments of this work could include deepening the work that my mentors and I have started, with the possibility of integrating this package with other machine learning packages in Julia and, from there, doing new analyses of the data in terms of social and geographical implications for health.\n\n", - "supporting": [ - "Michela_JSoC_files" - ], - "filters": [], - "includes": {} - } -} \ No newline at end of file diff --git a/_freeze/posts/mounika-gsoc-mentor/index/execute-results/html.json b/_freeze/posts/mounika-gsoc-mentor/index/execute-results/html.json deleted file mode 100644 index 67cbbbc..0000000 --- a/_freeze/posts/mounika-gsoc-mentor/index/execute-results/html.json +++ /dev/null @@ -1,12 +0,0 @@ -{ - "hash": "aa8ac27b7ea412105115a41b1ab4c229", - "result": { - "engine": "julia", - "markdown": "---\ntitle: \"GSoC Co-Mentoring Experience\"\ndescription: \"My experience as a GSoC co-mentor within JuliaHealth\"\nauthor: \"Mounika Thakkallapally\"\ndate: \"9/12/2024\"\ntoc: true\nengine: julia\nbibliography: ./references.bib\ncsl: ./../../ieee-with-url.csl\ncategories:\n - gsoc\n - mentor\n - experience\n---\n\n# Introduction\n\nHello πŸ‘‹, I am Mounika. I am a Data Engineer at [Brown Center for Biomedical Informatics](https://bcbi.brown.edu/). This summer, I had the privilege of co-mentoring a talented student, [Jay Sanjay](https://www.linkedin.com/in/jay-landge-589439260/) alongside [Jacob Zelko (\\@TheCedarPrince)](https://jacobzelko.com) on a [project](https://summerofcode.withgoogle.com/programs/2024/projects/ZXVIYAXG) for Google Summer of Code (aka [GSoC](https://summerofcode.withgoogle.com/)). Here, I would like to share my experience as a co-mentor, offering insights for future mentors and students alike. \n\nBefore diving into my experience, let me provide some background on how it all started. At JuliaCon 2023, I had the chance to meet Jacob Zelko and have been following his work at [JuliaHealth](https://juliahealth.org/) ever since. One day, I received a message from Jacob asking if I'd be interested in co-mentoring Jay for his GSoC project. Fortunately, I was already working on several projects at BCBI involving Julia programming, [OMOP CDM databases](https://ohdsi.github.io/CommonDataModel/cdm54.html) and [OHDSI](https://ohdsi.org/) tools, all of which were closely aligned with Jay's project.\n\n> Feel free to visit Jay's work on [OMOPCDMPathways.jl](https://github.com/JuliaHealth/OMOPCDMPathways.jl) or read about his [fellowship experience from this post](https://juliahealth.org/JuliaHealthBlog/posts/jay-gsoc/gsoc-2024-fellows.html).\n\n# Mentor-Mentee Relationship\n\nJay, being a proactive student with a strong involvement in JuliaHealth, worked closely with Jacob to build a [proposal for the project](https://summerofcode.withgoogle.com/organizations/the-julia-language/projects/details/ZXVIYAXG) several months before GSoC began this year. His early involvement and familiarity with the community set a solid foundation for the project. Jacob, with his extensive experience mentoring GSoC students over the years, brought invaluable insights not only for Jay but also for me, as I was just beginning my journey as a mentor.\n\nJacob established regular weekly Zoom meetings for the three of us to discuss Jay's progress, review his accomplishments, and plan the next steps. During these meetings, I focused on taking detailed notes to ensure we stayed organized and up to date with all the tasks. We used [Trello](https://trello.com/), a project management tool, to track progress and manage project tasks efficiently. Additionally, we stayed connected thoughout the week via a dedicated slack channel for any ongoing discussions or questions (on the [Julia Slack](https://julialang.org/slack/#the_julia_language_slack)).\n\n# Technical Discussion \n\nJay's project \"Developing Tooling for Observational Health Research in Julia\" was inspired by the [TreatmentPatterns R package](https://www.sciencedirect.com/science/article/pii/S016926072200462X?via%3Dihub) [@markus2022treatmentpatterns]. The main goal of the project was to enhance observational research capabilities within the JuliaHealth ecosystem. To help Jay get started, [Jacob created 10 to 15 GitHub issues](https://github.com/JuliaHealth/OMOPCDMPathways.jl/issues?q=), each linked to a specific function that Jay planned to work on.\n\nDuring our weekly meeting, we discussed the challenges Jay encountered, any roadblacks in his progress, and reviewed the pull requests he submitted on GitHub. These sessions allowed us to provide timely feedback and guide Jay through complex technical issues, ensuring steady progress throughout the project.\n\n# Learnings and Observations\n\nJay's proactive approach, steady progress, thoughtful questions, and clear focus on completing the project are qualities from which every student can benefit. His dedication to learning and problem-solving made a significant impact on the success of the project.\n\n## Tips for Mentees\n\nFrom a mentee's perspective having the following qualities would be helpful \n\n1. **Stick to the proposal:** While it's natural to feel the urge explore new ideas beyond the original proposal, it's essential to remain focused on the original proposal due to time constrains. \n\n2. **Adaptability and open-mindedness:** Be open to feedback and willing to adjust the tasks as you face challenges. \n\n3. **Time Management:** Many students juggle internships, interviews and other commitments during the summer. So it's to manage time effectively and discuss with the mentor about the progress during those times. \n\n4. **Effective communication:** Stay up to date with any updates from GSoC or from the mentor. Keeping your mentor updated about your progress or any challenges helps build a collaborative and supportive mentor relationship. \n\n## Tips for Mentors\n\nOn the other hand, Jacob demonstrated what it means to be an effective mentor. He showed me how to foster a supportive, collaborative relationship with the student. These are the lessons that I will carry forward in future mentorship roles:\n\nFrom a mentor's perspective having the following qualities would be helpful \n\n1. **Clear communication:** Communicating well in advance about the availability to meet or to review the work, having frequent meetings with the mentee would be helpful. \n\n2. **Encouragement:** While offering support, it's important to encourage the mentee to take ownership of the project. \n\n3. **Commitment and time:** Mentoring GSoC is a voluntary role, often taken on in addition to regular professional work. Balancing GSoC with other work commitments requires effective time management and commitment. \n\n4. **Structured Guidance:** Providing a well-organized plan, such as using task management tools like Trello and GitHub issues, ensures that the mentee can follow a clear path towards success completion of the project. \n\n# Conclusion\n\nGoogle Summer of Code offers an incredible opportunity for students to hone their programming skills while contributing to impactful open-source projects. It was a rewarding experience to be part of this journey as a co-mentor, and I am grateful to Jacob for giving me the chance to be involved in such a meaning project with the JuliaHealth community.\n\nThrough this experience, I not only gained insights into effective mentorship but also deepened my understanding of open-source collaboration and its potential to drive innovation in healthcare. I'm excited to explore further ways I can contribute to the JuliaHealth ecosystem and continue supporting the community.\n\n## Let's Keep in Touch!\n\nIf you would like to know more about me, you can connect with me on [Linkedin](https://www.linkedin.com/in/mounika-thakkallapally/).\n\n", - "supporting": [ - "index_files/figure-html" - ], - "filters": [], - "includes": {} - } -} \ No newline at end of file diff --git a/_freeze/posts/ryan-gsoc/Ryan_GSOC/execute-results/html.json b/_freeze/posts/ryan-gsoc/Ryan_GSOC/execute-results/html.json deleted file mode 100644 index cf1c48b..0000000 --- a/_freeze/posts/ryan-gsoc/Ryan_GSOC/execute-results/html.json +++ /dev/null @@ -1,12 +0,0 @@ -{ - "hash": "9b2677886ca73ecdd4ecc1f50f1c298e", - "result": { - "engine": "julia", - "markdown": "---\ntitle: \"GSoC '24: Enhancements to KomaMRI.jl GPU Support\"\ndescription: \"A summary of my project for Google Summer of Code\"\nauthor: \"Ryan Kierulf\"\ndate: \"8/30/2024\"\ntoc: true\nengine: julia\nimage: false\ncategories:\n - gsoc\n - mri\n - gpu\n - hpc\n - simulation\n---\n\n# Hi! πŸ‘‹\n\nI am Ryan, an MS student currently studying computer science at the University of Wisconsin-Madison. Looking for a project to work on this summer, my interest in high-performance computing and affinity for the Julia programming language drew me to Google Summer of Code, where I learned about this project opportunity to work on enhancing GPU support for KomaMRI.jl. \n\nIn this post, I'd like to summarize what I did this summer and everything I learned along the way!\n\n> If you want to learn more about me, you can connect with me here: [**LinkedIn**](https://www.linkedin.com/in/ryan-kierulf-022062201/), [**GitHub**](https://github.com/rkierulf)\n\n# What is KomaMRI?\n\n[KomaMRI](https://github.com/JuliaHealth/KomaMRI.jl) is a Julia package for efficiently simulating Magnetic Resonance Imaging (MRI) acquisitions. MRI simulation is a useful tool for researchers, as it allows testing new pulse sequences to analyze the signal output and image reconstruction quality without needing to actually take an MRI, which may be time or cost-prohibitive.\n\nIn contrast to many other MRI simulators, KomaMRI.jl is open-source, cross-platform, and comes with an intuitive user interface (To learn more about KomaMRI, you can read the paper introducing it [here](https://onlinelibrary.wiley.com/doi/full/10.1002/mrm.29635)). However, being developed fairly recently, there are still new features that can be added and optimization to be done.\n\n# Project Goals\n\nThe goals outlined by Carlos (my project mentor) and I the beginning of this summer were:\n\n1. Extend GPU support beyond CUDA to include AMD, Intel, and Apple Silicon GPUs, through the packages [AMDGPU.jl](https://github.com/JuliaGPU/AMDGPU.jl), [oneAPI.jl](https://github.com/JuliaGPU/oneAPI.jl), and [Metal.jl](https://github.com/JuliaGPU/Metal.jl)\n\n2. Create a CI pipeline to be able to test each of the GPU backends\n\n3. Create a new kernel-based simulation method optimized for the GPU, which we expected would outperform array broadcasting\n\n4. (Stretch Goal) Look into ways to support running distributed simulations across multiple nodes or GPUs\n\n\n# Step 1: Support for Different GPU backends\n\nPreviously, KomaMRI's support for GPU acceleration worked by converting each array used within the simulation to a `CuArray`, the device array type defined in [CUDA.jl](https://github.com/JuliaGPU/CUDA.jl). This was done through a general `gpu` function. The inner simulation code is GPU-agnostic, as the same operations can be performed on a CuArray or a plain CPU Array. This approach is good for extensibility, as it does not require writing different simulation code for the CPU / GPU, or different GPU backends, and would only work in a language like Julia based on runtime dispatch!\n\nTo extend this to multiple GPU backends, all that is needed is to generalize the `gpu` function to convert to either the device types of CUDA.jl, AMDGPU.jl, Metal.jl, or oneAPI.jl, depending on which backend is being used. To give an idea of what the gpu conversion code looked like before, here is a snippet:\n\n```julia\nstruct KomaCUDAAdaptor end\nadapt_storage(to::KomaCUDAAdaptor, x) = CUDA.cu(x)\n\nfunction gpu(x)\n check_use_cuda()\n return use_cuda[] ? fmap(x -> adapt(KomaCUDAAdaptor(), x), x; exclude=_isleaf) : x\nend\n\n#CPU adaptor\nstruct KomaCPUAdaptor end\nadapt_storage(to::KomaCPUAdaptor, x::AbstractArray) = adapt(Array, x)\nadapt_storage(to::KomaCPUAdaptor, x::AbstractRange) = x\n\ncpu(x) = fmap(x -> adapt(KomaCPUAdaptor(), x), x)\n```\n\nThe `fmap` function is from the package `Functors.jl` and can recursively apply a function to a struct tagged with `@functor`. The function being applied is `adapt` from `Adapt.jl`, which will call the lower-level `adapt_storage` function to actually convert to / from the device type. The second parameter to `adapt` is what is being adapted, and the first is what it is being adapted to, which in this case is a custom adapter struct `KomaCUDAAdapter`. \n\nOne possible approach to generalize to different backends would be to define additional adapter structs for each backend and corresponding `adapt_storage` functions. This is what the popular machine learning library [Flux.jl](https://github.com/FluxML/Flux.jl) does. However, there is a simpler way!\n\nEach backend package (CUDA.jl, Metal.jl, etc.) already defines `adapt_storage` functions for converting different types to / from corresponding device type. Reusing these functions is preferable to defining our own since, not only does it save work, but it allows us to rely on the expertise of the developers who wrote those packages! If there is an issue with types being converted incorrectly that is fixed in one of those packages, then we would not need to update our code to get this fix since we are using the definitions they created.\n\nOur final `gpu` and `cpu` functions are very simple. The `backend` parameter is a type derived from the abstract `Backend` type of [`KernelAbstractions.jl`](https://github.com/JuliaGPU/KernelAbstractions.jl), which is extended by each of the backend packages:\n\n```julia\nimport KernelAbstractions as KA\n\nfunction gpu(x, backend::KA.GPU)\n return fmap(x -> adapt(backend, x), x; exclude=_isleaf)\nend\n\ncpu(x) = fmap(x -> adapt(KA.CPU(), x), x, exclude=_isleaf)\n```\n\nThe other work needed to generalize our GPU support involved switching to use [package extensions](https://pkgdocs.julialang.org/v1/creating-packages/#Conditional-loading-of-code-in-packages-(Extensions)) to avoid having each of the backend packages as an explicit dependency, and defining some basic GPU functions for backend selection and printing information about available GPU devices. The pull request for adding support for multiple backends is linked below:\n\n> https://github.com/JuliaHealth/KomaMRI.jl/pull/405\n\n# Step 2: Buildkite CI\n\nAt the time the above pull request was merged, we weren't sure whether the added support for AMD and Intel GPUs actually worked, since we only had access to CUDA and Apple Silicon GPUs. So the next step was to set up a CI to test each GPU backend. To do this, we used [Buildkite](https://github.com/JuliaGPU/KernelAbstractions.jl), which is a CI platform that many other Julia packages also use. Since there were many examples to follow, setting up our testing pipeline was not too difficult. Each step of the pipeline does the required environment setup and then calls `Pkg.test()` for KomaMRICore. As an example, here is what the AMDGPU step of our pipeline looks like:\n\n```{yml}\n - label: \"AMDGPU: Run tests on v{{matrix.version}}\"\n matrix:\n setup:\n version:\n - \"1\"\n plugins:\n - JuliaCI/julia#v1:\n version: \"{{matrix.version}}\"\n - JuliaCI/julia-coverage#v1:\n codecov: true\n dirs:\n - KomaMRICore/src\n - KomaMRICore/ext\n command: |\n julia -e 'println(\"--- :julia: Instantiating project\")\n using Pkg\n Pkg.develop([\n PackageSpec(path=pwd(), subdir=\"KomaMRIBase\"),\n PackageSpec(path=pwd(), subdir=\"KomaMRICore\"),\n ])'\n \n julia --project=KomaMRICore/test -e 'println(\"--- :julia: Add AMDGPU to test environment\")\n using Pkg\n Pkg.add(\"AMDGPU\")'\n \n julia -e 'println(\"--- :julia: Running tests\")\n using Pkg\n Pkg.test(\"KomaMRICore\"; coverage=true, test_args=[\"AMDGPU\"])'\n agents:\n queue: \"juliagpu\"\n rocm: \"*\"\n timeout_in_minutes: 60\n```\n\nWe also decided that in addition to a testing CI, it would also be helpful to have a benchmarking CI to track performance changes resulting from each commit to the main branch of the repository. [Lux.jl](https://github.com/LuxDL/Lux.jl) had a very nice-looking benchmarking page, so I decided to look into their approach. They were using [github-action-benchmark](https://github.com/benchmark-action/github-action-benchmark), a popular benchmarking action that integrates with the Julia package [`BenchmarkTools.jl`](https://github.com/JuliaCI/BenchmarkTools.jl). github-action-benchmark does two very useful things:\n\n1. Collects benchmarking data into a json file and provides a default index.html to display this data. If put inside a relative path in the gh-pages branch of a repository, this results in a public benchmarking page which is automatically updated after each commit!\n\n2. Comments on a pull request with the benchmarking results compared with before the pull request. Example: https://github.com/JuliaHealth/KomaMRI.jl/pull/442#pullrequestreview-2213921334\n\nThe only issue was that since github-action-benchmark is a github action, it is meant to be run within github by one of the available github runners. While this works for CPU benchmarking, only Buildkite has the CI setup for each of the GPU backends we are using, and Lux.jl's benchmarks page only included CPU benchmarks, not GPU benchmarks (Note: we talked with Avik, the repository owner of Lux.jl, and Lux.jl has since adopted the approach outlined below to display GPU and CPU benchmarks together). I was not able to find any examples of other julia packages using github-action-benchmark for GPU benchmarking.\n\nFortunately, there is a tool someone developed to download results from Buildkite into a github action (https://github.com/EnricoMi/download-buildkite-artifact-action). This repository only had 1 star when I found it, but it does exactly what we needed: it identifies the corresponding Buildkite build for a commit, waits for it to finish, and then downloads the artifacts for the build into the github action it is being run from. With this, we were able to download the Buildkite benchmark results from a final aggregation step into our benchmarking action and upload to github-action-benchmark to publish to either the main data.js file for our benchmarking website, or pull request.\n\nOur final benchmarking page looks like this and is [publicly accessible](https://juliahealth.org/KomaMRI.jl/benchmarks/):\n\n![](./Benchmark_Page.png)\n\nOne neat thing about github-action-benchmark is that the default index.html is extensible, so even though by deault it only shows time, the information for memory usage and number of allocations is also collected into the json file, and can be displayed as well.\n\nA successful CI run on Buildkite Looks like [this](https://buildkite.com/julialang/komamri-dot-jl/builds/925):\n\n![](./CI_Run.png)\n\nThe pull requests for creating the CI testing and benchmarking pipeline, and changing the index.html for our benchmark page are listed below:\n\n1. https://github.com/JuliaHealth/KomaMRI.jl/pull/411\n2. https://github.com/JuliaHealth/KomaMRI.jl/pull/418\n3. https://github.com/JuliaHealth/KomaMRI.jl/pull/421\n\n# Step 3: Optimization\n\nWith support for multiple backends enabled, and a robust CI, the next step was to optimize our simulation code as much as possible. Our original idea was to create a new GPU-optimized simulation method, but before doing this we wanted to look more at the existing code and optimize for the CPU. \n\nThe simulation code is solving a differential equation (the [Bloch equations(https://en.wikipedia.org/wiki/Bloch_equations)]) over time. Most differential equation solvers step through time, updating the current state at each time step, but our previous simulation code, more optimized for the GPU, did a lot of computations across all time points in a simulation block, allocating a matrix of size `Nspins by NΞ”t` each time this was done. Although this is beneficial for the GPU, where there are millions of threads available on which to parallelize these computations, for the CPU it is more important to conserve memory, and the aforementioned approach of stepping through time is preferable.\n\nAfter seeing that this approach did help speed up simulation time on the CPU, but was not faster on the GPU (7x slower for Metal!) we decided to separate our simulation code for the GPU and CPU, dispatching based on the `KernelAbstractions.Backend` type depending on if it is `<:KernelAbstractions.CPU` or `<:KernelAbstractions.GPU`. \n\nOther things we were able to do to speed up CPU computation time:\n\n1. Preallocating each array used inside the core simulation code so it can be re-used from one simulation block to the next.\n\n2. [Skipping an expensive computation](https://github.com/JuliaHealth/KomaMRI.jl/blob/master/KomaMRICore/src/simulation/SimMethods/Bloch/BlochCPU.jl#L90) if the magnetization at that time point is not added to the final signal\n\n3. Ensuring that each statement is fully broadcasted. We were surprised to see the difference between the following examples:\n\n```julia\n#Fast\nBz = x .* seq.Gx' .+ y .* seq.Gy' .+ z .* seq.Gz' .+ p.Ξ”w ./ T(2Ο€ .* Ξ³)\n\n#Slow\nBz = x .* seq.Gx' .+ y .* seq.Gy' .+ z .* seq.Gz' .+ p.Ξ”w / T(2Ο€ * Ξ³)\n```\n\n4. Using the `cis` function for complex exponentiation, which is faster than `exp`\n\nWith these changes, the mean improvement in simulation time aggregating across each of our benchmarks for 1, 2, 4, and 8 CPU threads was ~4.28. For 1 thread, the average improvement in memory usage was 90x!\n\nThe next task was optimizing the simulation code for the GPU. Although our original idea was to put everything into one GPU kernel, we found that the existing broadcasting operations were already very fast, and that custom kernels we wrote were not able to outperform the previous implementation. The Julia GPU compiler team deserves a lot of credit for developing such fast broadcasting implementations!\n\nHowever, this does not mean that we were unable to improve the GPU simulation time. Similar to with the CPU, preallocation made a substantial difference. Parallelizing as much work as possible across the time points for a simulation block was also found to beneficial. For the parts that needed to be done sequentially, a [custom GPU kernel](https://github.com/JuliaHealth/KomaMRI.jl/blob/master/KomaMRICore/src/simulation/SimMethods/Bloch/KernelFunctions.jl#L5) was written which used the `KernelAbstractions.@localmem` macro for arrays being updated at each time step to yield faster memory access.\n\nThe mean speedup we saw across the 4 supported GPU backends was 4.16, although this varied accross each backend (for example, CUDA was only 2.66x faster while oneAPI was 28x faster). There is a [remaining bottleneck](https://github.com/JuliaHealth/KomaMRI.jl/blob/master/KomaMRICore/src/simulation/SimMethods/Bloch/BlochGPU.jl#L151) in the `run_spin_preceession!` function having to do with logical indexing that I was not able to resolve, but could be solved in the future to speed up the GPU simulation time even further!\n\nThe pull requests optimizing code for the CPU and GPU are below:\n\n1. https://github.com/JuliaHealth/KomaMRI.jl/pull/443\n\n2. https://github.com/JuliaHealth/KomaMRI.jl/pull/459\n\n3. https://github.com/JuliaHealth/KomaMRI.jl/pull/462\n\n# 4. Step 4: Distributed Support\n\nThis last step was a stretch goal for exploring how to add distributed support to KomaMRI. MRI simulations can become quite large, so it is useful to be able to distribute work across either multiple GPUs or multiple compute nodes.\n\nA nice thing about MRI simulation is the independent spin property: if a phantom object (representing, for example a brain tissue slice) is divided into two parts, and each part is simulated separately, the signal result from simulating the whole phantom will be equal to the sum of the signal results from simulating each subdivision of the original phantom. This makes it quite easy to distribute work, either across more than one GPU or accross multiple compute nodes.\n\nThe following scripts worked, with the only necessary code change to the repository being a new + function to add two RawAcquisitionData structs:\n\n```julia\n#Use multiple GPUs:\nusing Distributed\nusing CUDA\n\n#Add workers based on the number of available devices\naddprocs(length(devices()))\n\n#Define inputs on each worker process\n@everywhere begin\n using KomaMRI, CUDA\n sys = Scanner()\n seq = PulseDesigner.EPI_example()\n obj = brain_phantom2D()\n #Divide phantom\n parts = kfoldperm(length(obj), nworkers())\nend\n\n#Distribute simulation across workers\nraw = Distributed.@distributed (+) for i=1:nworkers()\n KomaMRICore.set_device!(i-1) #Sets device for this worker, note that CUDA devices are indexed from 0\n simulate(obj[parts[i]], seq, sys)\nend\n```\n\n```julia\n#Use multiple compute nodes\nusing Distributed\nusing ClusterManagers\n\n#Add workers based on the specified number of SLURM tasks\naddprocs(SlurmManager(parse(Int, ENV[\"SLURM_NTASKS\"])))\n\n#Define inputs on each worker process\n@everywhere begin\n using KomaMRI\n sys = Scanner()\n seq = PulseDesigner.EPI_example()\n obj = brain_phantom2D()\n parts = kfoldperm(length(obj), nworkers())\nend\n\n#Distribute simulation across workers\nraw = Distributed.@distributed (+) for i=1:nworkers()\n simulate(obj[parts[i]], seq, sys)\nend\n```\n\nPull reqeust for adding these examples to the KomaMRI documentation: https://github.com/JuliaHealth/KomaMRI.jl/pull/468\n\n# Conclusions / Future Work\n\nThis project was a 350-hour large project, since there were many goals to accomplish. To summarize what changed since the beginning of the project:\n\n1. Added support for AMDGPU.jl, Metal.jl, and oneAPI.jl GPU backends\n\n2. CI for automated testing and benchmarking accross each backend + [public benchmarks page](https://juliahealth.org/KomaMRI.jl/benchmarks/)\n\n3. Significantly faster CPU and GPU performance\n\n4. Demonstrated distributed support and examples added in documentation\n\nFuture work could look at ways to further optimize the simulation code, since despite the progress made, I believe there is more work to be done! The aforementioned logical indexing issue is still not resolved, and the kernel used inside the `run_spin_excitation!` function has not been profiled in depth. KomaMRI is also looking into adding support for higher-order ODE methods, which could require more GPU kernels being written.\n\n# Acknowledgements\n\nI would like to thank my mentor, Carlos Castillo, for his help and support on this project. I would also like to thank Jakub Mitura, who attended some of our meetings to help with GPU optimization, Dilum Aluthge who helped set up our BuildKite pipeline, and Tim Besard, who answered many GPU-related questions that Carlos and I had.\n\n", - "supporting": [ - "Ryan_GSOC_files/figure-html" - ], - "filters": [], - "includes": {} - } -} \ No newline at end of file diff --git a/_freeze/site_libs/clipboard/clipboard.min.js b/_freeze/site_libs/clipboard/clipboard.min.js deleted file mode 100644 index 1103f81..0000000 --- a/_freeze/site_libs/clipboard/clipboard.min.js +++ /dev/null @@ -1,7 +0,0 @@ -/*! - * clipboard.js v2.0.11 - * https://clipboardjs.com/ - * - * Licensed MIT Β© Zeno Rocha - */ -!function(t,e){"object"==typeof exports&&"object"==typeof module?module.exports=e():"function"==typeof define&&define.amd?define([],e):"object"==typeof exports?exports.ClipboardJS=e():t.ClipboardJS=e()}(this,function(){return n={686:function(t,e,n){"use strict";n.d(e,{default:function(){return b}});var e=n(279),i=n.n(e),e=n(370),u=n.n(e),e=n(817),r=n.n(e);function c(t){try{return document.execCommand(t)}catch(t){return}}var a=function(t){t=r()(t);return c("cut"),t};function o(t,e){var n,o,t=(n=t,o="rtl"===document.documentElement.getAttribute("dir"),(t=document.createElement("textarea")).style.fontSize="12pt",t.style.border="0",t.style.padding="0",t.style.margin="0",t.style.position="absolute",t.style[o?"right":"left"]="-9999px",o=window.pageYOffset||document.documentElement.scrollTop,t.style.top="".concat(o,"px"),t.setAttribute("readonly",""),t.value=n,t);return e.container.appendChild(t),e=r()(t),c("copy"),t.remove(),e}var f=function(t){var e=10?setTimeout((function(){e(r,n,s)}),1):(t.update(),n(s))}}},"./src/filter.js":function(t){t.exports=function(t){return t.handlers.filterStart=t.handlers.filterStart||[],t.handlers.filterComplete=t.handlers.filterComplete||[],function(e){if(t.trigger("filterStart"),t.i=1,t.reset.filter(),void 0===e)t.filtered=!1;else{t.filtered=!0;for(var r=t.items,n=0,s=r.length;nv.page,a=new g(t[s],void 0,n),v.items.push(a),r.push(a)}return v.update(),r}m(t.slice(0),e)}},this.show=function(t,e){return this.i=t,this.page=e,v.update(),v},this.remove=function(t,e,r){for(var n=0,s=0,i=v.items.length;s-1&&r.splice(n,1),v},this.trigger=function(t){for(var e=v.handlers[t].length;e--;)v.handlers[t][e](v);return v},this.reset={filter:function(){for(var t=v.items,e=t.length;e--;)t[e].filtered=!1;return v},search:function(){for(var t=v.items,e=t.length;e--;)t[e].found=!1;return v}},this.update=function(){var t=v.items,e=t.length;v.visibleItems=[],v.matchingItems=[],v.templater.clear();for(var r=0;r=v.i&&v.visibleItems.lengthe},innerWindow:function(t,e,r){return t>=e-r&&t<=e+r},dotted:function(t,e,r,n,s,i,a){return this.dottedLeft(t,e,r,n,s,i)||this.dottedRight(t,e,r,n,s,i,a)},dottedLeft:function(t,e,r,n,s,i){return e==r+1&&!this.innerWindow(e,s,i)&&!this.right(e,n)},dottedRight:function(t,e,r,n,s,i,a){return!t.items[a-1].values().dotted&&(e==n&&!this.innerWindow(e,s,i)&&!this.right(e,n))}};return function(e){var n=new i(t.listContainer.id,{listClass:e.paginationClass||"pagination",item:e.item||"
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    +
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    Welcome to the JuliaHealthBlog! πŸ‘‹

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    +
     
    +

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    +Dummy Post +

    +
    + +
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    +Post description +
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    + +
    +Jun 22, 2024 +
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    +Foobar +
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    +1 min +
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    +No matching items +
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    Copyright 2024-25, JuliaHealth.

    +
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    +   +
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    + +
    +
    +
    + + + + + \ No newline at end of file diff --git a/docs/404.html b/docs/blog/posts/dummy/index.html similarity index 64% rename from docs/404.html rename to docs/blog/posts/dummy/index.html index edce31b..bfdaf3c 100644 --- a/docs/404.html +++ b/docs/blog/posts/dummy/index.html @@ -2,12 +2,15 @@ - + + + + -Page Not Found – The JuliaHealth Blog +Dummy Post – JuliaHealth - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + - - - - - - + @@ -114,11 +118,8 @@ @@ -192,30 +168,52 @@
    - +
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    + Post description +
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    Author
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    Foobar

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    Published
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    June 22, 2024

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    @@ -225,14 +223,32 @@ -

    The page you requested cannot be found (perhaps it was moved or renamed).

    -

    You may want to try searching to find the page’s new location.

    - +
    +

    Seciton 1

    +

    Small dummy blog post

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    Section 2

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    Section 3

    - +
    - Back to top + +
    diff --git a/docs/index.html b/docs/index.html index 10ef49c..abd4111 100644 --- a/docs/index.html +++ b/docs/index.html @@ -2,12 +2,12 @@ - + -The JuliaHealth Blog +JuliaHealth + - - - + - + + - + + - - - - - - - - -
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    Welcome to the JuliaHealthBlog! πŸ‘‹

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    -GSoC ’24: Adding dataset-wide functions and integrations of augmentations -

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    -MedPipe3D - Medical segmentation pipeline with dataset-wide functions and augmentations. -
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    -Nov 3, 2024 -
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    -GSoC ’24: Adding functionalities to medical imaging visualizations -

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    -A summary of my project for Google Summer of Code - 2024 -
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    -Nov 1, 2024 -
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    -GSoC Co-Mentoring Experience -

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    -GSoC ’24: Developing Tooling for Observational Health Research in Julia -

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    -GSoC ’24: Enhancements to KomaMRI.jl GPU Support -

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    -GSoC ’24: IPUMS.jl Small Project -

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    +
    + JuliaHealth Logo +

    + Transforming Health Research! +

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    + Improving medicine, health and bio-medical research using the power of Julia. +

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    Explore Packages...

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    Below are some of the powerful packages developed by the community.

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    + + And many more... + +

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    Similar Organizations

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    We are not the only one, there are other communities researching on various similar aspects.

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    FAQs

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    + This is the first item's accordion body. It is shown by default, + until the collapse plugin adds the appropriate classes that we use to style each + element. These classes control the overall appearance, as well as the showing and + hiding via CSS transitions. You can modify any of this with custom CSS or overriding + our default variables. It's also worth noting that just about any HTML can go within + the .accordion-body, though the transition does limit overflow. +
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    diff --git a/docs/index.xml b/docs/index.xml deleted file mode 100644 index 65a0c83..0000000 --- a/docs/index.xml +++ /dev/null @@ -1,5684 +0,0 @@ - - - -The JuliaHealth Blog -https://juliahealth.org/JuliaHealthBlog/ - - -quarto-1.5.47 -Sun, 03 Nov 2024 04:00:00 GMT - - GSoC ’24: Adding dataset-wide functions and integrations of augmentations - Jan Zubik - https://juliahealth.org/JuliaHealthBlog/posts/JZubik-gsoc/GSoC_Jan_Zubik_MedPipe3D.html - -

    πŸ“πŸ©»πŸ“ŽπŸ“‰ ➑️ πŸ—ƒοΈπŸ“šβ™»οΈπŸ§‘β€πŸ« ➑️ πŸ€–πŸ‘οΈπŸ“ˆ ➑️ β€οΈβ€πŸ©Ή

    -

    These emoticons may resemble hieroglyphics, but very soon you will realize that they mean more than 1000s of lines of code.

    -
    - -Description of the emojis used in the title - -
      -
    • -πŸ“ Action Plan: A clear, structured plan that guides each step of the MedPipe3D pipeline. -
      • -
    • -🩻 3D Medical Images: Medical imaging data, such as MRI scans in Nifti format. -
      • -
    • -πŸ“Ž AI Model: The initial AI model that will be trained and refined within the pipeline. -
      • -
    • -πŸ“‰ Loss Function: A function that measures the model’s performance during training, guiding the optimization process. -
      • -
    • -πŸ—ƒοΈ Data Loading: Preparation and loading of data and metadata into HDF5 format. -
      • -
    • -πŸ“š Data Splitting: Dividing data into training, validation, and test sets. -
      • -
    • -♻️ Data Augmentation: Increasing data variability through augmentation. -
      • -
    • -πŸ§‘β€πŸ« AI Training: Using Lux.jl framework to train the AI model. -
      • -
    • -πŸ€– Model: The trained AI model that can perform tasks like segmentation on medical images. -
      • -
    • -πŸ‘οΈ Data for Visualization: Output data, such as masks and segmentations. -
      • -
    • -πŸ“ˆ Performance Logs: Logs and metrics documenting the AI’s performance. -
      • -
    • -β€οΈβ€πŸ©Ή Purpose of MedPipe3D -
      • -
    -
    -
    -

    In this post, I’d like to summarize what I did this summer and everything I learned along the way, rebuilding the MedPipe3D medical imaging pipeline. I will not start typically, but so that anyone even a novice can visualize what this project has achieved, while the latter part is intended for more experienced readers. It will be easiest to divide it into 4 steps separated by ➑️ in the title above. Each emoji stands for a different piece of pipeliner and will be described below.

    -

    πŸ“πŸ©»πŸ“ŽπŸ“‰ What we need from the user

    -

    MedPipe3D requires four essential inputs from the user to get started: a clear action plan πŸ“, 3D medical images like MRI scans 🩻, an AI model πŸ“Ž, and a loss function πŸ“‰.

    -

    πŸ—ƒοΈπŸ“šβ™»οΈπŸ§‘β€πŸ« The Pipeline essential AI manufacturing line

    -

    Following the plan πŸ“, MedPipe3D loads data, pre-processes, and organizes it πŸ—ƒοΈ. Allowing data to be easily split πŸ“š, and efficiently augmented ♻️ in many ways for learning AI πŸ§‘β€πŸ« model effectively. In the end, performing testing and post-processing for better determination of AI skills.
    -It’s designed to transform raw medical data into a format that your AI can learn from, segmenting meaningful patterns and structures.

    -

    πŸ€–πŸ‘οΈπŸ“ˆ Results and Insights

    -

    MedPipe3D is a tool for researchers and for that, it cannot do without analysis, testing, and evaluation. The result of the pipeline is a model πŸ€– as well as data πŸ‘οΈ and logs πŸ“ˆ needed in MedEval3D that are ready for visualization and further analysis with MedEye3D. In a nutshell, it makes visualizing results easy, tumor locations or other medical features directly as masks on the scans.

    -

    β€οΈβ€πŸ©Ή Purpose-Driven Technology

    -

    MedPipe3D’s mission goes beyond technology. It’s about providing the tools to create AIs that support healthcare professionals in making faster, more accurate decisions, with the ultimate goal of saving lives.

    -

    This four-part journey captures the heart of the MedPipe3D toolkit for advancing medical AI, from raw data to life-saving insight.

    -
    -

    Introduction

    -

    MedPipe3D is a framework created from hundreds of hours over summer vacation, thousands of lines of code, hundreds of mistakes, and most importantly the guidance of my mentor and author of all of these libraries Dr. Jakub Mitura. At its core, MedPipe3D combines sophisticated data handling from MedImage thanks to the hard work of Divyansh Goyal. Newly developed pipeline for model training, validation, and testing with existing MedEval3D, and result visualization with MedEye3D. Unfortunately, not all of the project’s goals have been fully achieved, and thereby there is one section ➑️ too many. Hopefully not for long. My name is Jan Zubik, and I wrote this entire library from scratch, which is currently my most complex project.

    -

    If you are a data scientist, programmer, or code enthusiast, I invite you to read the next section where I go into detail and present version 1 of this tool in detail.

    -

    I’m a 3rd-year student of BSc in Data Science and Machine Learning, I know that many things can be done better, expanded, debugged, and optimized. Now it just works, but don’t hesitate to write to me personally on LinkedIn, Julia’s Slack or GitHub! With your comments, and direct critique you will help me to be a better programmer and one day MedPipe3D will contribute in a tiny way to save someone’s life!

    -

    Exact work from the Google Summer of Code project you will find in GitHub the repository.

    -
    - -
    -

    Project Goals

    -

    The primary goal was to develop MedPipe3D and enhance MedImage, a Julia package designed to streamline the process of GPU-accelerated medical image segmentation. The project aimed to merge existing librariesβ€”MedEye3D, MedEval3D, and MedImageβ€”into a cohesive pipeline that facilitates advanced data handling, preprocessing, augmentation, model training, validation, testing with post-processing and visualization for medical imaging applications.

    -
    -
    -

    Tasks

    -
      -
    • πŸ†™ - Fully finished, with great potential for further development
      • -
    • βœ… - Fully completed
      • -
    • ⚠️ - Partially uncompleted
      • -
    • ❌ - Unreached
      • -
    -Full list of all major parts and minor tasks (all tasks set up in the original GSOC plan were completed at least minimum level, and many additional improvements above minimum were implemented) -
    -
      -
    1. Helpful functions to support the MedImage format βœ…
      2. -
    -
      -
    • Debugging rotations βœ…
      • -
    • Crop MedImage or 3D array βœ…
      • -
    • Pad MedImage or 3D array βœ…
      • -
    • Pad with edge values βœ…
      • -
    • Calculating the average of the edges of the picture πŸ†™
      • -
    -
      -
    1. Integrate Augmentations for Medical Data βœ…
      2. -
    -
      -
    • Brightness transform βœ…
      • -
    • Contrast augmentation transform βœ…
      • -
    • Gamma Transform βœ…
      • -
    • Gaussian noise transform βœ…
      • -
    • Rician noise transform βœ…
      • -
    • Mirror transform βœ…
      • -
    • Scale transform πŸ†™
      • -
    • Gaussian blur transform βœ…
      • -
    • Simulate low-resolution transform πŸ†™
      • -
    • Elastic deformation transform πŸ†™
      • -
    -
      -
    1. Develop a Pipeline ⚠️
      2. -
    -
      -
    • Structured configuration of all hyperparameters πŸ†™
      • -
    • Interactive creation of configuration βœ…
      • -
    • Creating a structured configuration of hyperparameters in JSON πŸ†™
      • -
    • Loading data into HDF5 βœ… -
        -
      • Cropping and padding to real coordinates of the main picture βœ…
        • -
      • Calculate Median and Mean Spacing with resampling πŸ†™
        • -
      • Cropping and padding to specific or average dimensions βœ…
        • -
      • Standardization and normalization βœ…
        • -
      • -
    • Managing index groups (channels) for batch requirements in HDF5 βœ… -
        -
      • Divide into train, validation, test specified as % βœ…
        • -
      • Divide with a specific division specified in JSON βœ…
        • -
      • Equal distribution when there are multiple classes βœ…
        • -
      • -
    • Extracting data and creating 5-dimensional tensors for batched learning βœ… -
        -
      • Hole images data loading βœ…
        • -
      • Patch-based data loading with probabilistic oversampling βœ…
        • -
      • -
    • Obtaining the necessary elements for learning βœ… -
        -
      • Get optimizer, loss function, and performance metrics βœ…
        • -
      • -
    • Apply augmentations βœ…
      • -
    • Train βœ… -
        -
      • Initializing model βœ…
        • -
      • The learning epoch βœ…
        • -
      • Epoch with early stopping functionality βœ…
        • -
      • -
    • Inferring βœ…
      • -
    • Validation βœ… -
        -
      • Evaluate metric βœ…
        • -
      • Evaluate validation loss βœ…
        • -
      • Validation with largest connected componentβœ…
        • -
      • -
    • Testing βœ… -
        -
      • Evaluate test set βœ…
        • -
      • Invertible augmentations evaluation βœ…
        • -
      • Patch-based invertible augmentations evaluation βœ…
        • -
      • -
    • Logging ⚠️ -
        -
      • Returning the necessary results ⚠️
        • -
      • Logging connection to TensorBoard ❌
        • -
      • Logging errors and warnings ❌
        • -
      • -
    • Visualization ⚠️ -
        -
      • Returning data in Nifti format βœ…
        • -
      • Automated visualization in MedEye3D ❌
        • -
      • -
    -
      -
    1. Optimize Performance with GPU Acceleration -
        -
      • Augmentations βœ…
        • -
      • Learning, Validation, Testing βœ…
        • -
      • Largest connected component βœ…
        • -
      2. -
    2. Documentation ⚠️ -
        -
      • Comments in important places in the code ⚠️
        • -
      • Documentation of the function ⚠️
        • -
      • Read me ⚠️
        • -
      • Documentation on juliahealth.org ❌
        • -
      3. -
    -
    -
    -

    Integrate augmentations for medical data πŸ†™

    -

    Augmenting medical data is a crucial step for enhancing model robustness, especially given the variations in imaging conditions and patient anatomy.

    -
      -
    • This pipeline currently supports multiple augmentation techniques: -
        -
      • Brightness transform βœ…
        • -
      • Contrast augmentation transform βœ…
        • -
      • Gamma Transform βœ…
        • -
      • Gaussian noise transform βœ…
        • -
      • Rician noise transform βœ…
        • -
      • Mirror transform βœ…
        • -
      • Scale transform πŸ†™
        • -
      • Gaussian blur transform βœ…
        • -
      • Simulate low-resolution transform πŸ†™
        • -
      • Elastic deformation transform πŸ†™
        • -
      • -
    -

    Which have been fully integrated. Each of these methods helps the model generalize better by simulating diverse imaging scenarios.

    -

    -

    Comments:

    -

    Augmentations such as scaling, and low-resolution simulation use interpolation that is not yet GPU-accelerated.

    -

    Elastic deformation with simulation of different tissue elasticities is a potential development opportunity that would further improve the model’s adaptability by mimicking more complex variations found in medical imaging.

    -
    -
    -

    Invertible augmentations and support test time augmentations πŸ†™

    -

    This section focuses on the ability to apply reversible augmentations to test data, allowing the model to be evaluated with different transformations. Only rotation is available at this time. The function evaluate_patches performs this evaluation by applying specified augmentations, dividing the test data into patches, and reconstructing the full image from the patches. During testing, one can choose to use of largest connected component post-processing. Metrics are calculated and results are saved for analysis.

    -
    - -evaluate_test: - -
    # ...
-for test_group in test_groups
-    test_data, test_label, attributes = fetch_and_preprocess_data([test_group], h5, config)
-    results, test_metrics = evaluate_patches(test_data, test_label,  tstate, model, config)
-    y_pred, metr = process_results(results, test_metrics, config)
-    save_results(y_pred, attributes, config)
-    push!(all_test_metrics, metr)
-end
-# ...
    -
    function evaluate_patches(test_data, test_label, tstate, model, config, axis, angle)
-    println("Evaluating patches...")
-    results = []
-    test_metrics = []
-    tstates = [tstate]
-    test_time_augs = []
-
-    for i in config["learning"]["n_invertible"]
-        data = rotate_mi(test_data, axis, angle)
-        for tstate_curr in tstates
-            patch_results = []
-            patch_size = Tuple(config["learning"]["patch_size"])
-            idx_and_patches, paded_data_size = divide_into_patches(test_data, patch_size)
-            coordinates = [patch[1] for patch in idx_and_patches]
-            patch_data = [patch[2] for patch in idx_and_patches]
-            for patch in patch_data
-                y_pred_patch, _ = infer_model(tstate_curr, model, patch)
-                push!(patch_results, y_pred_patch)
-            end
-            idx_and_y_pred_patch = zip(coordinates, patch_results)
-            y_pred = recreate_image_from_patches(idx_and_y_pred_patch, paded_data_size, patch_size, size(test_data))
-            if config["learning"]["largest_connected_component"]
-                y_pred = largest_connected_component(y_pred, config["learning"]["n_lcc"])
-            end
-            metr = evaluate_metric(y_pred, test_label, config["learning"]["metric"])
-            push!(test_metrics, metr)
-        end
-    end
-    return results, test_metrics
-end
    -
    function divide_into_patches(image::AbstractArray{T, 5}, patch_size::Tuple{Int, Int, Int}) where T
-    println("Dividing image into patches...")
-    println("Size of the image: ", size(image)) 
-
-    # Calculate the required padding for each dimension (W, H, D)
-    pad_size = (
-        (size(image, 1) % patch_size[1]) != 0 ? patch_size[1] - size(image, 1) % patch_size[1] : 0,
-        (size(image, 2) % patch_size[2]) != 0 ? patch_size[2] - size(image, 2) % patch_size[2] : 0,
-        (size(image, 3) % patch_size[3]) != 0 ? patch_size[3] - size(image, 3) % patch_size[3] : 0
-    )
-
-    # Pad the image if necessary
-    padded_image = image
-    if any(pad_size .> 0)
-        padded_image = crop_or_pad(image, (size(image, 1) + pad_size[1], size(image, 2) + pad_size[2], size(image, 3) + pad_size[3]))
-    end
-
-    # Extract patches
-    patches = []
-    for x in 1:patch_size[1]:size(padded_image, 1)
-        for y in 1:patch_size[2]:size(padded_image, 2)
-            for z in 1:patch_size[3]:size(padded_image, 3)
-                patch = view(
-                    padded_image,
-                    x:min(x+patch_size[1]-1, size(padded_image, 1)),
-                    y:min(y+patch_size[2]-1, size(padded_image, 2)),
-                    z:min(z+patch_size[3]-1, size(padded_image, 3)),
-                    :,
-                    :
-                )
-                push!(patches, [(x, y, z), patch])
-            end
-        end
-    end
-    println("Size of padded image: ", size(padded_image))
-    return patches, size(padded_image)
-end
-
-function recreate_image_from_patches(
-    coords_with_patches,
-    padded_size,
-    patch_size,
-    original_size
-)
-    println("Recreating image from patches...")
-    reconstructed_image = zeros(Float32, padded_size...)
-    
-    # Place patches back into their original positions
-    for (coords, patch) in coords_with_patches
-        x, y, z = coords
-        reconstructed_image[
-            x:x+patch_size[1]-1,
-            y:y+patch_size[2]-1,
-            z:z+patch_size[3]-1,
-            :,
-            :
-        ] = patch
-    end
-
-    # Crop the reconstructed image to remove any padding
-    final_image = reconstructed_image[
-        1:original_size[1],
-        1:original_size[2],
-        1:original_size[3],
-        :,
-        :
-    ]
-    println("Size of the final image: ", size(final_image))
-    return final_image
-end
    -
    -

    Comment:
    In this section, there is significant potential to incorporate additional types of invertible augmentations.

    -
    -
    -

    Patch-based data loading with probabilistic oversampling βœ…

    -

    In this section, patches are extracted using extract_patch from the medical images for model training, with a probability-based method to decide between a random patch or a patch with non-zero labels. Helper functions like get_random_patch and get_centered_patch determine the starting indices and dimensions for the patches based on given configurations, while padding methods ensure consistency even if the patch exceeds the original image dimensions. Probabilistic oversampling, as configured, allows for more balanced and informative data sampling, which improves the model’s ability to detect specific medical features.

    -
    - -extract_patch: - -
    function extract_patch(image, label, patch_size, config)
-    # Fetch the oversampling probability from the config
-    println("Extracting patch.")
-    oversampling_probability = config["learning"]["oversampling_probability"]
-    # Generate a random number to decide which patch extraction method to use
-    random_choice = rand()
-
-    if random_choice <= oversampling_probability
-        return extract_nonzero_patch(image, label, patch_size)
-    else
-
-        return get_random_patch(image, label, patch_size)
-    end
-end
-#Helper function, in case the mask is emptyClick to apply
-function extract_nonzero_patch(image, label, patch_size)
-    println("Extracting a patch centered around a non-zero label value.")
-    indices = findall(x -> x != 0, label)
-    if isempty(indices)
-        # Fallback to random patch if no non-zero points are found
-        return get_random_patch(image, label, patch_size)
-    else
-        # Choose a random non-zero index to center the patch around
-        center = indices[rand(1:length(indices))]
-        return get_centered_patch(image, label, center, patch_size)
-    end
-end
-# Function to get a patch centered around a specific index
-function get_centered_patch(image, label, center, patch_size)
-    center_coords = Tuple(center)
-    half_patch = patch_size .Γ· 2
-    start_indices = center_coords .- half_patch
-    end_indices = start_indices .+ patch_size .- 1
-
-    # Calculate padding needed
-    pad_beg = (
-        max(1 - start_indices[1], 0),
-        max(1 - start_indices[2], 0),
-        max(1 - start_indices[3], 0)
-    )
-    pad_end = (
-        max(end_indices[1] - size(image, 1), 0),
-        max(end_indices[2] - size(image, 2), 0),
-        max(end_indices[3] - size(image, 3), 0)
-    )
-
-    # Adjust start_indices and end_indices after padding
-    start_indices_adj = start_indices .+ pad_beg
-    end_indices_adj = end_indices .+ pad_beg
-
-    # Convert padding values to integers
-    pad_beg = Tuple(round.(Int, pad_beg))
-    pad_end = Tuple(round.(Int, pad_end))
-
-    # Pad the image and label using pad_mi
-    image_padded = pad_mi(image, pad_beg, pad_end, 0)
-    label_padded = pad_mi(label, pad_beg, pad_end, 0)
-
-    # Extract the patch
-    image_patch = image_padded[
-        start_indices_adj[1]:end_indices_adj[1],
-        start_indices_adj[2]:end_indices_adj[2],
-        start_indices_adj[3]:end_indices_adj[3]
-    ]
-    label_patch = label_padded[
-        start_indices_adj[1]:end_indices_adj[1],
-        start_indices_adj[2]:end_indices_adj[2],
-        start_indices_adj[3]:end_indices_adj[3]
-    ]
-
-    return image_patch, label_patch
-end
-
-function get_random_patch(image, label, patch_size)
-    println("Extracting a random patch.")
-    # Check if the patch size is greater than the image dimensions
-    if any(patch_size .> size(image))
-        # Calculate the needed size to fit the patch
-        needed_size = map(max, size(image), patch_size)
-        # Use crop_or_pad to ensure the image and label are at least as large as needed_size
-        image = crop_or_pad(image, needed_size)
-        label = crop_or_pad(label, needed_size)
-    end
-
-    # Calculate random start indices within the new allowable range
-    start_x = rand(1:size(image, 1) - patch_size[1] + 1)
-    start_y = rand(1:size(image, 2) - patch_size[2] + 1)
-    start_z = rand(1:size(image, 3) - patch_size[3] + 1)
-    start_indices = [start_x, start_y, start_z]
-    end_indices = start_indices .+ patch_size .- 1
-
-    # Extract the patch directly when within bounds
-    image_patch = image[start_indices[1]:end_indices[1], start_indices[2]:end_indices[2], start_indices[3]:end_indices[3]]
-    label_patch = label[start_indices[1]:end_indices[1], start_indices[2]:end_indices[2], start_indices[3]:end_indices[3]]
-
-    return image_patch, label_patch
-end
    -
    -
    -
    -

    Calculate Median and Mean Spacing with resampling πŸ†™

    -

    This part ensures that all images in the dataset have consistent real coordinates, spacing, and shape. It’s a critical factor in medical imaging for accurate analysis. Calculating and applying set values, median or mean across images ensures uniformity.

    -
    -

    Resample images to target image πŸ†™

    -

    This step aligns each image to the reference coordinates of the main image, ensuring that all images share a common spatial alignment. The resample_to_image function from MedImage.jl is used here, applying interpolation to adjust each image.

    -
    - -resample_images_to_target: - -
    if resample_images_to_target && !isempty(Med_images)
-    println("Resampling $channel_type files in channel '$channel_folder' to the first $channel_type in the channel.")
-    reference_image = Med_images[1]
-    Med_images = [resample_to_image(reference_image, img, interpolator) for img in Med_images]
-end
    -
    -

    Comment:
    Resample_to_image uses interpolation that is not yet GPU-accelerated in this implementation, this step slows down the data preparation phase significantly.

    -
    -
    -

    Ensure uniform spacing across the entire dataset πŸ†™

    -

    This step brings all images to a consistent voxel spacing across the dataset using resample_to_spacing from MedImage.jl. This uniform spacing is crucial for creating a standardized dataset where each image voxel represents the same physical volume.

    -
    - -esample_to_spacing: - -
    if resample_images_spacing == "set"
-    println("Resampling all $channel_type files to target spacing: $target_spacing")
-    target_spacing = Tuple(Float32(s) for s in target_spacing)
-    channels_data = [[resample_to_spacing(img, target_spacing, interpolator) for img in channel] for channel in channels_data]
-elseif resample_images_spacing == "avg"
-    println("Calculating average spacing across all $channel_type files and resampling.")
-    all_spacings = [img.spacing for channel in channels_data for img in channel]
-    avg_spacing = Tuple(Float32(mean(s)) for s in zip(all_spacings...))
-    println("Average spacing calculated: $avg_spacing")
-    channels_data = [[resample_to_spacing(img, avg_spacing, interpolator) for img in channel] for channel in channels_data]
-elseif resample_images_spacing == "median"
-    println("Calculating median spacing across all $channel_type files and resampling.")
-    all_spacings = [img.spacing for channel in channels_data for img in channel]
-    median_spacing = Tuple(Float32(median(s)) for s in all_spacings)
-    println("Median spacing calculated: $median_spacing")
-    channels_data = [[resample_to_spacing(img, median_spacing, interpolator) for img in channel] for channel in channels_data]
-elseif resample_images_spacing == false
-    println("Skipping resampling of $channel_type files.")
-    # No resampling will be applied, channels_data remains unchanged.
-end
    -
    -

    Comment:
    Resample_to_spacing uses interpolation that is not yet GPU-accelerated in this implementation, this step slows down the data preparation phase significantly.

    -
    -
    -

    Resizing all channel files to average or target size βœ…

    -

    To create a cohesive 5D tensor, all images in each channel are resized to a uniform shape, either the average size of all images or a specific target size. This resizing process uses crop_or_pad, ensuring that all images match the specified dimensions, making them suitable for model input.

    -
    - -crop_or_pad: - -
    if resample_size == "avg"
-    sizes = [size(img.voxel_data) for img in channels_data for img in img]  # Get sizes from all images
-    avg_dim = map(mean, zip(sizes...))
-    avg_dim = Tuple(Int(round(d)) for d in avg_dim)
-    println("Resizing all $channel_type files to average dimension: $avg_dim")
-    channels_data = [[crop_or_pad(img, avg_dim) for img in channel] for channel in channels_data]
-elseif resample_size != "avg"
-    target_dim = Tuple(resample_size)
-    println("Resizing all $channel_type files to target dimension: $target_dim")
-    channels_data = [[crop_or_pad(img, target_dim) for img in channel] for channel in channels_data]
-end
    -
    -
    -
    -
    -

    Basic Post-processing operations

    -

    Post-processing operations involve the algorithm largest_connected_components. It is achieved by label initialization and propagation in the segmented mask. The initialize_labels_kernel function assigns unique labels to different regions.

    -
    - -initialize_labels_kernel: - -
    @kernel function initialize_labels_kernel(mask, labels, width, height, depth)
-    idx = @index(Global, Cartesian)
-    i = idx[1]
-    j = idx[2]
-    k = idx[3]
-    
-    if i >= 1 && i <= width && j >= 1 && j <= height && k >= 1 && k <= depth
-        if mask[i, j, k] == 1
-            labels[i, j, k] = i + (j - 1) * width + (k - 1) * width * height
-        else
-            labels[i, j, k] = 0
-        end
-    end
-end
    -
    -Propagate_labels_kernel iteratively updates the labels to maintain connected regions. propagate_labels_kernel: -
    -
    @kernel function propagate_labels_kernel(mask, labels, width, height, depth)
-    idx= @index(Global, Cartesian)
-    i = idx[1]
-    j = idx[2]
-    k = idx[3]
-
-    if i >= 1 && i <= width && j >= 1 && j <= height && k >= 1 && k <= depth
-        if mask[i, j, k] == 1
-            current_label = labels[i, j, k]
-            for di in -1:1
-                for dj in -1:1
-                    for dk in -1:1
-                        if di == 0 && dj == 0 && dk == 0
-                            continue
-                        end
-                        ni = i + di
-                        nj = j + dj
-                        nk = k + dk
-                        if ni >= 1 && ni <= width && nj >= 1 && nj <= height && nk >= 1 && nk <= depth
-                            if mask[ni, nj, nk] == 1 && labels[ni, nj, nk] < current_label
-                                labels[i, j, k] = labels[ni, nj, nk]
-                            end
-                        end
-                    end
-                end
-            end
-        end
-    end
-end
    -
    -

    This process facilitates the identification of the largest connected components in 3D space, helping to isolate relevant medical structures, such as tumors, in the segmented mask. Allowing determining how many such areas are to be returned.

    -
    - -largest_connected_components: - -
    function largest_connected_components(mask::Array{Int32, 3}, n_lcc::Int)
-    width, height, depth = size(mask)
-    mask_gpu = CuArray(mask)
-    labels_gpu = CUDA.fill(0, size(mask))
-    dev = get_backend(labels_gpu)
-    ndrange = (width, height, depth)
-    workgroupsize = (3, 3, 3)
-
-    # Initialize labels
-    initialize_labels_kernel(dev)(mask_gpu, labels_gpu, width, height, depth, ndrange = ndrange)
-    CUDA.synchronize()
-
-    # Propagate labels iteratively
-    for _ in 1:10 
-        propagate_labels_kernel(dev, workgroupsize)(mask_gpu, labels_gpu, width, height, depth, ndrange = ndrange)
-        CUDA.synchronize()
-    end
-
-    # Download labels back to CPU
-    labels_cpu = Array(labels_gpu)
-    
-    # Find all unique labels and their sizes
-    unique_labels = unique(labels_cpu)
-    label_sizes = [(label, count(labels_cpu .== label)) for label in unique_labels if label != 0]
-
-    # Sort labels by size and get the top n_lcc
-    sort!(label_sizes, by = x -> x[2], rev = true)
-    top_labels = label_sizes[1:min(n_lcc, length(label_sizes))]
-
-    # Create a mask for each of the top n_lcc components
-    components = [labels_cpu .== label[1] for label in top_labels]
-    return components
-end
    -
    -
    -
    -

    Structured configuration of all hyperparameters πŸ†™

    -

    Hyperparameters for the entire pipeline are stored in a JSON configuration file, enabling straightforward adjustments for experimentation (just swap values, save and resume the study). This structured setup allows easy modification of key parameters, such as data set preparation, training settings, data augmentation, and resampling options.

    -
    - -Example configuration: - -
    {
-    "model": {
-        "patience": 10,
-        "early_stopping_metric": "val_loss",
-        "optimizer_name": "Adam",
-        "loss_function_name": "l1",
-        "early_stopping": true,
-        "early_stopping_min_delta": 0.01,
-        "optimizer_args": "lr=0.001",
-        "num_epochs": 10
-    },
-    "data": {
-        "batch_complete": false,
-        "resample_size": [200,101,49],
-        "resample_to_target": false,
-        "resample_to_spacing": false,
-        "batch_size": 3,
-        "standardization": false,
-        "target_spacing": null,
-        "channel_size": 1,
-        "normalization": false,
-        "has_mask": true
-    },
-    "augmentation": {
-        "augmentations": {
-            "Brightness transform": {
-                "mode": "additive",
-                "value": 0.2
-            }
-        },
-        "p_rand": 0.5,
-        "processing_unit": "GPU",
-        "order": [
-            "Brightness transform"
-        ]
-    },
-    "learning": {
-        "Train_Val_Test_JSON": false,
-        "largest_connected_component": false,
-        "n_lcc": 1,
-        "n_folds": 3,
-        "invertible_augmentations": false,
-        "n_invertible": true,
-        
-        "class_JSON_path": false,
-        "additional_JSON_path": false,
-        "patch_size": [50,50,50],
-        "metric": "dice",
-        "n_cross_val": false,
-        "patch_probabilistic_oversampling": false,
-        "oversampling_probability": 1.0,
-        "test_train_validation": [
-            0.6,
-            0.2,
-            0.2
-        ],
-        "shuffle": false
-    }
-}
    -
    -

    Comments:
    The current configuration is loaded as a dictionary, which simplifies access and modification. This setup presents a strong foundation for integrating automated search algorithms for hyperparameter tuning, enabling more efficient model optimization.
    The configuration structure could be reorganized and re-named to improve readability, making it easier for users to locate and adjust specific parameters.

    -
    -
    -

    Visualization of algorithm outputs ⚠️

    -

    This module provides basic visualization functionality by saving output masks and images first to MedImage format and then to Nifti format. The create_nii_from_medimage function from MedImage.jl generates Nifti files, which can be loaded into MedEye3D for 3D visualization.

    -

    Comments:
    Integrating this visualization module more fully with the pipeline could eliminate unnecessary steps. By automatically loading output masks and images as raw data into MedEye3D for 3D visualization and supporting a more efficient end-to-end workflow.

    -
    -
    -

    K-fold cross-validation functionality βœ…

    -

    K-fold cross-validation is implemented to evaluate model performance more robustly. The data is split into multiple folds, with each fold serving as a validation set once, while the others form the training set. This functionality provides a better assessment of model performance across different subsets of the data.

    -
    - -K-fold cross-validation functionality: - -
    ...
-  tstate = initialize_train_state(rng, model, optimizer)
-  if config["learning"]["n_cross_val"]
-      n_folds = config["learning"]["n_folds"]
-      all_tstate = []
-      combined_indices = [indices_dict["train"]; indices_dict["validation"]]
-      shuffled_indices = shuffle(rng, combined_indices)
-      for fold in 1:n_folds
-          println("Starting fold $fold/$n_folds")
-          train_groups, validation_groups = k_fold_split(combined_indices, n_folds, fold, rng)
-          
-          tstate = initialize_train_state(rng, model, optimizer)
-          final_tstate = epoch_loop(num_epochs, train_groups, validation_groups, h5, model, tstate, config, loss_function, num_classes)
-          
-          push!(all_tstate, final_tstate)
-      end
-  else
-      final_tstate = epoch_loop(num_epochs, train_groups, validation_groups, h5, model, tstate, config, loss_function, num_classes)
-  end
-  return final_tstate
-...
    -
    -

    The k_fold_split function organizes the indices for each fold, ensuring comprehensive coverage of the dataset during training.

    -
    - -k_fold_split - -
    function k_fold_split(data, n_folds, current_fold)
-    fold_size = length(data) Γ· n_folds
-    validation_start = (current_fold - 1) * fold_size + 1
-    validation_end = validation_start + fold_size - 1
-    validation_indices = data[validation_start:validation_end]
-    train_indices = [data[1:validation_start-1]; data[validation_end+1:end]]
-    return train_indices, validation_indices
-end
    -
    -
    -
    -
    -

    Conclusions and Future Development

    -

    I have successfully established a foundation for a medical imaging pipeline, addressing significant challenges in data handling, model training, and augmentation integration. The integration of dataset-wide functions has significantly enhanced the reproducibility and handling of batched data with GPU support enabling scalability of experiments, making it easier for researchers and practitioners to produce better results.

    -
    -
    -

    Future Development

    -

    As we look to the future, there are several areas where MedPipe3D can be expanded and improved to better serve the medical AI community. These include:

    -
    -

    Necessary Enhancements

    -

    Comprehensive Logging: Develop detailed logging mechanisms that capture a wide range of events, including system statuses, model performance metrics, and user activities, to facilitate debugging and system optimization. This is currently executed as a simple println function.

    -

    TensorBoard Integration: Implement an interface for TensorBoard to allow users to visualize training dynamics in real time, providing insights into model behavior and performance trends.

    -

    Error and Warning Logs: Introduce advanced error and warning logging capabilities to alert users of potential issues before they affect the pipeline’s performance, ensuring smoother operations and maintenance.

    -

    Automated Visualization: Integrate MedEye3D directly into MedPipe3D to enable automated visualization of outputs, such as segmentation masks or other relevant medical imaging features. This feature would provide users with real-time visual feedback on model performance and data quality. Code-Level Documentation: Due to needed changes in the fundamental structure of the pipeline in the final phase of the project, it is necessary to reevaluate all documentation.

    -

    Official JuliaHealth Documentation: Extend the documentation efforts to include official entries on juliahealth.org, providing a centralized and authoritative resource for users seeking to learn more about MedPipe3D and its capabilities with examples shown

    -
    -
    -

    Potential Enhancements

    -

    GPU support for interpolation will allow for significant acceleration of such functions as Scale transform, Simulate, Low-resolution transform, Elastic deformation transform, and Resampling spacing.

    -

    Add more reversible augmentations to test time.

    -

    Calculating the average of the edges of the picture: checking the type of photo and calculating more correctly on this basis

    -

    Elastic deformation transforms with the simulation of different tissue elasticities.

    -
    -
    -
    -

    Acknowledgments πŸ™‡β€β™‚οΈ

    -

    I would like to express my deepest gratitude to my mentor Dr. Jakub Mitura for his invaluable guidance and support throughout this project. His expertise and encouragement were instrumental in overcoming challenges and achieving project milestones.

    - - - - -
    - - Back to top

    Citation

    BibTeX citation:
    @online{zubik2024,
-  author = {Zubik, Jan},
-  title = {GSoC ’24: {Adding} Dataset-Wide Functions and Integrations of
-    Augmentations},
-  date = {2024-11-03},
-  url = {https://juliahealth.org/JuliaHealthBlog/posts/JZubik-gsoc/GSoC_Jan_Zubik_MedPipe3D.html},
-  langid = {en}
-}
-
    For attribution, please cite this work as:
    -Zubik, Jan. 2024. β€œGSoC ’24: Adding Dataset-Wide Functions and -Integrations of Augmentations.” November 3, 2024. https://juliahealth.org/JuliaHealthBlog/posts/JZubik-gsoc/GSoC_Jan_Zubik_MedPipe3D.html. -
    ]]>     - gsoc - AI/ML - imaging - gpu - analysis - https://juliahealth.org/JuliaHealthBlog/posts/JZubik-gsoc/GSoC_Jan_Zubik_MedPipe3D.html - Sun, 03 Nov 2024 04:00:00 GMT -     - - GSoC ’24: Adding functionalities to medical imaging visualizations - Divyansh Goyal - https://juliahealth.org/JuliaHealthBlog/posts/divyansh-gsoc/gsoc-2024-fellows.html - -

    Hello Everyone! πŸ‘‹

    -

    I am Divyansh, an undergraduate student from Guru Gobind Singh Indraprastha university, majoring in Artificial Intelligence and Machine Learning. Stumbling upon projects under the Juliahealth sub-ecosystem of medical imaging packages, the intricacies of imaging modalities and file formats, reflected in their relevant project counterparts, captured my interest. Working with standards such as NIfTI (Neuroimaging Informatics Technology Initiative) and DICOM (Digital Imaging and Communications in Medicine) with MedImages.jl, I became interested in the visualization routines of such imaging datasets and their integration within the segmentation pipelines for modern medical-imaging analysis.

    -

    In this post, I’d like to summarize what I did this summer and everything I learned along the way, contributing to MedEye3d.jl medical imaging visualizer under GSOC-2024!

    -
    -

    If you want to learn more about me, you can connect with me on LinkedIn and follow me on GitHub

    -
    - -
    -

    Background

    -
    -

    What is MedEye3d.jl?

    -

    MedEye3D.jl is a package under the Julia language ecosystem designed to facilitate the visualization and annotation of medical images. Tailored specifically for medical applications, it offers a range of functionalities to enhance the interpretation and analysis of medical images. MedEye3D aims to provide an essential tool for 3D medical imaging workflow within Julia. The underlying combination of Rocket.jl and ModernGL.jl ensures the high-performance robust visualizations that the package has to offer.

    -

    MedEye3d.jl is open-source and comes with an intuitive user interface (To learn more about MedEye3d, you can read the paper introducing it here [1]).

    -
    -
    -

    What features does this project encompass?

    -

    This project covers implementation of several tasks that will enable the establishment of additional important functionalities within the MedEye3D package, facilitating enhancements within the visualization’s windowing for MRI and PET data, support for super voxels (sv), improved load times, high-level functionality implementation and robust viewing for multiple images.

    -
    -
    -
    -

    Project Goals

    -

    The goals outlined by Dr. Jakub Mitura (my project mentor) and I, beginning of this summer were:

    -
      -

    1. Migration of package reliance from Rocket.jl to base Julia channel and macros: The first decision that was made was to fix the issue of screen tearing and flicker, resulting from the Rocket.jl’s actor-subscription mechanism present at the core of MedEye3d.jl’s event-driven programming. Here, Julia’s threadsafe and asynchronous channels provided a way to introduce reactive programming and state management within MedEye3d without the tradeoffs resulting from external packages such as Rocket

      2. -

    2. Implementation of high level functions with simplified basic usage: Prior to this, MedEye3d involved initialization of data, texture specifications and text display for a final visualization. To reduce complexity, methods to abstract such chores were devised and implemented which resulted in the exposure of functions for loading images, accessing display data and modification of display data. This also encompassed the loading of images via MedImages.jl which required prior work for the integration of C++ ITK backend for image I/O.

      3. -

    3. Improved precompilation with decreased outputs to reduce start time

      4. -

    4. Automatic windowing for most common MRI and PET modalities: This task is a step in the direction of maintaining consistent visualizations across MRI and PET’s most common modalities, to mimic images similar to what is displayed within 3dSlicer for the same.

      5. -

    5. Adding support for multi-image viewing with crosshair marker for image registration

      6. -

    6. Adding support for the display of SuperVoxels sv with borders within the image slices to better understand anatomical regions within slices: Supervoxels, described either through indicator masks or meshes, encapsulate regions of interest with distinct image characteristics.

      7. -
    -

    Additionally, we had a few stretch goals which are going to be a work in progress:

    -
      -

    1. Visualization of structures by 3D rendering using OpenGL,

      2. -

    2. Support for MedVoxelHD visualization by voxel-based Hausdorff distance computation.

      3. -

    3. Support for OSX users

      4. -
    -
    -
    -

    Tasks

    -
    -

    1. Migration of package from Rocket to Julia’s Base.Channel

    -

    Initially, there was significant screen-tearing evident from the pixelated display of the rendered text and main image which, furthermore exhibited flickering upon scrolling through the slices in the relevant displayed image’s planar views i.e (Transversal, Coronal and Saggital). Troubleshooting along the way, we narrowed down the issue within the Rocket’s actor-subscription mechanism and decided to integrate Julia’s Base.Channel within MedEye3d.jl for handling the event and state management routine. Julia has asynchronous, threadsafe channels which facilitate in asynchronous programming with the help of a producer-consumer mechanism. An example usage of Base.Channel is as follows:

    -
    function consumer(channel::Base.Channel)
-    while(true)
-    channelData::String = take!(channel)
-    println("Channel got " * channelData)
-    end
-end
-
-newChannel = Base.Channel(100)
-
-@async consumer(newChannel)
-put!(newChannel, "apples")
    -

    Julia’s multiple dispatch made for the architectural setup of MedEye3d, facilitated fixing the issue of screen tearing. Below is how the on_next! function, invokes different reactive components based on the types of arguments it is dealing with.

    -
    -

    Dump data in channel -> fetch data from the channel in an event loop -> invoke on_next!(state, channelData) -> invoke relevant functionality based on the type of arguments passed

    -
    -

    -

    The end result was a visualizer with a seamless display of a CT image without any pixelating artifacts.

    -

    -
    -
    -

    2. Implementation of high level functions with simplified basic usage

    -

    Implementing a bare-bones image visualization required a lot of function calls and definitions, in order to execute the following phases:

    -
      -

    1. Rendering an image-plane with OpenGL

      2. -

    2. Loading data slices from the image

      3. -

    3. Creating texture specifications for modalities

      4. -

    4. Producing the final segmentation display

      5. -
    -

    In order to simplify basic usage, high-level abstractions were put in place with the help of MedImages.jl (under ongoing development) library to load images in the form of MedImage objects to formulate a single display function for the user. Further simplifications were made to accommodate options for the user to manipulate the imaging data that is displayed currently in the visualizer i.e retrieval of voxel arrays and their modification. Taking this in mind, the following relevant functions were exposed:

    -
    MedEye3d.SegmentationDisplay.displayImage()
    -
    MedEye3d.DisplayDataManag.getDisplayedData()
    -
    MedEye3d.DisplayDataManag.setDisplayedData()
    -

    Putting all of the above functions to use together, we can launch the visualizer, retrieve the displayed voxel data and modify it to our liking. A sample script to achieve the former, is highlighted below:

    -
    using MedEye3d
-ctNiftiImage = "/home/hurtbadly/Downloads/ct_soft_study.nii.gz"
-medEyeStruct = MedEye3d.SegmentationDisplay.displayImage(ctNiftiImage)
-displayData = MedEye3d.DisplayDataManag.getDisplayedData(medEyeStruct, [Int32(1), Int32(2)]) #passing the active texture number
-
-# We need to check if the return type of the displayData is a single Array{Float32,3} or a vector{Array{Float32,3}}
-# Now in this case we are setting Gaussian noise over the manualModif Texture voxel layer, and the manualModif texture defaults to 2 for active number
-
-displayData[2][:, :, :] = randn(Float32, size(displayData[2]))
-MedEye3d.DisplayDataManag.setDisplayedData(medEyeStruct, displayData)
    -

    The result of this Gaussian noise within the annotation layer, made for an outcome like the following:

    -

    -
    -
    -

    3. Improved precompilation with decreased outputs to reduce start time

    -

    Previously, the package’s precompilation was failing in Julia v1.9 and v1.10 due to pattern matching errors arising after the usage of match macros from the Match.jl pkg in MedEye3d’s keymapping workflow between GLFW callbacks from mouse and keyboard. The relevant equivalent native conditional (if-else) statements, resolved the issue and facilitated in successful precompilation of the package. Further, only following minimal outputs were produced during precompilation:

    -

    -

    Changes highlighted within the following pull-request:

    -

    https://github.com/JuliaHealth/MedEye3d.jl/pull/12

    -
    -
    -

    4. Automatic windowing for most common MRI and PET modalities

    -

    Windowing is a crucial aspect of medical imaging, particularly in MRI (Magnetic Resonance Imaging) and PET (Positron Emission Tomography) modalities. It enables radiologists to enhance the contrast of images, highlighting specific features and improving the overall diagnostic accuracy. Windowing involves controlling the display range of pixel values to optimize the contrast between different tissues or structures. The display range is defined by two values: the minimum (min) and maximum (max) values that contribute to the final range of pixels that are displayed. By adjusting these values, radiologists can enhance or suppress specific features in the image, facilitating a more accurate diagnosis.

    -

    The setTextureWindow function utilizes a set of predefined keymap controls to simplify the windowing process. The F1-F7 keys are designated for controlling windowing in MRI and PET modalities. The keymap controls are as follows:

    -
      -

    • F1: Display wide window for bone (CT) or increase minimum value for PET

      • -

    • F2: Display window for soft tissues (CT) or increase minimum value for PET

      • -

    • F3: Display wide window for lung viewing (CT) or increase minimum value for PET

      • -

    • F4: Decrease minimum value for display

      • -

    • F5: Increase minimum value for display

      • -

    • F6: Decrease maximum value for display

      • -

    • F7: Increase maximum value for display

      • -
    -

    Implementation of setTextureWindow Function

    -

    The setTextureWindow function is designed to update the texture window settings based on the input keymap control. The function takes three arguments:

    -
      -
    • activeTextur: The current texture specification
      • -
    • stateObject: The state data fields
      • -
    • windowControlStruct: The window control structure containing the letter code for the keymap control
      • -
    -

    The function performs the following steps:

    -
      -
    1. Checks the letter code of the keymap control and updates the minimum and maximum values of the texture specification accordingly.
      2. -
    2. Updates the uniforms for the texture specification using the controlMinMaxUniformVals function.
      3. -
    -
    function setTextureWindow(activeTextur::TextureSpec, stateObject::StateDataFields, windowControlStruct::WindowControlStruct)
-    activeTexturName = activeTextur.name
-    displayRange = activeTextur.minAndMaxValue[2] - activeTextur.minAndMaxValue[1]
-    activeTexturStudyType = activeTextur.studyType
-    if windowControlStruct.letterCode == "F1"
-        if activeTexturStudyType == "CT"
-            #Bone windowing in CT
-            activeTextur.minAndMaxValue = Float32.([400, 1000])
-        elseif activeTexturStudyType == "PET"
-            activeTextur.minAndMaxValue[1] += 0.10 * displayRange #windowing for pet, in the case of PET simply increase the minimum by 20% , doing the same in f1,f2 and f3
-        end
-    elseif windowControlStruct.letterCode == "F2"
-        if activeTexturStudyType == "CT"
-            activeTextur.minAndMaxValue = Float32.([-40, 350])
-        elseif activeTexturStudyType == "PET"
-            activeTextur.minAndMaxValue[1] += 0.10 * displayRange
-        end
-    elseif windowControlStruct.letterCode == "F3"
-        if activeTexturStudyType == "CT"
-            activeTextur.minAndMaxValue = Float32.([-426, 1000])
-        elseif activeTexturStudyType == "PET"
-            activeTextur.minAndMaxValue[1] += 0.10 * displayRange
-        end
-    elseif windowControlStruct.letterCode == "F4"
-        activeTextur.minAndMaxValue[1] -= 0.20 * displayRange
-    elseif windowControlStruct.letterCode == "F5"
-        activeTextur.minAndMaxValue[1] += 0.20 * displayRange
-    elseif windowControlStruct.letterCode == "F6"
-        activeTextur.minAndMaxValue[2] -= 0.20 * displayRange
-    elseif windowControlStruct.letterCode == "F7"
-        activeTextur.minAndMaxValue[2] += 0.20 * displayRange
-    elseif windowControlStruct.letterCode == "F8"
-        activeTextur.uniforms.maskContribution -= 0.10
-    elseif windowControlStruct.letterCode == "F9"
-        activeTextur.uniforms.maskContribution += 0.10
-    end
-
-    stateObject.mainForDisplayObjects.listOfTextSpecifications = map(texture -> texture.name == activeTexturName ? activeTextur : texture, stateObject.mainForDisplayObjects.listOfTextSpecifications)
-    coontrolMinMaxUniformVals(activeTextur)
-end
    -
    -

    Bone windowing in CT

    -
    -

    -
    -

    Bone windowing in PET

    -
    -

    -
    -
    -

    5. Adding support for multi-image viewing with crosshair marker for image registration

    -

    Following the mid-term evaluation, MedEye3d.jl underwent a significant enhancement, whereby a multi-image display capability was implemented through a series of refinements. Specifically, a novel approach was adopted, whereby separate OpenGL fragment shaders were introduced to concurrently render images on either side of the visualizer, namely the left and right views. Prior to integrating voxel data into the fragment shaders, an initial series of tests involved evaluating individual colors to validate the integrity of the double image display. A screenshot from one of these critical testing phases is presented below:

    -

    The shaders were further manipulated to automatically initialize for each of the images separately. Further, the reactive aspect of the visualizer in multi-image display mode was iterated upon and now, instead of a single state management struct, a vector of states was being passed around, facilitating the user to scroll each of the images separately just by simply hovering their mouse over either of the image, activating its relevant associated state struct.

    -

    Down below, is the struct for state that handles all of the things currently related with an image:

    -
    @with_kw mutable struct StateDataFields
-  currentDisplayedSlice::Int = 1 # stores information what slice number we are currently displaying
-  mainForDisplayObjects::forDisplayObjects = forDisplayObjects() # stores objects needed to  display using OpenGL and GLFW
-  onScrollData::FullScrollableDat = FullScrollableDat()
-  textureToModifyVec::Vector{TextureSpec} = [] # texture that we want currently to modify - if list is empty it means that we do not intend to modify any texture
-  isSliceChanged::Bool = false # set to true when slice is changed set to false when we start interacting with this slice - thanks to this we know that when we start drawing on one slice and change the slice the line would star a new on new slice
-  textDispObj::ForWordsDispStruct = ForWordsDispStruct()# set of objects and constants needed for text diplay
-  currentlyDispDat::SingleSliceDat = SingleSliceDat() # holds the data displayed or in case of scrollable data view for accessing it
-  calcDimsStruct::CalcDimsStruct = CalcDimsStruct()   #data for calculations of necessary constants needed to calculate window size , mouse position ...
-  valueForMasToSet::valueForMasToSetStruct = valueForMasToSetStruct() # value that will be used to set  pixels where we would interact with mouse
-  lastRecordedMousePosition::CartesianIndex{3} = CartesianIndex(1, 1, 1) # last position of the mouse  related to right click - usefull to know onto which slice to change when dimensions of scroll change
-  forUndoVector::AbstractArray = [] # holds lambda functions that when invoked will  undo last operations
-  maxLengthOfForUndoVector::Int64 = 15 # number controls how many step at maximum we can get back
-  fieldKeyboardStruct::KeyboardStruct = KeyboardStruct()
-  displayMode::DisplayMode = SingleImage
-  imagePosition::Int64 = 1
-  switchIndex::Int = 1
-  mainRectFields::GlShaderAndBufferFields = GlShaderAndBufferFields()
-  crosshairFields::GlShaderAndBufferFields = GlShaderAndBufferFields()
-  textFields::GlShaderAndBufferFields = GlShaderAndBufferFields()
-  spacingsValue::Union{Vector{Tuple{Float64,Float64,Float64}},Tuple{Float64,Float64,Float64}} = [(1.0, 1.0, 1.0)]
-  originValue::Union{Vector{Tuple{Float64,Float64,Float64}},Tuple{Float64,Float64,Float64}} = [(1.0, 1.0, 1.0)]
-  supervoxelFields::GlShaderAndBufferFields = GlShaderAndBufferFields()
-end
    -

    After the integrity of the fragment shaders was verified in multi-image, voxel data for the images was integrated and further modifications to the high-level functions were made and eventually the following script produced a rather appealing result.

    -

    Script for loading the same NIFTI image twice in the visualizer for side-by-side display:

    -
    using MedEye3d
-ctNiftiImage = "/home/hurtbadly/Downloads/ct_soft_study.nii.gz"
-MedEye3d.SegmentationDisplay.displayImage([[ctNiftiImage],[ctNifitImage]])
    -
    -

    Results in :

    -
    -

    -

    Crosshair marker for image registration are displayed in the relevant passive image to hightlight the same anatomical regions based on the spatial meta-data of the images i.e spacing, origin and direction. In order to achive the crosshair rendering in the passive image, the following action items were devised:

    -
      -

    1. Retrieval of GLFW Mouse Callbacks for x and y position of the cursor in window coordinates (0 to window-width) from the active image

      2. -

    2. Conversion of these x and y window coordinates into their relevant active image x and y texture coordinates

      3. -

    3. Conversion of these texture coordinates into real space point with the help of spatial metadata

      4. -

    4. Conversion of the real space point into the texture coordinates of the passive image

      5. -

    5. Conversion of the passive image texture coordinates into their relevant OpenGL coordinate system values (-1 to 1)

      6. -

    6. Rendering of crosshair on OpenGL coordinate in passive image

      7. -
    -

    Conversion between different coordinate systems and accounting for the image’s spatial metadata during calculating proved to be challenging at first, but with multiple revisions, a final solution was achieved with seemingly no noticeable amount of lag or delay. One such frame of [CT] images with crosshair display in multi-image is depicted below:

    -

    -
    -

    Another frame from the openGL rendering cycle, highlighting PET images with crosshair display in multi-image mode:

    -
    -

    -
    -
    -

    6. Adding support for the display of SuperVoxels sv with borders within the image slices to better understand anatomical regions within slices

    -

    In enhancing MedEye3d’s functionality, supporting super voxels (sv) with boundaries becomes paramount. The sv rendering, effectively capturing gradients, serves as the cornerstone for detecting these boundaries within both MRI and PET volumes. Supervoxels, described either through indicator masks or meshes, encapsulate regions of interest with distinct image characteristics. By integrating boundary detection for super-voxels, MedEye3d can offer enhanced segmentation capabilities, enabling more precise delineation and analysis of anatomical structures and pathological regions within medical imaging data.

    -

    Supervoxels are basically a collection of voxels that share similar image properties. For example: in MRI scans of the brain cortex, super voxels could represent clusters of voxels corresponding to specific anatomical regions or functional areas. The main objective of this task was to add support for the display of super voxel-based segmentation of images, followed by some janitorial tasks:

    -
      -

    1. Display of the borders of super-voxels (sv), extracted using the machine learning algorithms.

      2. -

    2. Checking image gradient agreement with super-voxel borders.

      3. -
    -

    This initial workflow involved, the initialization of relevant buffers in OpenGL for dynamic rendering of lines over the image display, namely vertex array buffers (vao), vertex buffers (vbo) and edge buffers (ebo). Further, these buffers are updated on a scroll event, where the information from the currently displayed slice is passed to the event handler, which invokes a function that updates the vertex buffer (vbo) with new vertices pertaining to the relevant slice number and planar view, precalculated from an HDF5 file during initialization of the visualizer. For instance, if the user is scrolling in the 3rd axis (transversal plane) and is currently on slice 40, the supervoxel display will pertain to edges specifically calculated for that specific slice in that plane.

    -

    Eventually, with ever so increasing number of attempts and a few hurdles along the way, one of which particularly stood out since it marked our first step towards a good direction:

    -
    -

    Challenges in rendering

    -
    -

    -

    At last, an appealing result hit our sight.

    -
    -

    Final result

    -
    -
    -

    Note: The image borders are intentional to emphasize the size of the visualizer which is currently defaulted to a certain width and height.

    -
    -

    -
    -

    Note: However, There are a few things left to cover here, most of which revolve around MedImages.jl and documentation for the same. List of PRs that facilitated the completion of the tasks highlighted above:

    -
    -
      -

    1. https://github.com/JuliaHealth/MedEye3d.jl/pull/21

      2. -

    2. https://github.com/JuliaHealth/MedEye3d.jl/pull/20

      3. -

    3. https://github.com/JuliaHealth/MedEye3d.jl/pull/16

      4. -

    4. https://github.com/JuliaHealth/MedEye3d.jl/pull/14

      5. -

    5. https://github.com/JuliaHealth/MedEye3d.jl/pull/13

      6. -

    6. https://github.com/JuliaHealth/MedEye3d.jl/pull/12

      7. -
    -
    -
    -
    -

    Contributions Beyond Coding

    -
    -

    1. Mentoring and Guidance

    -

    I regularly organized meetings with my mentor to seek guidance on project direction and troubleshooting issues in the visualizer. This ensured that I stayed on track, received timely feedback, and addressed any challenges that arose.

    -
    -
    -

    2. Package Documentation and Community Contribution

    -

    I contributed to other medical imaging sub-ecosystem packages in JuliaHealth, including MedImages.jl and MedEval3D.jl. Specifically, I set up documentation for these packages using DocuementerVitepress.jl. This not only enhanced the functionality of these packages but also helped maintain a coherent and organized package ecosystem.

    -
    -
    -

    3. Multirepo Management and Collaboration

    -

    In addition to my work on the MedEye3d visualizer, I made significant contributions to other JuliaHealth repositories, including MedImages.jl and worked over an Insight Toolkit wrapper library ITKIOWrapper.jl for support in image I/O down the road in MedImages.jl. I also maintained relevant documentation and ensured continuous collaboration and synchronization across these packages.

    -
    -
    -
    -

    Conclusions and Future Development

    -

    Within the scope of this 350-hour project, a comprehensive range of objectives were successfully addressed. Noteworthy achievements include:

    -
      -

    1. Fixed screen tear and flicker within the visualizer. Integration of threadsafe Julia channels.

      2. -

    2. Achieved multi-image display over CT and PET modalities with crosshair rendering (Although, only one modality can be visualize at a time, i.e either CT | CT or PET | PET).

      3. -

    3. Achieved supervoxel display in single image display mode.

      4. -

    4. Achieved automatic windowing of MRI and PET most common modalities.

      5. -
    -

    Future work would include:

    -
      -

    • Support for the users on Darwin (Apple-based platforms).

      • -

    • Apart from that, we would need to add a function that dynamically allocates the texture number to the manual modification mask, regardless of the number of images passed for display, which is currently defaulted to 2.

      • -

    • Also, in the future, we would explore the stretch goals a bit more rigorously, particularly the implementation of MedVoxelHD within MedEye3d.

      • -
    -
    -
    -

    Acknowledgements πŸ™‡β€β™‚οΈ

    -
      -

    1. Jakub Mitura: aka, Dr. Jakub Mitura

      2. -

    2. Carlos Castillo Passi: aka, cncastillo

      3. -
    -

    I would like to thank my mentor Dr. Jakub Mitura, for his help through out every phase of this project. The troubleshooting routines around problems would have rendered the project unsuccessful, if not for the support and guidance of my mentor throughout each part of this project. I would also like to thank Jacob Zelko, for leading the Juliahealth community with such vast expertise and leading efforts for engagement amongst the members through monthly meetings. My sincere gratitude towards your support, help and guidance through out the fellowship.

    - - - - - -
    - - Back to top

    References

    -
    -
    [1]
    J. Mitura and B. E. Chrapko, β€œ3D medical segmentation visualization in julia with MedEye3d,” Zeszyty Naukowe Warszawskiej WyΕΌszej SzkoΕ‚y Informatyki, vol. nr 25, pp. 57–67, 2021, doi: 10.26348/znwwsi.25.57
    -
    -

    Citation

    BibTeX citation:
    @online{goyal2024,
-  author = {Goyal, Divyansh},
-  title = {GSoC ’24: {Adding} Functionalities to Medical Imaging
-    Visualizations},
-  date = {2024-11-01},
-  url = {https://juliahealth.org/JuliaHealthBlog/posts/divyansh-gsoc/gsoc-2024-fellows.html},
-  langid = {en}
-}
-
    For attribution, please cite this work as:
    -
    D. -Goyal, β€œGSoC ’24: Adding functionalities to medical imaging -visualizations,” Nov. 01, 2024. Available: https://juliahealth.org/JuliaHealthBlog/posts/divyansh-gsoc/gsoc-2024-fellows.html
    -
    ]]>     - gsoc - openGl - imaging - neuro - https://juliahealth.org/JuliaHealthBlog/posts/divyansh-gsoc/gsoc-2024-fellows.html - Fri, 01 Nov 2024 04:00:00 GMT -     - - GSoC Co-Mentoring Experience - Mounika Thakkallapally - https://juliahealth.org/JuliaHealthBlog/posts/mounika-gsoc-mentor/ - -

    Introduction

    -

    Hello πŸ‘‹, I am Mounika. I am a Data Engineer at Brown Center for Biomedical Informatics. This summer, I had the privilege of co-mentoring a talented student, Jay Sanjay alongside Jacob Zelko (@TheCedarPrince) on a project for Google Summer of Code (aka GSoC). Here, I would like to share my experience as a co-mentor, offering insights for future mentors and students alike.

    -

    Before diving into my experience, let me provide some background on how it all started. At JuliaCon 2023, I had the chance to meet Jacob Zelko and have been following his work at JuliaHealth ever since. One day, I received a message from Jacob asking if I’d be interested in co-mentoring Jay for his GSoC project. Fortunately, I was already working on several projects at BCBI involving Julia programming, OMOP CDM databases and OHDSI tools, all of which were closely aligned with Jay’s project.

    -
    -

    Feel free to visit Jay’s work on OMOPCDMPathways.jl or read about his fellowship experience from this post.

    -
    - -
    -

    Mentor-Mentee Relationship

    -

    Jay, being a proactive student with a strong involvement in JuliaHealth, worked closely with Jacob to build a proposal for the project several months before GSoC began this year. His early involvement and familiarity with the community set a solid foundation for the project. Jacob, with his extensive experience mentoring GSoC students over the years, brought invaluable insights not only for Jay but also for me, as I was just beginning my journey as a mentor.

    -

    Jacob established regular weekly Zoom meetings for the three of us to discuss Jay’s progress, review his accomplishments, and plan the next steps. During these meetings, I focused on taking detailed notes to ensure we stayed organized and up to date with all the tasks. We used Trello, a project management tool, to track progress and manage project tasks efficiently. Additionally, we stayed connected thoughout the week via a dedicated slack channel for any ongoing discussions or questions (on the Julia Slack).

    -
    -
    -

    Technical Discussion

    -

    Jay’s project β€œDeveloping Tooling for Observational Health Research in Julia” was inspired by the TreatmentPatterns R package [1]. The main goal of the project was to enhance observational research capabilities within the JuliaHealth ecosystem. To help Jay get started, Jacob created 10 to 15 GitHub issues, each linked to a specific function that Jay planned to work on.

    -

    During our weekly meeting, we discussed the challenges Jay encountered, any roadblacks in his progress, and reviewed the pull requests he submitted on GitHub. These sessions allowed us to provide timely feedback and guide Jay through complex technical issues, ensuring steady progress throughout the project.

    -
    -
    -

    Learnings and Observations

    -

    Jay’s proactive approach, steady progress, thoughtful questions, and clear focus on completing the project are qualities from which every student can benefit. His dedication to learning and problem-solving made a significant impact on the success of the project.

    -
    -

    Tips for Mentees

    -

    From a mentee’s perspective having the following qualities would be helpful

    -
      -

    1. Stick to the proposal: While it’s natural to feel the urge explore new ideas beyond the original proposal, it’s essential to remain focused on the original proposal due to time constrains.

      2. -

    2. Adaptability and open-mindedness: Be open to feedback and willing to adjust the tasks as you face challenges.

      3. -

    3. Time Management: Many students juggle internships, interviews and other commitments during the summer. So it’s to manage time effectively and discuss with the mentor about the progress during those times.

      4. -

    4. Effective communication: Stay up to date with any updates from GSoC or from the mentor. Keeping your mentor updated about your progress or any challenges helps build a collaborative and supportive mentor relationship.

      5. -
    -
    -
    -

    Tips for Mentors

    -

    On the other hand, Jacob demonstrated what it means to be an effective mentor. He showed me how to foster a supportive, collaborative relationship with the student. These are the lessons that I will carry forward in future mentorship roles:

    -

    From a mentor’s perspective having the following qualities would be helpful

    -
      -

    1. Clear communication: Communicating well in advance about the availability to meet or to review the work, having frequent meetings with the mentee would be helpful.

      2. -

    2. Encouragement: While offering support, it’s important to encourage the mentee to take ownership of the project.

      3. -

    3. Commitment and time: Mentoring GSoC is a voluntary role, often taken on in addition to regular professional work. Balancing GSoC with other work commitments requires effective time management and commitment.

      4. -

    4. Structured Guidance: Providing a well-organized plan, such as using task management tools like Trello and GitHub issues, ensures that the mentee can follow a clear path towards success completion of the project.

      5. -
    -
    -
    -
    -

    Conclusion

    -

    Google Summer of Code offers an incredible opportunity for students to hone their programming skills while contributing to impactful open-source projects. It was a rewarding experience to be part of this journey as a co-mentor, and I am grateful to Jacob for giving me the chance to be involved in such a meaning project with the JuliaHealth community.

    -

    Through this experience, I not only gained insights into effective mentorship but also deepened my understanding of open-source collaboration and its potential to drive innovation in healthcare. I’m excited to explore further ways I can contribute to the JuliaHealth ecosystem and continue supporting the community.

    -
    -

    Let’s Keep in Touch!

    -

    If you would like to know more about me, you can connect with me on Linkedin.

    - - - - - -
    -
    - - Back to top

    References

    -
    -
    [1]
    A. F. Markus, K. M. Verhamme, J. A. Kors, and P. R. Rijnbeek, β€œTreatmentPatterns: An r package to facilitate the standardized development and analysis of treatment patterns across disease domains,” Computer Methods and Programs in Biomedicine, vol. 225, p. 107081, 2022.
    -
    -

    Citation

    BibTeX citation:
    @online{thakkallapally2024,
-  author = {Thakkallapally, Mounika},
-  title = {GSoC {Co-Mentoring} {Experience}},
-  date = {2024-09-12},
-  url = {https://juliahealth.org/JuliaHealthBlog/posts/mounika-gsoc-mentor/},
-  langid = {en}
-}
-
    For attribution, please cite this work as:
    -
    M. -Thakkallapally, β€œGSoC Co-Mentoring Experience,” Sep. 12, -2024. Available: https://juliahealth.org/JuliaHealthBlog/posts/mounika-gsoc-mentor/
    -
    ]]>     - gsoc - mentor - experience - https://juliahealth.org/JuliaHealthBlog/posts/mounika-gsoc-mentor/ - Thu, 12 Sep 2024 04:00:00 GMT -     - - GSoC ’24: Developing Tooling for Observational Health Research in Julia - Jay Sanjay Landge - https://juliahealth.org/JuliaHealthBlog/posts/jay-gsoc/gsoc-2024-fellows.html - -

    Hi Everyone! πŸ‘‹

    -

    I am Jay Sanjay, and I am pursuing a Bachelor’s degree in Computational Sciences and Engineering at the Indian Institute of Technology (IIT) in Hyderabad, India. Coming from a mathematics and data analysis background, I was initially introduced to Julia at my university lectures. Later, I delved more into the language and the JuliaHealth community - an intersection of Julia, Health Research, Data Sciences, and Informatics. Here, I met some of the great folks in JuliaHealth and I decided to take it on as a full-fledged summer project. In this blog, I will briefly describe what my project is and what I did as a part of it.

    -
      -

    1. You can find my GSoC project archive link

      2. -

    2. You can also find the related publication of my work on Zenodo

      3. -

    3. If you want to know more about me, you can connect with me on LinkedIn and follow me on GitHub

      4. -
    - -
    -

    Background

    -
    -

    What Is Observational Health Research?

    -

    Observational Health Research refers to studies that analyze real-world data (such as patient medical claims, electronic health records, etc.) to understand patient health. These studies often encompass a vast amount of data concerning patient care. An outstanding challenge here is that these datasets can become very complex and grow large enough to require advanced computing methods to process this information.

    -
    -
    -

    What Are Patient Pathways?

    -

    Patient pathways refer to the journey that patients with specific medical conditions undergo in terms of their treatment. This concept goes beyond simple drug uptake statistics and looks at the sequence of treatments patients receive over time, including first-line treatments and subsequent therapies. Understanding patient pathways is essential for analyzing treatment patterns, adherence to clinical guidelines, and the disbursement of drugs. To analyze patient pathways, one would typically use real-world data from sources such as electronic health records, claims data, and registries. However, barriers such as data interoperability and resource requirements have hindered the full utilization of real-world data for this purpose.

    -

    So to address these challenges we (the JuliaHealth organization and I) want to develop a set of tools to extract and analyze these patient pathways. These sets of tools are based on the Observational Medical Outcomes Partnership (OMOP) Common Data Model, which standardizes healthcare data to promote interoperability.

    -
    -
    -
    -

    Project Description

    -

    As part of this project with JuliaHealth, I developed a new package called OMOPCDMPathways.jl. This package is designed for deployment in research projects, particularly those related to health and medical data analysis. This project takes much inspiration from the paper TreatmentPatterns: An r package to facilitate the standardized development and analysis of treatment patterns across disease domains [1] and explores the implementation of some of those ideas to develop new tools within the JuliaHealth Observational Health Subecosystem for exploring patient pathways. Additional new features and approaches were added and explored within this project. Additionally, I have authored a developer guide for the package, providing instructions on its use and contribution. This project provided me with hands-on experience in developing production-level code and exposed me to open-source software development practices. I had the opportunity to work in a team, under my mentors, and ensured the integration of the package with the rest of JuliaHealth, facilitating its adoption and usability within the community.

    -
    -
    -

    Project Goals

    -

    As a part of the development, I was majorly engaged in crafting the following functionalities:

    -
      -

    1. Selecting treatments of interest: The first decision that was made was to decide the time from which the desired treatments of interest should be included in the treatment pathway study. Here the periodPriorToIndex specifies the period (i.e. number of days) before the index date from which treatments should be included.

      2. -

    2. Find Treatment History of Patients: Create the treatment history of a patient based on target, event, and exit cohorts. Then filter patient events based on the start and end dates of the target cohort. Third, Calculate the duration of treatment eras and the gap between treatments.

      3. -

    3. Filters: Filter the treatment history based on the minEraDuration parameter and EraCollapse parameter.

      4. -

    4. Create a Continuous Integration and Continuous Development pipeline for the package.

      5. -

    5. Implement the combinationWindow function, which combines treatments with various overlapping strategies.

      6. -
    -

    Additionally, we had a few stretch goals which were:

    -
      -

    1. Composing with JuliaStats Ecosystem

      2. -

    2. Novel Visualizations for Pathways

      3. -
    -
    -
    -

    Tasks

    -
    -

    1. Setting Up the Package in JuliaHealth Channel

    -

    Initially, there was no package as such for generating pathways, so I had to build it from scratch. First, I created the repository with the name OMOPCDMPathways.jl. Once the repository was created, we needed to have a skeleton for a standard Julia repository. For this, we used the PkgTemplates.jl this provided a basic skeleton for the repository that included - folders for test suites, documentation, src code files, GitHub files, README and LICENSE file, TOML and citation files. All this we can further edit and modify as per our work. By default, PkgTemplate.jl uses Documenter.jl for the documentation part but as suggested and discussed with my mentor we decided to shift to DocumenterVitepress.jl for the documentation part. However, we still faced some deployment issues in the new documentation due to a few mistakes in the make.jl file, thanks to Anshul Singhvi for helping fix the Deployment issues with DocumenterVitepress. With this, we were ready with the documentation set up and fully functional. After we had shifted to DocumenterVitepress the main task now was to host the documentation, this was done using Github-Actions, detailed steps for hosting are provided at this page. Then we added the CodeCov to our package by triggering it via a dummy function and a corresponding test case for it. Also, the CI for the package was set up with it. And, now finally the repository was ready with test coverage, CI, and documentation fully functional repository ready. Here’s some snapshots of the documentation set-up:

    -

    -
    -

    Initial documentation with Documenter.jl

    -
    -

    -
    -

    New documentation using DocumenterVitepress.jl

    -
    -

    So, as a part of it, I created this documentation which provides detailed steps for converting docs from Documenter to DocumenterVitepress.

    -
    -
    -

    2. Loading the PostgreSQL Database

    -

    The main database we worked on/built analysis was the freely available OMOPCDM Database. The Database was formatted within a PostgreSQL database with installation instructions here are some instructions on how to set up Postgres in a Linux machine. However, I was provided with some more extra synthetic data from my mentor for further testing of the functionalities. Being a very large database we had to strategically download it further, my mentor helped me in setting up the Postgres on my local machine. Once, the database was set up proper testing was performed on it to check if things were as expected. With this, we were done with the database setup as well and could finally dive into the actual code logic for the Pathways synthesis.

    -
    -
    -

    3. Testing and Development setup on my local computer

    -

    To get a proper environment for functionality creation and concurrent testing we required a proper testing setup so that we could test the new functions made at the same time. This was done using Revise.jl, which helps to keep Julia sessions running without frequent restarts when making changes to code. It allowed me to edit my code, update packages, or switch git branches during a session, with changes applied immediately in the next command. My mentor helped me set it up, added Revise.jl to the global Julia environment, also PackageCompatUI that provides a terminal text interface to the [compat] section of a Julia Project.toml file, and finally made a Julia script by the name β€œstartup.jl” out of it. This script was then added to /home/jay-sanjay/.julia/config/ path in my local computer.

    -

    Here is the sample for the startup.jl file:

    -
    using PackageCompatUI
-using PkgTemplates
-using Revise
-
-###################################
-# HELPER FUNCTIONS
-###################################
-function template()
-    Template(;
-        user="jay-sanjay",
-        dir="~/FOSS",
-        authors="jaysanjay <jaysanjay@gmail.com> and contributors",
-        julia=v"1.6",
-        plugins=[
-            ProjectFile(; version=v"0.0.1"),
-            Git(),
-            Readme(),
-            License(; name="MIT"),
-            GitHubActions(; extra_versions=["1.6", "1", "nightly"]),
-            TagBot(),
-            Codecov(),
-            Documenter{GitHubActions}(),
-            Citation(; readme = true),
-            RegisterAction(),
-            BlueStyleBadge(),
-            Formatter(;style = "blue")
-        ],
-    )
-end
    -
    -
    -

    4. Selecting Treatments of Interest

    -

    So, as a part of this, we used the previously mentioned research paper and discussion with the mentors we came up with logic for it. The first thing to do was to determine the moment in time from which selected treatments of interest should be included in the treatment pathway. The default is all treatments starting after the index date of the target cohort. For example, for a target cohort consisting of newly diagnosed patients, treatments after the moment of first diagnosis are included. However, it would also be desirable to include (some) treatments before the index date, for instance in case a specific disease diagnosis is only confirmed after initiating treatment. Therefore, periodPriorToIndex specifies the period (i.e. number of days) before the index date from which treatments should be included. We have created two dispatches for this function. After that proper testing and documentation are also added.

    -

    A basic implementation for it is:

    -
      -

    1. Construct a SQL query to select cohort_definition_id, subject_id, and cohort_start_date from a specified table, filtering by cohort_id.

      2. -

    2. The SQL query construction and execution was done using the FunSQL.jl library, in the below-shown manner:

      3. -
    -
    sql = From(tab) |>
-            Where(Fun.in(Get.cohort_definition_id, cohort_id...)) |>
-            Select(Get.cohort_definition_id, Get.subject_id, Get.cohort_start_date) |>
-            q -> render(q, dialect=dialect)
    -
      -

    1. Executes the constructed SQL query using a database connection, fetching the results into a data frame.

      2. -

    2. If the DataFrame is not empty, convert cohort_start_date to DateTime and subtract date_prior from each date, then return the modified DataFrame.

      3. -
    -

    This was then be called this:

    -
    period_prior_to_index(
-        cohort_id = [1, 1, 1, 1, 1], 
-        conn; 
-        date_prior = Day(100), 
-        tab=cohort
-    )
    -
    -
    -

    5. Filters Applied

    -

    After this, we where needed to get the patient’s database filtered more finely so that there are minimal variations that can be ignored. The duration of the above extracted event eras may vary a lot and it can be preferable to limit to only treatments exceeding a minimum duration. Hence, minEraDuration specifies the minimum time an event era should last to be included in the analysis. All these implementations were more of Dataframe manipulation where I used DataFrames.jl package.

    -

    After that proper testing and documentation are also added.

    -

    A basic implementation for the minEraDuration is: It filters the treatment history DataFrame to retain only those rows where the duration between drug_exposure_end and drug_exposure_start is at least minEraDuration. This function can be used as follows:

    -
    #| eval: false 
-
-calculate_era_duration(test_df, 920000)
-
-#= ... =#
-
-4Γ—3 DataFrame
- Row β”‚ person_id  drug_exposure_start  drug_exposure_end 
-     β”‚ Int64      Float64              Int64             
-─────┼───────────────────────────────────────────────────
-   1 β”‚         1           -3.7273e8          -364953600
-   2 β”‚         1            2.90304e7           31449600
-   3 β”‚         1           -8.18208e7          -80006400
-   4 β”‚         1            1.32918e9         1330387200
    -

    Another filter we worked on is the EraCollapse. So, let’s suppose a case where an individual receives the same treatment for a long period of time (e.g. need for chronic treatment). Then it’s highly likely that the person would require refills. Now as patients are not 100% adherent, there might be a gap between two subsequent event eras. Usually, these eras are still considered as one treatment episode, and the eraCollapseSize deals with the maximum gap within which two eras of the same event cohort would be collapsed into one era (i.e. seen as a continuous treatment instead of a stop and re-initiation of the same treatment). After that proper testing and documentation are also added.

    -

    A basic implementation for the eraCollapseSize is: (a) Sorts the data frame by event_start_date and event_end_date. (b) Calculates the gap between each era and the previous era. (c) Filters out rows with gap_same > eraCollapseSize.

    -

    These functions can be used as follows:

    -
    #| eval: false 
-
-#= ... =#
-
-EraCollapse(treatment_history = test_df, eraCollapseSize = 400000000)
-4Γ—4 DataFrame
- Row β”‚ person_id  drug_exposure_start  drug_exposure_end  gap_same   
-     β”‚ Int64      Float64              Int64              Float64    
-─────┼───────────────────────────────────────────────────────────────
-   1 β”‚         1           -5.33347e8         -532483200  -1.86373e9
-   2 β”‚         1           -3.7273e8          -364953600   1.59754e8
-   3 β”‚         1           -8.18208e7          -80006400   2.83133e8
-   4 β”‚         1            2.90304e7           31449600   1.09037e8
    -
    -
    -

    6. Treatment History of the Patients

    -

    The create_treatment_history function constructs a detailed treatment history for patients in a target cohort by processing and filtering event cohort data from a given DataFrame. It begins by isolating the target cohort based on its cohort_id, adding a new column for the index_year derived from the cohort’s start date. Then, it selects relevant event cohorts based on a provided list of cohort IDs and merges them with the target cohort on the subject_id to associate events with individuals in the target group. The function applies different filtering criteria depending on whether the user is interested in treatments starting or ending within a specified period before the target cohort’s start date (defined by periodPriorToIndex). It keeps only the event cohorts that match the filtering condition, ensuring that only relevant treatments are considered. After filtering, the function calculates time gaps between consecutive cohort events for each patient, adding these gaps to the DataFrame. The final DataFrame provides a history of treatments, including the dates of events and the time intervals between them, offering a clear timeline of treatment for each patient. After that proper testing and documentation are also added.

    -
    -
    -

    7. CombinationWindow Functionality To Combine Overlapping Treatments

    -

    Now once we have the filtering of the treatments done, we need to combine the overlapping treatments based on some set of rules. The combinationWindow specifies the time that two event eras need to overlap to be considered a combination treatment. If there are more than two overlapping event eras, we sequentially combine treatments, starting from the first two overlapping event eras.

    -

    The combination_Window function processes a patient’s treatment history by identifying overlapping treatment events and combining them into continuous treatment periods based on certain rules. It first converts event_cohort_id into strings and sorts the treatment data by person_id, event_start_date, and event_end_date. The helper function selectRowsCombinationWindow calculates gaps between consecutive treatments, marking rows where treatments overlap or occur too closely. In the main loop, the function checks these overlaps and gaps against a specified combinationWindow. If treatments overlap (or nearly overlap), the function adjusts the treatment periods by either merging adjacent rows or splitting rows to create continuous treatment periods. The process continues until all overlapping treatments are combined into one, creating an updated and accurate treatment history. The function ensures the final output reflects realistic treatment windows by handling special cases where gaps between treatments are smaller than the treatment durations themselves.

    -

    It mainly covers the three cases mentioned in the R-research paper:

    -
    -

    Switch Case:

    -

    Condition: If the gap between the two treatment events is smaller than the combinationWindow, but the gap is not equal to the duration of either event. Action: The event_end_date of the previous treatment is set to the event_start_date of the current treatment. This effectively β€œshifts” the previous treatment’s end date to eliminate the gap, merging the treatments into one continuous period. Purpose: This ensures that treatment gaps that are too small (less than combinationWindow) are treated as part of the same treatment window.

    -
    #| eval: false 
-
-#= ... =#
-
-if -gap_previous < combinationWindow && !(-gap_previous in [duration_era, prev_duration_era])
-    treatment_history[i-1, :event_end_date] = treatment_history[i, :event_start_date]
    -

    Here is the pictorial representation for the same:

    -
    -
    -

    FRFS (First Row, First Shortened):

    -

    Condition: If the gap is larger than or equal to the combinationWindow, or the gap equals the duration of one of the two treatments, and the first treatment ends before or on the same date as the second treatment. Action: A new row is created where the second treatment’s event_end_date is set to the end date of the first treatment. This preserves the overlap but ensures that the earlier treatment period stays intact. Purpose: This prevents unnecessary truncation of the first treatment if it spans the entire overlap window.

    -
    #| eval: false 
-
-#= ... =#
-
-elseif -gap_previous >= combinationWindow || -gap_previous in [duration_era, prev_duration_era]
-    if treatment_history[i-1, :event_end_date] <= treatment_history[i, :event_end_date]
-        new_row = deepcopy(treatment_history[i, :])
-        new_row.event_end_date = treatment_history[i-1, :event_end_date]
-        append!(treatment_history, DataFrame(new_row'))
    -

    Here is the pictorial representation for the same:

    -
    -
    -

    LRFS (Last Row, First Shortened):

    -

    Condition: If the gap is larger than or equal to the combinationWindow, or the gap equals the duration of one of the treatments, and the first treatment ends after the second treatment. Action: The current treatment’s event_end_date is adjusted to match the event_end_date of the previous treatment. Purpose: This handles cases where the second treatment’s window should be shortened to prevent overlap with the previous treatment, merging them into a single continuous window.

    -
    #| eval: false 
-
-#= ... =#
-
-else
-    treatment_history[i, :event_end_date] = treatment_history[i-1, :event_end_date]
    -

    Here is the pictorial representation for the same:

    -
    -

    Note: However, There are a few things left to cover here, most of which are the documentation and writing the test suite for the same.

    -
    -
    -
    -
    -
    -

    Contributions Beyond Coding

    -
    -

    1. Organizing Meetings and Communication

    -

    Throughout the project, I regularly met with my mentor, [Jacob Zelko], and co-mentor, [Mounika], via weekly Zoom calls to discuss progress and seek guidance. During these meetings, we reviewed my work, identified areas where I needed help, and set clear goals for the upcoming weeks. We used Trello to organize and track these goals, ensuring that nothing was overlooked. My mentors provided detailed insights into specific technical aspects and guided me through the logic behind various functions. Outside of our scheduled meetings, they were always available for quick queries via Slack, ensuring constant support.

    -
    -
    -

    2. Personal Documentation

    -

    In addition to the notes from our meetings, I maintained personal documentation where I recorded every step I took, including the challenges I faced and the mistakes I made. This helped me reflect on my progress and stay organized throughout the fellowship. Following my selection for GSoC 2024, I also published a blog post on Medium to share my journey and experiences with the Julia Language community.

    -
    -
    -

    3. Contributions To the Rest of the JuliaHealth Repositories

    -

    Earlier I have contributed a lot to the OMOPCDMCohortCreator.jl including adding new functionalities writing test suites, adding blogs including - Patient Pathways within JuliaHealth. Apart from that I also initiated 3 new releases of this package.

    -
    -
    -
    -

    Conclusions and Future Development

    -

    This project was a 350-hour large project since there were many goals to accomplish. Here is what we accomplished:

    -
      -

    1. Built a new repository in JuliaHealth land dedicated especially to treatment pathways synthesis.

      2. -

    2. CI/CD for the Package and Documentation hosting.

      3. -

    3. All required basic functionalities required to build the pathways.

      4. -

    4. Documentation and test suites added for each.

      5. -
    -

    Future work would include:

    -
      -

    • Finish this PR test-suites and documentation are remaining for this PR.

      • -

    • Apart from that, we would need to add a function that sews up all the functionalities of the package so that the user can run the complete pathways analysis by running just one function instead of running each of the functions manually.

      • -

    • Also, in the future, we would explore what statistical functionalities we would need to further explore pathways.

      • -

    • Then, we could explore how to compose JuliaHealth with packages from ecosystems like JuliaStats and JuliaDSP (for time series analysis) that are mentioned in this PR.

      • -

    • And finally work on creating novel visualizations for the Pathways. Commonly used visualizations for treatment pathways (such as sunburst or icicle plots) showing which patients fall under what treatment pathways could be developed as plotting recipes to visualize various aspects of a patient’s care pathway rapidly.

      • -
    -
    -
    -

    Acknowledgements πŸ™‡β€β™‚οΈ

    -
      -

    1. Jacob S. Zelko: aka, TheCedarPrince

      2. -

    2. Mounika Thakkallapally

      3. -
    -

    Thank you for your continuous help and support throughout the fellowship. Note: This blog post was also written with the assistance of LLM technologies to help with grammar, flow, and spelling!

    - - - - - -
    - - Back to top

    References

    -
    -
    [1]
    A. F. Markus, K. M. Verhamme, J. A. Kors, and P. R. Rijnbeek, β€œTreatmentPatterns: An r package to facilitate the standardized development and analysis of treatment patterns across disease domains,” Computer Methods and Programs in Biomedicine, vol. 225, p. 107081, 2022.
    -
    -

    Citation

    BibTeX citation:
    @online{sanjay_landge2024,
-  author = {Sanjay Landge, Jay},
-  title = {GSoC ’24: {Developing} {Tooling} for {Observational} {Health}
-    {Research} in {Julia}},
-  date = {2024-09-07},
-  url = {https://juliahealth.org/JuliaHealthBlog/posts/jay-gsoc/gsoc-2024-fellows.html},
-  langid = {en}
-}
-
    For attribution, please cite this work as:
    -
    J. -Sanjay Landge, β€œGSoC ’24: Developing Tooling for Observational -Health Research in Julia,” Sep. 07, 2024. Available: https://juliahealth.org/JuliaHealthBlog/posts/jay-gsoc/gsoc-2024-fellows.html
    -
    ]]>     - gsoc - sql - observational health - analysis - https://juliahealth.org/JuliaHealthBlog/posts/jay-gsoc/gsoc-2024-fellows.html - Sat, 07 Sep 2024 04:00:00 GMT -     - - GSoC ’24: Enhancements to KomaMRI.jl GPU Support - Ryan Kierulf - https://juliahealth.org/JuliaHealthBlog/posts/ryan-gsoc/Ryan_GSOC.html - -

    Hi! πŸ‘‹

    -

    I am Ryan, an MS student currently studying computer science at the University of Wisconsin-Madison. Looking for a project to work on this summer, my interest in high-performance computing and affinity for the Julia programming language drew me to Google Summer of Code, where I learned about this project opportunity to work on enhancing GPU support for KomaMRI.jl.

    -

    In this post, I’d like to summarize what I did this summer and everything I learned along the way!

    -
    -

    If you want to learn more about me, you can connect with me here: LinkedIn, GitHub

    -
    - -
    -

    What is KomaMRI?

    -

    KomaMRI is a Julia package for efficiently simulating Magnetic Resonance Imaging (MRI) acquisitions. MRI simulation is a useful tool for researchers, as it allows testing new pulse sequences to analyze the signal output and image reconstruction quality without needing to actually take an MRI, which may be time or cost-prohibitive.

    -

    In contrast to many other MRI simulators, KomaMRI.jl is open-source, cross-platform, and comes with an intuitive user interface (To learn more about KomaMRI, you can read the paper introducing it here). However, being developed fairly recently, there are still new features that can be added and optimization to be done.

    -
    -
    -

    Project Goals

    -

    The goals outlined by Carlos (my project mentor) and I the beginning of this summer were:

    -
      -

    1. Extend GPU support beyond CUDA to include AMD, Intel, and Apple Silicon GPUs, through the packages AMDGPU.jl, oneAPI.jl, and Metal.jl

      2. -

    2. Create a CI pipeline to be able to test each of the GPU backends

      3. -

    3. Create a new kernel-based simulation method optimized for the GPU, which we expected would outperform array broadcasting

      4. -

    4. (Stretch Goal) Look into ways to support running distributed simulations across multiple nodes or GPUs

      5. -
    -
    -
    -

    Step 1: Support for Different GPU backends

    -

    Previously, KomaMRI’s support for GPU acceleration worked by converting each array used within the simulation to a CuArray, the device array type defined in CUDA.jl. This was done through a general gpu function. The inner simulation code is GPU-agnostic, as the same operations can be performed on a CuArray or a plain CPU Array. This approach is good for extensibility, as it does not require writing different simulation code for the CPU / GPU, or different GPU backends, and would only work in a language like Julia based on runtime dispatch!

    -

    To extend this to multiple GPU backends, all that is needed is to generalize the gpu function to convert to either the device types of CUDA.jl, AMDGPU.jl, Metal.jl, or oneAPI.jl, depending on which backend is being used. To give an idea of what the gpu conversion code looked like before, here is a snippet:

    -
    struct KomaCUDAAdaptor end
-adapt_storage(to::KomaCUDAAdaptor, x) = CUDA.cu(x)
-
-function gpu(x)
-    check_use_cuda()
-    return use_cuda[] ? fmap(x -> adapt(KomaCUDAAdaptor(), x), x; exclude=_isleaf) : x
-end
-
-#CPU adaptor
-struct KomaCPUAdaptor end
-adapt_storage(to::KomaCPUAdaptor, x::AbstractArray) = adapt(Array, x)
-adapt_storage(to::KomaCPUAdaptor, x::AbstractRange) = x
-
-cpu(x) = fmap(x -> adapt(KomaCPUAdaptor(), x), x)
    -

    The fmap function is from the package Functors.jl and can recursively apply a function to a struct tagged with @functor. The function being applied is adapt from Adapt.jl, which will call the lower-level adapt_storage function to actually convert to / from the device type. The second parameter to adapt is what is being adapted, and the first is what it is being adapted to, which in this case is a custom adapter struct KomaCUDAAdapter.

    -

    One possible approach to generalize to different backends would be to define additional adapter structs for each backend and corresponding adapt_storage functions. This is what the popular machine learning library Flux.jl does. However, there is a simpler way!

    -

    Each backend package (CUDA.jl, Metal.jl, etc.) already defines adapt_storage functions for converting different types to / from corresponding device type. Reusing these functions is preferable to defining our own since, not only does it save work, but it allows us to rely on the expertise of the developers who wrote those packages! If there is an issue with types being converted incorrectly that is fixed in one of those packages, then we would not need to update our code to get this fix since we are using the definitions they created.

    -

    Our final gpu and cpu functions are very simple. The backend parameter is a type derived from the abstract Backend type of KernelAbstractions.jl, which is extended by each of the backend packages:

    -
    import KernelAbstractions as KA
-
-function gpu(x, backend::KA.GPU)
-    return fmap(x -> adapt(backend, x), x; exclude=_isleaf)
-end
-
-cpu(x) = fmap(x -> adapt(KA.CPU(), x), x, exclude=_isleaf)
    -

    The other work needed to generalize our GPU support involved switching to use package extensions to avoid having each of the backend packages as an explicit dependency, and defining some basic GPU functions for backend selection and printing information about available GPU devices. The pull request for adding support for multiple backends is linked below:

    -
    -

    https://github.com/JuliaHealth/KomaMRI.jl/pull/405

    -
    -
    -
    -

    Step 2: Buildkite CI

    -

    At the time the above pull request was merged, we weren’t sure whether the added support for AMD and Intel GPUs actually worked, since we only had access to CUDA and Apple Silicon GPUs. So the next step was to set up a CI to test each GPU backend. To do this, we used Buildkite, which is a CI platform that many other Julia packages also use. Since there were many examples to follow, setting up our testing pipeline was not too difficult. Each step of the pipeline does the required environment setup and then calls Pkg.test() for KomaMRICore. As an example, here is what the AMDGPU step of our pipeline looks like:

    -
          - label: "AMDGPU: Run tests on v{{matrix.version}}"
-        matrix:
-          setup:
-            version:
-              - "1"
-        plugins:
-          - JuliaCI/julia#v1:
-              version: "{{matrix.version}}"
-          - JuliaCI/julia-coverage#v1:
-              codecov: true
-              dirs:
-                - KomaMRICore/src
-                - KomaMRICore/ext
-        command: |
-          julia -e 'println("--- :julia: Instantiating project")
-              using Pkg
-              Pkg.develop([
-                  PackageSpec(path=pwd(), subdir="KomaMRIBase"),
-                  PackageSpec(path=pwd(), subdir="KomaMRICore"),
-              ])'
-          
-          julia --project=KomaMRICore/test -e 'println("--- :julia: Add AMDGPU to test environment")
-              using Pkg
-              Pkg.add("AMDGPU")'
-          
-          julia -e 'println("--- :julia: Running tests")
-              using Pkg
-              Pkg.test("KomaMRICore"; coverage=true, test_args=["AMDGPU"])'
-        agents:
-          queue: "juliagpu"
-          rocm: "*"
-        timeout_in_minutes: 60
    -

    We also decided that in addition to a testing CI, it would also be helpful to have a benchmarking CI to track performance changes resulting from each commit to the main branch of the repository. Lux.jl had a very nice-looking benchmarking page, so I decided to look into their approach. They were using github-action-benchmark, a popular benchmarking action that integrates with the Julia package BenchmarkTools.jl. github-action-benchmark does two very useful things:

    -
      -

    1. Collects benchmarking data into a json file and provides a default index.html to display this data. If put inside a relative path in the gh-pages branch of a repository, this results in a public benchmarking page which is automatically updated after each commit!

      2. -

    2. Comments on a pull request with the benchmarking results compared with before the pull request. Example: https://github.com/JuliaHealth/KomaMRI.jl/pull/442#pullrequestreview-2213921334

      3. -
    -

    The only issue was that since github-action-benchmark is a github action, it is meant to be run within github by one of the available github runners. While this works for CPU benchmarking, only Buildkite has the CI setup for each of the GPU backends we are using, and Lux.jl’s benchmarks page only included CPU benchmarks, not GPU benchmarks (Note: we talked with Avik, the repository owner of Lux.jl, and Lux.jl has since adopted the approach outlined below to display GPU and CPU benchmarks together). I was not able to find any examples of other julia packages using github-action-benchmark for GPU benchmarking.

    -

    Fortunately, there is a tool someone developed to download results from Buildkite into a github action (https://github.com/EnricoMi/download-buildkite-artifact-action). This repository only had 1 star when I found it, but it does exactly what we needed: it identifies the corresponding Buildkite build for a commit, waits for it to finish, and then downloads the artifacts for the build into the github action it is being run from. With this, we were able to download the Buildkite benchmark results from a final aggregation step into our benchmarking action and upload to github-action-benchmark to publish to either the main data.js file for our benchmarking website, or pull request.

    -

    Our final benchmarking page looks like this and is publicly accessible:

    -

    -

    One neat thing about github-action-benchmark is that the default index.html is extensible, so even though by deault it only shows time, the information for memory usage and number of allocations is also collected into the json file, and can be displayed as well.

    -

    A successful CI run on Buildkite Looks like this:

    -

    -

    The pull requests for creating the CI testing and benchmarking pipeline, and changing the index.html for our benchmark page are listed below:

    -
      -
    1. https://github.com/JuliaHealth/KomaMRI.jl/pull/411
      2. -
    2. https://github.com/JuliaHealth/KomaMRI.jl/pull/418
      3. -
    3. https://github.com/JuliaHealth/KomaMRI.jl/pull/421
      4. -
    -
    -
    -

    Step 3: Optimization

    -

    With support for multiple backends enabled, and a robust CI, the next step was to optimize our simulation code as much as possible. Our original idea was to create a new GPU-optimized simulation method, but before doing this we wanted to look more at the existing code and optimize for the CPU.

    -

    The simulation code is solving a differential equation (the [Bloch equations(https://en.wikipedia.org/wiki/Bloch_equations)]) over time. Most differential equation solvers step through time, updating the current state at each time step, but our previous simulation code, more optimized for the GPU, did a lot of computations across all time points in a simulation block, allocating a matrix of size Nspins by NΞ”t each time this was done. Although this is beneficial for the GPU, where there are millions of threads available on which to parallelize these computations, for the CPU it is more important to conserve memory, and the aforementioned approach of stepping through time is preferable.

    -

    After seeing that this approach did help speed up simulation time on the CPU, but was not faster on the GPU (7x slower for Metal!) we decided to separate our simulation code for the GPU and CPU, dispatching based on the KernelAbstractions.Backend type depending on if it is <:KernelAbstractions.CPU or <:KernelAbstractions.GPU.

    -

    Other things we were able to do to speed up CPU computation time:

    -
      -

    1. Preallocating each array used inside the core simulation code so it can be re-used from one simulation block to the next.

      2. -

    2. Skipping an expensive computation if the magnetization at that time point is not added to the final signal

      3. -

    3. Ensuring that each statement is fully broadcasted. We were surprised to see the difference between the following examples:

      4. -
    -
    #Fast
-Bz = x .* seq.Gx' .+ y .* seq.Gy' .+ z .* seq.Gz' .+ p.Ξ”w ./ T(2Ο€ .* Ξ³)
-
-#Slow
-Bz = x .* seq.Gx' .+ y .* seq.Gy' .+ z .* seq.Gz' .+ p.Ξ”w / T(2Ο€ * Ξ³)
    -
      -
    1. Using the cis function for complex exponentiation, which is faster than exp
      2. -
    -

    With these changes, the mean improvement in simulation time aggregating across each of our benchmarks for 1, 2, 4, and 8 CPU threads was ~4.28. For 1 thread, the average improvement in memory usage was 90x!

    -

    The next task was optimizing the simulation code for the GPU. Although our original idea was to put everything into one GPU kernel, we found that the existing broadcasting operations were already very fast, and that custom kernels we wrote were not able to outperform the previous implementation. The Julia GPU compiler team deserves a lot of credit for developing such fast broadcasting implementations!

    -

    However, this does not mean that we were unable to improve the GPU simulation time. Similar to with the CPU, preallocation made a substantial difference. Parallelizing as much work as possible across the time points for a simulation block was also found to beneficial. For the parts that needed to be done sequentially, a custom GPU kernel was written which used the KernelAbstractions.@localmem macro for arrays being updated at each time step to yield faster memory access.

    -

    The mean speedup we saw across the 4 supported GPU backends was 4.16, although this varied accross each backend (for example, CUDA was only 2.66x faster while oneAPI was 28x faster). There is a remaining bottleneck in the run_spin_preceession! function having to do with logical indexing that I was not able to resolve, but could be solved in the future to speed up the GPU simulation time even further!

    -

    The pull requests optimizing code for the CPU and GPU are below:

    -
      -

    1. https://github.com/JuliaHealth/KomaMRI.jl/pull/443

      2. -

    2. https://github.com/JuliaHealth/KomaMRI.jl/pull/459

      3. -

    3. https://github.com/JuliaHealth/KomaMRI.jl/pull/462

      4. -
    -
    -
    -

    4. Step 4: Distributed Support

    -

    This last step was a stretch goal for exploring how to add distributed support to KomaMRI. MRI simulations can become quite large, so it is useful to be able to distribute work across either multiple GPUs or multiple compute nodes.

    -

    A nice thing about MRI simulation is the independent spin property: if a phantom object (representing, for example a brain tissue slice) is divided into two parts, and each part is simulated separately, the signal result from simulating the whole phantom will be equal to the sum of the signal results from simulating each subdivision of the original phantom. This makes it quite easy to distribute work, either across more than one GPU or accross multiple compute nodes.

    -

    The following scripts worked, with the only necessary code change to the repository being a new + function to add two RawAcquisitionData structs:

    -
    #Use multiple GPUs:
-using Distributed
-using CUDA
-
-#Add workers based on the number of available devices
-addprocs(length(devices()))
-
-#Define inputs on each worker process
-@everywhere begin
-    using KomaMRI, CUDA
-    sys = Scanner()
-    seq = PulseDesigner.EPI_example()
-    obj = brain_phantom2D()
-    #Divide phantom
-    parts = kfoldperm(length(obj), nworkers())
-end
-
-#Distribute simulation across workers
-raw = Distributed.@distributed (+) for i=1:nworkers()
-    KomaMRICore.set_device!(i-1) #Sets device for this worker, note that CUDA devices are indexed from 0
-    simulate(obj[parts[i]], seq, sys)
-end
    -
    #Use multiple compute nodes
-using Distributed
-using ClusterManagers
-
-#Add workers based on the specified number of SLURM tasks
-addprocs(SlurmManager(parse(Int, ENV["SLURM_NTASKS"])))
-
-#Define inputs on each worker process
-@everywhere begin
-    using KomaMRI
-    sys = Scanner()
-    seq = PulseDesigner.EPI_example()
-    obj = brain_phantom2D()
-    parts = kfoldperm(length(obj), nworkers())
-end
-
-#Distribute simulation across workers
-raw = Distributed.@distributed (+) for i=1:nworkers()
-    simulate(obj[parts[i]], seq, sys)
-end
    -

    Pull reqeust for adding these examples to the KomaMRI documentation: https://github.com/JuliaHealth/KomaMRI.jl/pull/468

    -
    -
    -

    Conclusions / Future Work

    -

    This project was a 350-hour large project, since there were many goals to accomplish. To summarize what changed since the beginning of the project:

    -
      -

    1. Added support for AMDGPU.jl, Metal.jl, and oneAPI.jl GPU backends

      2. -

    2. CI for automated testing and benchmarking accross each backend + public benchmarks page

      3. -

    3. Significantly faster CPU and GPU performance

      4. -

    4. Demonstrated distributed support and examples added in documentation

      5. -
    -

    Future work could look at ways to further optimize the simulation code, since despite the progress made, I believe there is more work to be done! The aforementioned logical indexing issue is still not resolved, and the kernel used inside the run_spin_excitation! function has not been profiled in depth. KomaMRI is also looking into adding support for higher-order ODE methods, which could require more GPU kernels being written.

    -
    -
    -

    Acknowledgements

    -

    I would like to thank my mentor, Carlos Castillo, for his help and support on this project. I would also like to thank Jakub Mitura, who attended some of our meetings to help with GPU optimization, Dilum Aluthge who helped set up our BuildKite pipeline, and Tim Besard, who answered many GPU-related questions that Carlos and I had.

    - - - - -
    - - Back to top

    Citation

    BibTeX citation:
    @online{kierulf2024,
-  author = {Kierulf, Ryan},
-  title = {GSoC ’24: {Enhancements} to {KomaMRI.jl} {GPU} {Support}},
-  date = {2024-08-30},
-  url = {https://juliahealth.org/JuliaHealthBlog/posts/ryan-gsoc/Ryan_GSOC.html},
-  langid = {en}
-}
-
    For attribution, please cite this work as:
    -Kierulf, Ryan. 2024. β€œGSoC ’24: Enhancements to KomaMRI.jl GPU -Support.” August 30, 2024. https://juliahealth.org/JuliaHealthBlog/posts/ryan-gsoc/Ryan_GSOC.html. -
    ]]>     - gsoc - mri - gpu - hpc - simulation - https://juliahealth.org/JuliaHealthBlog/posts/ryan-gsoc/Ryan_GSOC.html - Fri, 30 Aug 2024 04:00:00 GMT -     - - GSoC ’24: IPUMS.jl Small Project - Michela Rocchetti - https://juliahealth.org/JuliaHealthBlog/posts/michela-gsoc/Michela_JSoC.html - -

    Hello! πŸ‘‹

    -

    Hi! I am Michela, I have a Master’s degree in Physics of Complex Systems and I am currently working as a software engineer in Rome, where I am from. During my studies, I became interested in the use of modeling and AI methods to improve healthcare and how these tools can be used to better understand how cultural and social backgrounds influence the health of individuals. I am also interested in the computational modeling of the brain and the human body and its implications for a better understanding of certain pathological conditions.

    -

    With these motivations in mind, I heard about Google Summer of Code. Since I had studied Julia in some courses and given that the language is expanding rapidly, I decided to find a project within Julia. As a result, I found the project of Jacob Zelko (@TheCedarPrince) to start this experience.

    -
    -

    If you want to learn more about me, you can connect with me here: LinkedIn, GitHub

    -
    - -
    -

    Project Description

    -

    IPUMS is the β€œworld’s largest available single database of census microdata”, providing survey and census data from around the world. It includes several projects that provide a wide variety of datasets. The information and data collected by IPUMS are useful for comparative research, as well as for the analysis of individuals in their life contexts. These data can be used to create a more comprehensive dataset that will facilitate research on the social determinants of health for different types of diseases, social communities, and geographical areas.

    -

    -
    -

    To learn more about IPUMS, visit the website

    -
    -
    -
    -

    Tasks and Goals

    -

    The primary objectives of this proposal are to:

    -
      -

    1. Develop a native Julia package to interact with the APIs available around the datasets IPUMS provides.

      2. -

    2. Provide useful utilities within this package for manipulating IPUMS datasets.

      3. -

    3. Compose this package with the wider Julia ecosystem to enable novel research in health, economics, and more.

      4. -
    -

    To achieve this, the work was distributed as follows:

    -
      -
    1. Expand some of the functionality developed in ipumsr IPUMS NHGIS -
        -
      • Create a link between OpenAPI documentation and the functions internally used in IPUMS.jl: updating already present functions, determining if updating is needed, and testing them
        • -
      • Develop functionality similar to the get_metadata_nghis function present in ipumsr
        • -
      2. -
    2. Update IPUMS documentation -
        -
      • Set up and deploy DocumenterVitepress.jl
        -
        • -
      • Write a blog post on how IPUMS.jl can be composed within the ecosystem.
        • -
      3. -
    -
    -
    -

    How the work was done

    -

    The first task was to migrate documents from Documenter to DocumenterVitepress.This issue aims to support the significant refactoring underway across JuliaHealth, aimed at improving the discoverability and cohesion of the JuliaHealth ecosystem, particularly about documentation. This issue is intended to create a more attractive entry point for new Julia users interested in health research within the Julia community. To accomplish this task, a dependency of DocumenterVitepress was added to the docs directory of the IPUMS.jl repository. Once this was done, the Documenter.jl make.jl file was migrated into a DocumenterVitepress.jl make.jl file. Working on the make.jl file, the pages structure were added to the web page explaining the IPUMS.jl package. With this in mind, those were added: 1. Home: to explain the main purpose of the package 2. Workflows: to explain the working process 3. How to: to give general information 4. Tutorials: to show how to use IPUMS.jl
    -5. Examples: some examples of activities 6. Mission: to explain why the package is useful for the community 7. References: references used to write the pages.

    -

    This first task takes some time, especially setting up GitHub and cloning the repository locally. At this point, my experience with GitHub was really limited and I had to learn how to use the Git environment from scratch, for example how to do continuous integration (to commit code to a shared repository), documentation release and merge, and local testing. I found the support of my mentors and searching for material online was really helpful.

    -

    The second task was to update the documentation of IPUMS.jl by modifying the functionality within the model folder in the IPUMS.jl folder. The main aim of this task was to a description of the function and its attributes, an example of possible implementation and result, and finally to show how to use it. The documentation to be updated as of several types of functions: 1. Data extract 2. Data set 3. Data Table 4. Time series table 5. Error 6. Shapefile. Each of these macro-categories (from 1 to 4) contains a set of functions, each signaling the different expected output and specific purpose. Information about what each function does, and the meaning of each specific input variable, has been found on the IPUMS website and references have been made in the written documentation.

    -
    -
    -

    How to work with IPUMS

    -

    After writing down the description of the function and the inputs, examples were formulated, starting from the IPUMS website: when you register at IPUMS, an API key is given. which is used, among other things, to run pre-written code on the website. This code contains examples of these functions, and these examples have been adapted by changing some input values and adapting them to work in the Julia framework. The latter task was done by simply rewriting some structures, such as dictionaries, maps, or lists, in the Julia language. Here is a small guide on how to set up working with the API: 1. Create an IPUMS account 2. Log in to your account 3. Copy the API key, which can be obtained from the website 4. Use the key to run the code that is already available on the IPUMS Developer Portal, where you will also find information about the variables and packages.

    -
    -
    -

    Functions testing

    -

    A final task was to test the functions in the β€˜api_IPUMSAPI.jl’ file. In this file, the function to be tested and other functions are defined and the most important ones are extracted to be available in the available throughout the framework. Some of the functions to be tested were the following:

    -
      -
    1. metadata_nhgis_data_tables_get
      2. -
    2. metadata_nhgis_datasets_dataset_data_tables_data_table_get
      3. -
    3. metadata_nhgis_datasets_dataset_get
      4. -
    4. metadata_nhgis_datasets_get
      5. -
    -

    Before working on the Julia files, testing and understanding the original R function was done using R studio.

    -

    -

    Each function was then tested using the API key from the IPUMS registration as well as other input examples taken from the documentation or the IPUMS website. or from the IPUMS website. All functions were displayed successfully, giving the expected result, so it can be concluded that the translation from R to Julia is successful.

    -
    -
    -Code -
    using IPUMS
-using OpenAPI
-
-api_key = "insert your key here"
-
-version = "2"
-page_number = 1
-page_size = 2500
-#media_type = 
-
-api = IPUMSAPI("https://api.ipums.org", Dict("Authorization" => api_key));
-
-res1 = metadata_nhgis_data_tables_get(api, version)
-
-res2 = metadata_nhgis_datasets_dataset_get(api, "2022_ACS1", "2");
-
-res3 = metadata_nhgis_datasets_dataset_data_tables_data_table_get(api, "2022_ACS1","B01001", "2");
-
-res4 = metadata_nhgis_datasets_get(api, "2");
    -
    -
    -

    An example of the output is:

    -
    . . .
-
-{
-  "name": "NT1",
-  "nhgisCode": "AAA",
-  "description": "Total Population",
-  "universe": "Persons",
-  "sequence": 1,
-  "datasetName": "1790_cPop",
-  "nVariables": [
-    1
-  ]
-}
-
-. . .
    -
    -
    -

    Accomplished Goals and Future Development

    -

    The project was a 90-hour small project and during this time the documentation was completed and the testing of the metadata function was done, as well as the migration from Documenter.jl to DocumenterVitepress.jl. During these months some things took longer than I expected because of some problems that occurred, so some things were missing in relation to the original plan. However, this time was useful for learning new things: - I saw how to work with a package under development, how to work with large datasets, and how to write documentation - I had the opportunity to better understand how to work with Git and GitHub - I learned some new things about R, which was a completely unknown language to me. - I deepened my knowledge of Julia, a language I had worked with during my time at university. - I had the chance to work on a large open-source project, to be part of a large community, and to learn how to communicate with it efficiently.

    -

    A special thanks goes to my mentors, Jacob Zelko and Krishna Bhogaonker, for helping me through this process.

    -

    Future developments of this work could include deepening the work that my mentors and I have started, with the possibility of integrating this package with other machine learning packages in Julia and, from there, doing new analyses of the data in terms of social and geographical implications for health.

    - - - - -
    - - Back to top

    Citation

    BibTeX citation:
    @online{rocchetti2024,
-  author = {Rocchetti, Michela},
-  title = {GSoC ’24: {IPUMS.jl} {Small} {Project}},
-  date = {2024-08-26},
-  url = {https://juliahealth.org/JuliaHealthBlog/posts/michela-gsoc/Michela_JSoC.html},
-  langid = {en}
-}
-
    For attribution, please cite this work as:
    -Rocchetti, Michela. 2024. β€œGSoC ’24: IPUMS.jl Small -Project.” August 26, 2024. https://juliahealth.org/JuliaHealthBlog/posts/michela-gsoc/Michela_JSoC.html. -
    ]]>     - gsoc - geospatial - census - https://juliahealth.org/JuliaHealthBlog/posts/michela-gsoc/Michela_JSoC.html - Mon, 26 Aug 2024 04:00:00 GMT -     - - Dummy Post - Foobar - https://juliahealth.org/JuliaHealthBlog/posts/dummy/ - -

    Seciton 1

    -

    Small dummy blog post

    -
    -
    -Code -
    2 + 2
    -
    -
    -
    4
    -
    -
    -
    -
    -Code -
    println(2 + 2)
    -
    -
    -
    4
    -
    -
    - -
    -

    Section 2

    -
    -
    -

    Section 3

    - - - - -
    - - Back to top

    Citation

    BibTeX citation:
    @online{2024,
-  author = {, Foobar},
-  title = {Dummy {Post}},
-  date = {2024-06-22},
-  url = {https://juliahealth.org/JuliaHealthBlog/posts/dummy/},
-  langid = {en}
-}
-
    For attribution, please cite this work as:
    -Foobar. 2024. β€œDummy Post.” June 22, 2024. https://juliahealth.org/JuliaHealthBlog/posts/dummy/. -
    ]]>     - news - code - analysis - https://juliahealth.org/JuliaHealthBlog/posts/dummy/ - Sat, 22 Jun 2024 04:00:00 GMT -     -     -     diff --git a/docs/listings.json b/docs/listings.json index 152f947..c58dc0b 100644 --- a/docs/listings.json +++ b/docs/listings.json @@ -1,14 +1,8 @@ [ { - "listing": "/index.html", + "listing": "/blog/index.html", "items": [ - "/posts/JZubik-gsoc/GSoC_Jan_Zubik_MedPipe3D.html", - "/posts/divyansh-gsoc/gsoc-2024-fellows.html", - "/posts/mounika-gsoc-mentor/index.html", - "/posts/jay-gsoc/gsoc-2024-fellows.html", - "/posts/ryan-gsoc/Ryan_GSOC.html", - "/posts/michela-gsoc/Michela_JSoC.html", - "/posts/dummy/index.html" + "/blog/posts/dummy/index.html" ] } ] \ No newline at end of file diff --git a/docs/posts/dummy/index.html b/docs/pages/connect_with_us.html similarity index 52% rename from docs/posts/dummy/index.html rename to docs/pages/connect_with_us.html index 60320bf..e088973 100644 --- a/docs/posts/dummy/index.html +++ b/docs/pages/connect_with_us.html @@ -2,15 +2,12 @@ - + - - - -Dummy Post – The JuliaHealth Blog +Connect With Us – JuliaHealth - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + - - - - - - + @@ -117,11 +81,8 @@ @@ -195,60 +131,22 @@
    - diff --git a/docs/pages/meeting_notes.html b/docs/pages/meeting_notes.html new file mode 100644 index 0000000..a4fcf19 --- /dev/null +++ b/docs/pages/meeting_notes.html @@ -0,0 +1,2506 @@ + + + + + + + + + +Meeting Notes – JuliaHealth + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + +
    + +
    + + + + +
    + +
    +
    +

    Meeting Notes

    +
    + + + +
    + + + + +
    + + + +
    + + +

    These are the public notes for the JuliaHealth Community. Notes are published publicly here and are available for comments and review on the public HackMD. Additionally, the notes are hosted publicly on the GitHub and are open for PRs or edits as needed

    +
    +

    February 29 2024

    +
    +

    Meeting Summary (Americas/Europe/Africa Specific)

    +

    In Attendance: Jacob Zelko, Anshul Singhvi, Adam WysokiΕ„ski, Aurora Rossi, Dan Getz, Luna Fazio, Jay Landge, Edwin Mkwanazi, Alice Piller, Thembi Ndimande, Siyabonga Nxumalo, Hlengiwe, Muhammad Mahmoud, Jan Zubik, Sfundo Khumalo, Carlos Castillo Passi, Ram Samarth, Dina Khalid

    +

    Location: Virtual (Northeastern University Zoom)

    +

    Summary: Introducing new JuliaHealth projects, JuliaHealth blog, Google Summer of Code, and planning a JuliaHealth Day

    +

    Keywords: #juliahealth #meeting #americas #africa #europe #neuro #imaging #gsoc #planning

    +
    +
    +

    Meeting Outcomes

    +
    +

    Short-Term Outcomes

    +
      +
    • Jacob follows up with Carlos and Boris about synthetic MRI generation
      • +
    +
    +
    +

    Long-Term Outcomes

    +
    +
    +
    +

    Notes

    +
      +
    1. Announcements:
      2. +
    +
      +
    • Meeting recording logistics
      • +
    +
      +
    1. New member introductions +
        +
      • Luna Fazio +
          +
        • Statistics PhD
          • +
        • Coming back to epidemiology
          • +
        • Coming back to health roots
          • +
        • +
      • Adam WysokiΕ„ski +
          +
        • Creator of NeuroAnalyzer.jl
          • +
        • Psychiatrist
          • +
        • Many different modalities of research
          • +
        • +
      • Aurora Rossi +
          +
        • Functional MRI
          • +
        • PhD student
          • +
        • +
      • Alice Piller +
          +
        • Applying Julia in bioinformatics
          • +
        • +
      • Edwin Mkwanazi +
          +
        • Julia in clinical trials
          • +
        • Learn more about how to implement more in Julia
          • +
        • +
      • Carlos Castillo +
          +
        • Creator of KomaMRI
          • +
        • PhD student
          • +
        • +
      2. +
    2. New contributor round-up! +
        +
      • KomaMRI
        • +
      • NeuroAnalyzer Adam Wysokinski
        • +
      3. +
    3. JuliaHealth News +
        +
      • Northeastern University RISE Conference
        • +
      • A JuliaHealth Blog?!?!?
        • +
      4. +
    4. Task Follow-ups +
        +
      • Jacob follows up with Carlos and Boris about synthetic MRI generation
        • +
      5. +
    5. GSoC + JuliaHealth +
        +
      • Projects
        • +
      • Important dates
        • +
      • Open discussion
        • +
      6. +
    6. Brainstorming a JuliaHealth Day +
        +
      • JuliaHealth is growing rapidly!!!
        • +
      • Might be confusing about where to go/get started +
          +
        • Three core ares
          • +
        • +
      • Luna +
          +
        • Had a mixture of working with different data
          • +
        • Public health approach
          • +
        • I as a doctor want to predict for patients
          • +
        • Perhaps it would be interesting to see what problems they have +
            +
          • Possible approaches
            • +
          • +
        • +
      • Jan +
          +
        • More about pipelines
          • +
        • What’s their strength in practice
          • +
        • Seeing pipelines in action
          • +
        • +
      • Ram +
          +
        • How is Julia being used in health already?
          • +
        • +
      7. +
    7. Glass Notebooks +
        +
      • Created by Dale Black
        • +
      • Link: https://glassnotebook.io
        • +
      8. +
    8. Upcoming and ongoing research opportunities +
        +
      • Observational Health Research at Northeastern Uni
        • +
      9. +
    9. Upcoming Events
      10. +
    +
      +
    • JuliaCon 2024
      • +
    +
      +
    1. Open Discussion
      2. +
    +
    +
    +
    +

    January 25 2024

    +
    +

    Meeting Summary (Americas/Europe/Africa Specific)

    +

    In Attendance: Jay Sanjay, Abhirath Anand, Carlos Castillo, Boris Enrique, Jacob Zelko

    +

    Location: Virtual (JuliaHealth Google Meet)

    +

    Summary: Medical imagining, fairness and health equity in observational health, and dashboards!

    +

    Keywords: #juliahealth #meeting #americas #africa #europe #fairness #koma #fairness #dashboards

    +
    +
    +

    Meeting Outcomes

    +
    +

    Short-Term Outcomes

    +
      +
    • Jacob follows up with Carlos and Boris about synthetic MRI generation +
        +
      • Pulls in Jakub and Zachary to discussion
        • +
      • +
    +
    +
    +

    Long-Term Outcomes

    +
    +
    +
    +

    Notes

    +
      +

    1. New member introductions

      +
        +

      • Carlos Castillo

        +
          +

        • King’s College London

          • +

        • PhD student

          • +
        • +

      • Abhirath Anand

        +
          +
        • Final year undergraduate
          • +
        • Curious about getting more into life sciences
          • +
        • Biology and healthcare
          • +
        • +
      2. +

    2. Announcements:

      +
        +
      • New meeting times +
          +
        • Last Thursday of every month at 12PM EST
          • +
        • +
      • Why two separate meetings? +
          +
        • One for Asia/Oceania +
            +
          • Thanks Jay Sanjay for running this!!!
            • +
          • +
        • One for Americas/Africa/Europe
          • +
        • Trying to improve accessibility and inclusion
          • +
        • +
      • Meeting recordings +
          +
        • Going forward, meetings will be recorded
          • +
        • Added to a playlist on Julia YouTube page
          • +
        • +
      3. +

    3. New contributor round-up!

      +
        +
      • Nothing this meeting
        • +
      4. +

    4. Running tasks follow-ups:

      +
        +
      • Nothing this meeting
        • +
      5. +

    5. Presentation by Carlos Castillo Passi on GSoC projects on medical imaging.

      6. +
    +
      +
    • Written using CuDA
      • +
    • Doing MRI simulation very quickly +
        +
      • Can be used for machine learning overview
        • +
      • +
    • Built around several packages with MRI +
        +
      • Incredible work with coverage
        • +
      • +
    • Super friendly GUI
      • +
    • Bloch equations are hard to understand
      • +
    • GSoC Project +
        +
      • Trying to do actual kernel programming
        • +
      • KernelAbstractions.jl
        • +
      • Solving DifferentialEquations.jl
        • +
      • Boost speed
        • +
      • Implement new algorithms
        • +
      • Suggested skills +
          +
        • Experience with Julia
          • +
        • MRI concepts
          • +
        • GPU programming
          • +
        • +
      • Goals: +
          +
        • New Bloch kernel methods
          • +
        • Further tests on build kite/GPU testing
          • +
        • Documentation
          • +
        • +
      • +
    +
      +

    1. Fairness and health equity within Observational Health Research

      +
        +

      • Assessing phenotype fairness

        • +

      • Forthcoming package

        • +

      • Work done so far

        • +

      • Paper reference: https://arxiv.org/pdf/2203.05174.pdf

        • +
      2. +

    2. Creating dashboards for JuliaHealth

      +
        +

      • Announcement from Genie.jl

        • +

      • Custom dashboard components

        • +

      • Question: What would this look like for JuliaHealth?

        +
          +
        • Create a standard interface across JuliaHealth packages
          • +
        • Can interface with a JuliaHealthDashboards package +
            +
          • HealthDashboard.jl?
            • +
          • Custom components for the general JuliaHealth ecosystem could be housed in package
            • +
          • +
        • Researchers can easily build together commonly used health dashboards
          • +
        • +
      3. +

    3. Event Reminders

      +
        +

      • Google Summer of Code

        • +

      • JuliaCon 2024

        • +
      4. +

    4. Upcoming and ongoing research opportunities

      +
        +

      • Observational Health Research at Northeastern Uni

        • +

      • Glass Notebooks from Dale Black (Not Discussed; saved for next month)

        • +
      5. +
    +
    +
    +
    +

    January 20 2024

    +
    +

    Meeting Summary (Oceania/Asia specific)

    +

    In Attendance: Jay Sanjay, Abhirath Anand, Jacob Zelko

    +

    Location: Virtual (JuliaHealth Google Meet)

    +

    Summary: Overview of the Oceania/Asia specific JuliaHealth monthly meeting

    +

    Keywords: #juliahealth #meeting #asia #oceania #llms #beginner

    +
    +
    +

    Meeting Outcomes

    +
    +

    Short-Term Outcomes

    +
    +
    +

    Long-Term Outcomes

    +
    +
    +
    +

    Meeting Notes

    +
      +

    • Abhirath Anand

      +
        +

      • Final year CS student in India.

        • +

      • Former GSoCer.

        +
          +

        • Worked on MetalHead.jl

          • +

        • No longer quite interested in Computer CV

          • +
        • +

      • Interested in JuliaHealth.

        • +
      • +

    • People excited about separate JuliaHealth meeting

      +
        +

      • Grown to a separate JuliaHealth meeting for Oceania/Asia specific times.

        • +

      • Wanted more people to join .

        • +
      • +

    • Different packages and ideas

      +
        +

      • JuliaHealthLLMs

        +
          +
        • How can we use LLMs for JuliaHealth?
          • +
        • +
      • +

    • How to get started with JuliaHealth - Abhirath

      +
        +

      • Medical imaging looks well-aligned but want to explore some different.

        • +

      • What is the observational health subecosystem?

        +
          +

        • Go through documentation of JuliaHealth.

          • +

        • Jay can send some.

          • +
        • +
      • +
    +
    +
    +
    +

    December 15 2023

    +

    In Attendance: Jacob Zelko, Jay Sanjay, Jakub Mitura, Zach Christensen, Divital coder

    +

    Location: Virtual (JuliaHealth Google Meet)

    +

    Summary: JuliaHealth full year review, Dicsussions on the upcoming GSoC projects in JuliaHealth.

    +

    Keywords: #medical #imaging #gsoc #ohdsi #newyear #observationalHealth

    +
    +

    Agenda

    +
      +
    1. New member introductions
      2. +
    2. New contributor round-up!
      3. +
    3. Running tasks follow-ups:
      4. +
    4. State of the JuliaHealth community discussion +
        +
      • Talking about the different aspects of the JuliaHealth community +
          +
        • Mapping the JuliaHealth community
          • +
        • +
      • Accomplishments throughout the year +
          +
        • JuliaCon 2023
          • +
        • GSoC
          • +
        • Publications/etc.
          • +
        • +
      • Open Problems and ongoing work +
          +
        • Technical problems
          • +
        • Making JuliaHealth more accessible for all
          • +
        • +
      • Future goals for the JuliaHealth ecosystem
        • +
      • Open discussion
        • +
      5. +
    5. JuliaCon 2024!
      6. +
    6. Google Summer of Code Discussion +
        +
      • What it is
        • +
      • Proposed projects and ideas
        • +
      • Open discussion
        • +
      7. +
    7. Calls for collaboration
      8. +
    8. Open discussion
      9. +
    +
    +
    +

    Meeting Outcomes

    +
    +

    Short-Term Outcomes

    +
      +
    • Jacob follows-up with Zach.
      • +
    +
    +
    +

    Long-Term Outcomes

    +
      +
    • Increasing code ownership.
      • +
    +
    +
    +
    +

    Notes

    +
      +
    1. Introductions +
        +
      • Divital coder +
          +
        • Aspiring contributor for the 2024 Julia Organization.
          • +
        • +
      2. +
    2. Contributor Round-Up +
        +
      • Shout outs to Farreeda for working on JuliaHealth Observational Health Sub-ecosystem Juliacon proceddings paper.
        • +
      • Shout outs to Jay-Sanjay for tagging new release of OMOPCDMCohortCreator.
        • +
      3. +
    3. State of the JuliaHealth community discussion +
        +
      • Talking about the different aspects of the JuliaHealth community +
          +
        • Mapping the JuliaHealth community
          • +
        • +
      • Accomplishments throughout the year +
          +
        • JuliaCon 2023 +
            +
          • Birds of Feather: Julia for Health and Medicine – Dilum Aluthge, Jacob Zelko
            • +
          • 100 Million Patients: Julia for international Health studies
            • +
          • +
        • First ever JuliaHealth GSoC fellow - Fareeda Abdelazeez
          • +
        • ODHSI Global Symposium 2023
          • +
        • +
      • Open Problems and ongoing work +
          +
        • Technical problems
          • +
        • Making JuliaHealth more accessible for all
          • +
        • Future goals for the JuliaHealth ecosystem
          • +
        • Expanding the OMOPCDM for hospital price transparency and transparency coverage.
          • +
        • +
      • Open discussion +
          +
        • Open discussion on standards across JuliaHealth
          • +
        • Zach happy to support and think around this
          • +
        • Schedule one-off discussion
          • +
        • Making juliahealth calls more Europe+asia/pacific friendly. Suggestions to have a one meet each for american time zone separate and one for asia/pacific time zone
          • +
        • +
      4. +
    4. JuliaCon 2024! +
        +
      • Proposal-a-thon
        • +
      5. +
    5. Google Summer of Code Discussion +
        +
      • What is GSoC/JSoC ?
        • +
      • Proposed projects and ideas
        • +
      • MedPipe3D +
          +
        • Loading medical imaging data
          • +
        • Modeling perspective most generally developed
          • +
        • Super-voxels image mapping
          • +
        • Edge matching; can make this code within Julia vs. Cpp
          • +
        • Display borders of images
          • +
        • Integrate segmentation like rotations recalling gamma.
          • +
        • Add basic post-processing like largest corrected components.
          • +
        • Add patch based data loading with probabilistic oversampling.
          • +
        • +
      • Open discussion
        • +
      6. +
    6. Calls for collaboration
      7. +
    7. Open discussion +
        +
      • JuliaCon 2024 and Proposal-a-thon
        • +
      • Addressing the β€œParadox of Composition”
        • +
      8. +
    +
    +
    +
    +

    October 27 2023

    +

    In Attendance: Jakub Mitura, Phil Vernes, Jay Sanjay

    +

    Location: Virtual (JuliaHealth Google Meet)

    +

    Summary: Jakub Mitura presented on his work for MedEval3D, discussion on medical imaging, debrief from the OHDSI Symposium, and some initial conversation about GSoC

    +

    Keywords: #medical #imaging #gsoc #ohdsi

    +
    +

    Agenda

    +
      +
    1. New member introductions
      2. +
    2. New contributor round-up!
      3. +
    3. Running tasks follow-ups:
      4. +
    +
      +
    • Short-term task follow-ups: +
        +
      • Jacob shares info on waste water management + viral load information
        • +
      • +
    • Long-term task follow-ups: +
        +
      • Creating a template repository
        • +
      • +
    +
      +
    1. Presentation by Jakub Mitura on sub-ecosystem he created for working with CT, PET, and other medical imaging types of data.
      2. +
    2. Debrief from OHDSI Symposium (Observational Health research venue)
      3. +
    3. Google Summer of Code Project Discussion
      4. +
    +
      +
    • JuliaHealth documentation improvement
      • +
    • Observational Health Tooling improvements and discussion
      • +
    • Visualization tools
      • +
    +
      +
    1. Upcoming and ongoing research opportunities
      2. +
    +
      +
    • Call for collaboration on using JuliaHealth observational health tools for multi-site study
      • +
    +
      +
    1. Medical Imaging Extension for Real World Evidence exploration
      2. +
    2. Open discussion
      3. +
    +
    +
    +

    Meeting Outcomes

    +
    +

    Short-Term Outcomes

    +
      +
    • Jacob intro’s Phil and Jakub
      • +
    • Jacob follows-up with Phil
      • +
    +
    +
    +

    Long-Term Outcomes

    +
      +
    • Create a template repository for JuliaHealth
      • +
    +
    +
    +
    +

    Notes

    +
      +
    • New member introductions +
        +
      • Phil Vernes +
          +
        • Works at JuliaHub
          • +
        • Developing platform for running Julia jobs
          • +
        • Many people at JuliaHub using tools within epi
          • +
        • Can solve many problems in DSL
          • +
        • +
      • Jay Sanjay +
          +
        • Started contributing to the JuliaHealth ecosystem
          • +
        • Looking forward to collaborating
          • +
        • +
      • +
    • Running tasks follow-ups: +
        +
      • Short-term task follow-ups: +
          +
        • Jacob shares info on waste water management + viral load information
          • +
        • +
      • Long-term task follow-ups: +
          +
        • Creating a template repository +
            +
          • We need to have a data structure to hold metadata (DICOM, NIFTI, etc.)
            • +
          • JuliaNeuro
            • +
          • HDF5 for long-term storage +
              +
            • Would be great to see everyone using this
              • +
            • To work on this to bring this together
              • +
            • Multiple packages could have same
              • +
            • +
          • +
        • +
      • +
    • Presentation by Jakub Mitura on sub-ecosystem he created for working with CT, PET, and other medical imaging types of data. +
        +
      • Created three packages
        • +
      • Mainly talking about MedEye3D
        • +
      • Segment data and iterate to see what is going on
        • +
      • Wanted to create tools for everything around model creation
        • +
      • Wanted to make a viewer that is well-suited for the Julia ecosystem +
          +
        • Most medical viewers are quite β€œold”
          • +
        • Not really dynamic
          • +
        • Hard to show changes within run-time
          • +
        • +
      • Easy to get big increase in Julia +
          +
        • Usually something like 10x’s faster
          • +
        • +
      • We do not yet standardize way to load data
        • +
      • Metadata is saved to HDF5 format
        • +
      • Can introduce dynamic annotations
        • +
      • Can have layers and switch on and switch layers
        • +
      • Can annotate for saying where is the problem in the viewer
        • +
      • Viewer can dynamically update
        • +
      • Questions +
          +
        • Tested some semi-automatic algorithms
          • +
        • Do evaluate repeat
          • +
        • Makes it faster for evaluation and reviewing of medical images
          • +
        • Depends on OpenGL and NVIDIA drivers
          • +
        • Working on Docker container that keeps
          • +
        • What segmentation algorithm? Approach? +
            +
          • Based on Gaussian probability distributions
            • +
          • Some relaxation applied
            • +
          • Based mainly on the units and different kinds
            • +
          • Becoming more interested in transformers
            • +
          • Implemented in JAX but want to bring it into Julia
            • +
          • Segmentation for bladder cancer in image analysis
            • +
          • Restarted work recently in Julia
            • +
          • +
        • Would be useful for others? +
            +
          • New segmentation for other ecosystem within Julia
            • +
          • +
        • +
      • +
    • Upcoming and ongoing research opportunities
      • +
    • Call for collaboration on using JuliaHealth observational health tools for multi-site study
      • +
    • Medical Imaging Extension for Real World Evidence exploration +
        +
      • Idea was to implement package for medical imaging
        • +
      • Pillars +
          +
        • Computing statistics across medical imaging
          • +
        • Complete datasets for experimenting
          • +
        • Feature segmentation and scanning +
            +
          • Align probabilistic model between different scans
            • +
          • Become easier for physicians
            • +
          • +
        • ML model for complex models for image segmentation
          • +
        • +
      • Thing to consider – need more robustness for image alignment? +
          +
        • Some transformations are relatively easier to repair
          • +
        • Elastic deformations
          • +
        • +
      • +
    +
    +
    +
    +

    September 29 2023

    +

    In Attendance: Tiem van der Deure, Scott Jones, dx/dt

    +

    Location: Virtual (JuliaHealth Google Meet)

    +

    Summary: Discussion on viral load found in wastewater, GSoD for this fall/GSoC for next summer, and upcoming research opportunities and events

    +

    Keywords: #databases #wastewater #interfaces #gsoc #ohdsi

    +
    +

    Agenda

    +
      +

    1. New member introductions

      2. +

    2. Running tasks follow-ups:

      3. +
    +
      +

    1. Short-term task follow-ups:

      2. +

    2. Long-term task follow-ups:

      3. +
    +
    i. Creating a template repository
    +
      +

    1. Infectious Disease load for various sewage water data

      2. +

    2. Upcoming research opportunities and events

      3. +
    +
      +

    1. Not too early to start thinking about GSoC

      2. +

    2. Julia and OHDSI Symposium

      3. +
    +
      +
    1. Open discussion
      2. +
    +
    +
    +

    Meeting Outcomes

    +
    +

    Short-Term Outcomes

    +
      +
    • Jacob shares info on waste water management + viral load information
      • +
    +
    +
    +

    Long-Term Outcomes

    +
    +
    +
    +

    Notes

    +
      +
    • New member introductions +
        +
      • Tiem van der Deure +
          +
        • University of Copenhagen PhD
          • +
        • Vector-borne Disease Modeling
          • +
        • Epidemiological modeling and climate effects on health
          • +
        • Rafael Schoueten
          • +
        • +
      • Scott Jones +
          +
        • Heavily involved in healthcare IT
          • +
        • +
      • dx/dt
        • +
      • +
    • Google Summer of Code +
        +
      • Didn’t know it existed
        • +
      • Google Season of Docs is great too +
          +
        • Best for long-term maintenance
          • +
        • In the Julia docs ecosystem is kinda a mess
          • +
        • +
      • +
    • OHDSI + Julia +
        +
      • How difficult it has been to work with EHR from EPIC +
          +
        • Still a bit manual but getting better
          • +
        • +
      • Turing modeling β€œmaking them work” +
          +
        • Getting them to run +
            +
          • Making it run fast enough
            • +
          • Much easier to use but not as fast as otherwise
            • +
          • +
        • Extremely mathy very fast
          • +
        • +
      • +
    • Sewage water information for disease population estimations +
        +
      • Weekly excerpt
        • +
      • Infectious disease doctor +
          +
        • Would be really neat to make some kind of app
          • +
        • To check wastewater +
            +
          • Propensity of viruses in ER
            • +
          • +
        • +
      • Physician testing for rough understanding of what is happening in community +
          +
        • You don’t just need to look for one disease, but rather multiple co-factors
          • +
        • +
      • Many healthcare systems put together monitoring systems +
          +
        • NHS (in UK) dismantled their monitoring systems
          • +
        • +
      • +
    +
    +
    +
    +

    September 29 2023

    +

    In Attendance: Tiem van der Deure, Scott Jones, dx/dt

    +

    Location: Virtual (JuliaHealth Google Meet)

    +

    Summary: Discussion on viral load found in wastewater, GSoD for this fall/GSoC for next summer, and upcoming research opportunities and events

    +

    Keywords: #databases #wastewater #interfaces #gsoc #ohdsi

    +
    +

    Agenda

    +
      +

    1. New member introductions

      2. +

    2. Running tasks follow-ups:

      +
        +

      1. Short-term task follow-ups:

        2. +

      2. Long-term task follow-ups:

        +
          +
        • Creating a template repository
          • +
        3. +
      3. +

    3. Upcoming research opportunities and events

      +
        +

      1. Not too early to start thinking about GSoC

        2. +

      2. Julia and OHDSI Symposium

        3. +
      4. +

    4. Infectious Disease load for various sewage water data

      5. +

    5. Open discussion

      6. +
    +
    +
    +

    Meeting Outcomes

    +
    +

    Short-Term Outcomes

    +
      +
    • Jacob shares info on waste water management + viral load information
      • +
    +
    +
    +
    +

    Notes

    +
      +
    • Introductions +
        +
      • Tiem van der Deure +
          +
        • University of Copenhagen PhD
          • +
        • Vector-borne Disease Modeling
          • +
        • Epidemiological modeling and climate effects on health
          • +
        • Rafael Schoueten
          • +
        • +
      • Scott Jones +
          +
        • Heavily involved in healthcare IT
          • +
        • +
      • dx/dt
        • +
      • +
    • Google Summer of Code +
        +
      • Recently discovered by the team
        • +
      • Google Season of Docs +
          +
        • Best for long-term maintenance
          • +
        • Significant challenge organizing in Julia docs ecosystem
          • +
        • +
      • +
    • OHDSI + Julia +
        +
      • Working with EHR from EPIC is demanding +
          +
        • Labour intensive albeit improving
          • +
        • +
      • Turing modeling β€œmaking them work” +
          +
        • Getting them to run +
            +
          • Making it run fast enough
            • +
          • Trade off ease-of-use for computation speed
            • +
          • +
        • Requires significant mathematical ability for speed gains
          • +
        • +
      • +
    • Sewage water information for disease population estimations +
        +
      • Weekly excerpt
        • +
      • Infectious disease doctor +
          +
        • Would be really neat to make some kind of app to check wastewater +
            +
          • Propensity of viruses in ER
            • +
          • +
        • +
      • Physician testing for rough understanding of what is happening in community +
          +
        • Ability to look for multiple co-factors instead of just one disease
          • +
        • +
      • Many healthcare systems put together monitoring systems +
          +
        • NHS (in UK) dismantled their monitoring systems
          • +
        • +
      • +
    • Databases and JuliaHealth +
        +
      • Show how to do the basics
        • +
      • Common database errors +
          +
        • How to address them
          • +
        • +
      • Consider having more people working in this space?
        • +
      • Not really a problem within ecosystem
        • +
      • Look at drivers across all packages to see how things work in Julia ecosystem +
          +
        • See how we can address issues across ecosystem
          • +
        • +
      • +
    +
    +
    +
    +

    August 25 2023

    +

    In Attendance: Edmund Miller, Jonathan Starr, Clark Evans, Kirill Simonov, Jacob Zelko

    +

    Location: Virtual (JuliaHealth Google Meet)

    +

    Summary: Recap of events from the JuliaHealth BoF at JuliaCon and introduction to the NumFOCUS OSSci project

    +

    Keywords: #numfocus #ossci #juliacon #bof #interoperability #databases #documentation

    +
    +

    Agenda

    +
      +

    1. New member introductions

      2. +

    2. Misc Announcements

      +
        +

      1. CalciumScoring.jl – Dale Black

        2. +

      2. Survival Analyses – Arin Basu

        3. +

      3. Google Summer of Code Fellowship wrapping up

        4. +

      4. We are on the Julia Community Calendar!

        5. +

      5. Small updates to the JuliaHealth website

        6. +
      3. +

    3. Running tasks follow-ups:

      +
        +

      1. Short-term task follow-ups:

        +
          +

        • @Jacob Set-up HackMD to take notes going forward

          +
            +
          • Copy and paste meeting minutes over to JuliaHealth PR to update at end of meetings
            • +
          • +
        2. +

      2. @Dilum finds out how to live stream JuliaHealth BoF

        +
          +
        • Long-term task follow-ups:
          • +
        3. +

      3. Creating a template repository 

        4. +
      4. +

    4. Debrief from JuliaCon

      +
        +

      1. Interoperability of Julia with health research ecosystems (R )

        2. +

      2. Develop and document tutorials showcasing compositional solutions to JuliaHealth ecosystem problems

        3. +

      3. Coordinate with bigger Julia Blog to bridge between communities even better

        4. +

      4. Databases and JuliaHealth

        5. +
      5. +

    5. Jon Starr and NumFOCUS’s OSSci Program

      6. +

    6. Open discussion on next steps for the JuliaHealth community

      7. +
    +
    +
    +

    Meeting Outcomes

    +
    +

    Short-Term Outcomes

    +
      +
    • @Jacob follow-up with Jonathan about JuliaHealth + OSSci
      • +
    • @Edmund let Jacob know about blog posts solving problems
      • +
    +
    +
    +

    Long-Term Outcomes

    +
      +
    • Support OSSci about JuliaHealth
      • +
    +
    +
    +
    +

    Notes

    +
      +
    • Introductions +
        +
      • Clark C. Evans +
          +
        • Master cobbler of YAML
          • +
        • Used to work at Prometheus Research +
            +
          • Sold to IQVIA
            • +
          • +
        • Worked under MechanicalRabbit Umbrella +
            +
          • Developed FunSQL.jl with Kirill
            • +
          • Database characterization
            • +
          • +
        • Joined Tufts University CTSA +
            +
          • Helping with data warehousing +
              +
            • Objects to query OHDSI databases and EPIC Clarity
              • +
            • +
          • Getting Pluto working
            • +
          • +
        • +
      • Jonathan +
          +
        • Manager for OSSci for NumFOCUS
          • +
        • Goal: Mapping open source science ecosystem
          • +
        • Work with Distributed Computing +
            +
          • Berkeley technology
            • +
          • Blocks and chains!
            • +
          • +
        • Using Open Source and Science to drive research
          • +
        • +
      • Edmund +
          +
        • PhD Candidate at Texas Dallas +
            +
          • Molecular and Cell Biology
            • +
          • Functional Genomics
            • +
          • +
        • Coming from JuliaCon
          • +
        • Excited about Health stuff
          • +
        • +
      • +
    • Interoperability of Julia with health research ecosystems (R) +
        +
      • Easiest way to interoperate is to call them directly from the command line
        • +
      • Build your own executables
        • +
      • Most reliable/easiest
        • +
      • Database approach: +
          +
        • Build table in one language
          • +
        • Ingest in another
          • +
        • +
      • Combining executables in one location – use Docker? +
          +
        • Can run on several different machines
          • +
        • +
      • Building R packages with Julia backends is possible
        • +
      • +
    • Develop and document tutorials showcasing compositional solutions to JuliaHealth ecosystem problems +
        +
      • Competing Julia with other tutorials?
        • +
      • Switching over to Julia from what?
        • +
      • Why are people still not switching? +
          +
        • Demonstrating the use is one way
          • +
        • +
      • Obviously, one could write more posts
        • +
      • But there seems to be a lot of content already – what is missing?
        • +
      • Does seem like there is two different levels of documentation +
          +
        • Beginner
          • +
        • Advanced
          • +
        • +
      • Where are the practical means of solving problems in Julia?
        • +
      • +
    • Databases and JuliaHealth +
        +
      • Show how to do the basics
        • +
      • Common database errors +
          +
        • How to address them
          • +
        • +
      • Unclear on how to solve it; more people working in this space?
        • +
      • Not really a problem within ecosystem
        • +
      • Look at drivers across all packages to see how things work in Julia ecosystem +
          +
        • See how we can address issues across ecosystem
          • +
        • +
      • +
    • Jonathan Starr and NumFOCUS’s OSSci Program +
        +
      • Getting to deep diving within Julia ecosystem
        • +
      • Researchers who want to find a package that they can use and develop
        • +
      • Mapping projects and people to a given tool +
          +
        • Can look at map to see where packages are needed for a particular ecosystem
          • +
        • Can click on and connect with researchers
          • +
        • Highlighting of credit for researchers
          • +
        • +
      • Starting with NumFOCUS projects
        • +
      • Building out knowledge of all ongoing projects/software +
          +
        • Julia is little represented right now
          • +
        • +
      • How to show to funders/orgs what projects to support
        • +
      • How to build support across or collaboration between groups
        • +
      • Trying to stop abandonware from happening
        • +
      • Attempting to build social infrastructure
        • +
      • Q&A +
          +
        • Tufts doing something very similar – happy to collaborate
          • +
        • How can JuliaHealth get started and involved? +
            +
          • Jonathan: Send me reference page and we can get this started!
            • +
          • +
        • +
      • Links: +
          +
        • About: https://numfocus.org/open-source-science-initiative-ossci
          • +
        • How To Join: https://opensource.science
          • +
        • Map of Open Source Science (MOSS)
          • +
        • +
      • +
    +
    +
    +
    +

    July 28 2023

    +

    In Attendance: [Attendance Waived for In-Person Meeting

    +

    Location: JuliaCon 2023 JuliaHealth Birds of a Feather

    +

    Summary: New member backgrounds, problems within the Julia ecosystem related to healthcare, thoughts on addressing issues within a JuliaHealth context, and learning resources for Julia within the context of health.

    +

    Keywords: #ehr #genomics #biology #interoperability #database #sql #outreach

    +
    +

    Agenda

    +
      +

    1. Introductions and what people in the community are using Julia for in health research

      2. +

    2. What is missing of painful in Julia that is needed to drive health research forward

      3. +

    3. Thoughts on how to address some of these problems

      4. +

    4. Open discussion and next steps for JuliaHealth

      5. +
    +
    +

    Short-Term Outcomes

    +

    Not Available

    +
    +
    +

    Long-Term Outcomes

    +
      +

    • ACTION: Develop and document tutorials showcasing compositional solutions to JuliaHealth ecosystem problems.

      • +

    • ACTION: Establish cohesive and organized Julia Blog to guide users and highlight official blogs.

      • +
    +
    +
    +
    +

    Meeting Notes

    +
      +

    • Attendee interests and background

      +
        +

      • Here to learn

        • +

      • From EHR development and background

        • +

      • Genie folks here to support JuliaHealth endeavors

        • +

      • Genomics research and prevention

        • +

      • Quebec Heart and Lung Institute

        • +

      • Representing PumasAI

        • +

      • Consulting group

        +
          +

        • Developing health research in Michigan area

          • +

        • Aggregating claims data

          • +

        • To learn what is going on in the community

          • +
        • +

      • Creator of MetaAnalysis.jl

        • +

      • Involved with backend of healthcare IT

        • +

      • Working on JuliaHub

        +
          +

        • Learning about packages that are out there

          • +

        • Here to support JuliaHealth members

          • +

        • New Zealand longitudinal child health

          +
            +

          • Have own secure system

            • +

          • Post-COVID syndrome

            • +
          • +

        • Computational biology

          +
            +

          • Sickle Cell

            • +

          • Applying some ML

            • +
          • +
        • +
      • +

    • Problems within the Julia ecosystem

      +
        +

      • Julia needs more database connectivity to more easily do operations research

        • +

      • Databases are a pain point and composing with other aspects of the ecosystem

        • +

      • Interoperability within Julia and other sorts of resources

        • +

      • I end up doing the bare minimum in SQL

        +
          +

        • Do we have RAM?

          • +

        • Can we pull this into the Julia ecosystem?

          • +

        • Crank up the RAM! But only so much scaling

          • +

        • Minimal SQL writing

          +
            +
          • Searchlight.jl: Julia ORM layer within
            • +
          • +

        • Is Genie like a shiny?

          +
            +

          • No, more of a full-stack

            • +

          • Goes beyond just visualization dashboards

            • +
          • +
        • +

      • Sequencing data

        +
          +

        • Equally data

          • +

        • Everyone uploads data in slightly different ways

          • +

        • Make simple ways to pull that data

          • +

        • R Conductor –> JuliaConductor?

          +
            +
          • Would make genomic pipelines within Julia pipelines a lot easier
            • +
          • +

        • We need to understand the underlying structures

          • +

        • One of the big pain points

          +
            +
          • Often to have roll your own
            • +
          • +
        • +

      • EpiR –> EpiJ?

        +
          +
        • Power calculators
          • +
        • +

      • Co-founder of start-up

        +
          +

        • Found unmet need for remote monitoring for neuotropenia

          • +

        • Non-invasive screen for neutropenia

          • +

        • Device runs Julia

          • +

        • Pain points:

          +
            +

          • Testability of hardware

            • +

          • LOTS of CI – bit of a pain

            • +

          • How much repetition happens in CI

            • +
          • +

        • Part of the problem for these problems:

          +
            +

          • There are still going to be folks who use the same organizations

            • +

          • Overcoming inertia to do the same or similar things in Julia

            • +

          • Wrapping around Julia?

            +
              +

            • Bringing it into the R ecosystem

              • +

            • Leading to big impacts for callable things from R by having smaller static binaries

              • +

            • Wrapping Julia packages in R

              • +
            • +

          • N3C – National COVID Cohort Collaborative

            +
              +

            • Went to many healthcare systems across the US to get COVID data

              • +

            • Shelled out to Palantir

              • +

            • Open source tools within the ecosystem

              • +

            • JuliaHub has Boeing board member

              +
                +

              • Trusted within security community

                • +

              • Could help in this situation

                • +
              • +
            • +
          • +
        • +
      • +

    • Thoughts on how to address some of these problems

      +
        +

      • Using other packages outside of Julia

        +
          +

        • If you have some way to wrap around it

          • +

        • Getting support

          • +

        • PythonCall.jl or RCall.jl

          +
            +
          • Not clear how to make this compositional
            • +
          • +
        • +

      • The paradox of compositionality

        +
          +

        • Blog posts go a huge ways to solving problems

          • +

        • Tutorials showing how things can be combined together

          • +

        • Promotional type material

          • +

        • Nice docs are nice

          • +
        • +

      • The Julia Blog itself

        +
          +

        • Mentions JuliaBloggers but doesn’t help with guiding users to read

          • +

        • Blogs need to go on as official blogs

          • +

        • Julia Forem – is it maintained?

          +
            +

          • Hook into the tags from blogs

            • +

          • Cross-posting where appropriate

            • +
          • +
        • +

      • How to learn Julia within the context of health

        +
          +
        • Carpentries for learning resources
          • +
        • +
      • +
    +
    +
    +
    +

    June 30 2023

    +
    +

    Meeting Summary

    +

    In Attendance: Jacob Zelko, Fareeda Abdelazeez, Zachary Christensen

    +

    Location: Virtual

    +

    Summary: Discussed new members, upcoming JuliaCon, JuliaHealth Birds of a Feather discussion on topics like neural decoding and OMOP tooling, managing logistics for Julia organizations, and JuliaHealth PR reviews.

    +

    Keywords: #brain #imaging #neural #decoding #collaboration #community #engagement

    +
    +
    +

    Agenda

    +
      +

    1. New member welcomes!

      2. +

    2. Planning JuliaHealth Birds of a Feather

      +
        +
      1. Topics?
        2. +
      2. Facilitators?
        3. +
      3. Creating actionable outcomes?
        4. +
      3. +

    3. Open discussion on Julia Orgs, How Do You Manage Logistics?

      4. +

    4. Misc topics

      5. +

    5. Julia for Health Informatics Research & Bridging community organizations

      6. +
    +
    1. Open Discussion on [The Graphs Ecosystem](https://discourse.julialang.org/t/the-graphs-ecosystem/99463?u=thecedarprince)
    +
    +
    +

    Meeting Outcomes

    +
    +

    Short-Term Outcomes

    +
      +

    • @Jacob Set-up HackMD to take notes going forward

      +
        +
      • Copy and paste meeting minutes over to JuliaHealth PR to update at end of meetings
        • +
      • +
    +
    +
    +

    Long-Term Outcomes

    +
      +
    • ACTION: Creating a template repository 
      • +
    +
    +
    +
    +

    Meeting Notes

    +
      +

    • New members:

      +
        +

      • Zachary Christensen

        +
          +

        • Neuroimaging research

          • +

        • MD/PhD

          +
            +
          • Trying to finish this year!!!
            • +
          • +

        • Lots of background work like in JuliaData

          • +

        • Works on making Julia interface

          • +
        • +
      • +

    • Announcement: JuliaCon about 1 month away!

      +
        +
      • We have our own track: biology and medicine
        • +
      • Many people working on different things
        • +
      • +

    • JuliaHealth Birds of a Feather Discussion

      +
        +

      • Possible Topics:

        +
          +

        • Neural decoding 

          +
          • +

        • OMOP Tooling for Real World Data

          • +

        • How to start collaborations?

          +
            +

          • Maybe grant collaborations?

            • +

          • Getting access to datasets

            +
              +
            • Coming up with different research questions
              • +
            • +
          • +

        • How can we integrate across the community?

          +
            +

          • What problem can we solve?

            +
              +

            • Become a community resource to point to packages

              • +

            • Don’t need to keep recreating or developing new packages

              +
                +
              • Packages could be applications built on top of a specific use case
                • +
              • Combining old packages in new ways
                • +
              • +
            • +
          • +
        • +
      • +

    • Open discussion on Julia Orgs, How Do You Manage Logistics?

      +
        +

      • Have multiple persons part of the organizations

        • +

      • Sharing meeting documentation

        +
          +

        • Share Google Doc at the beginning or before a meeting in announcement

          • +

        • Publish notes on website publicly

          +
            +

          • PR to update the JuliaHealth website with new tab for meeting minutes

            +
              +
            • ACTION: Using HackMD to take notes going forward
              • +
            • Copy and paste meeting minutes over to JuliaHealth PR to update at end of meetings
              • +
            • +
          • +
        • +

      • Consistent APIs for JuliaHealth

        +
          +

        • Initial first pass with HealthBase.jl: https://github.com/JuliaHealth/HealthBase.jl 

          • +

        • As free as possible from niche

          • +

        • Could become quickly overwhelming or run risk of bikeshedding

          • +

        • ArrayInterface is a learning example in this context

          • +

        • Light dependency package is great with a well-described API 

          • +

        • How to move forward and get momentum

          +
            +
          • Without it turning into a mess
            • +
          • +

        • Common ontologies: http://obofoundry.org 

          • +
        • +

      • JuliaHealth PR Reviews

        +
        • +
      • +
    +
    +
    +
    +

    May 26 2023

    +
    +

    Meeting Summary

    +

    In Attendance: Jacob Zelko, Dilum Aluthge, Asher Wasserman, Fareeda Abdelazeez, Kyle Beggs

    +

    Location: Virtual

    +

    Summary: First JuliaHealth community call to meet other Julians, learn how we can galvanize the Juliahealth Community, and open discussion on paths forward

    +

    Keywords: #data #analysis #hemodynamics #omop #machine #learning

    +
    +
    +

    Agenda

    +
      +

    1. Introductions

      2. +

    2. What people are using Julia for in health research

      3. +

    3. Selected topics and state within the Julia ecosystem:

      +
        +
      1. Observational Health
        2. +
      2. Medical Imaging
        3. +
      3. Machine Learning and Health
        4. +
      4. Interoperability Standards
        5. +
      5. Drug Discovery
        6. +
      4. +

    4. Standard Interfaces

      5. +
    +
    +
    +

    Meeting Outcomes

    +
    +

    Short-Term Outcomes

    +
      +
    • @Dilum finds out how to live stream JuliaHealth BoF
      • +
    +
    +
    +

    Long-Term Outcomes

    +
    +
    +
    +

    Meeting Notes

    +
      +

    1. Introductions

      +
        +

      1. Dilum Aluthge – MD/PhD Student Brown University (BCBI), PumasAI

        +
          +

        1. Julia Community Involvement

          +
            +
          1. Pkg
            2. +
          2. General Registry
            3. +
          3. Continuous Integration
            4. +
          2. +

        2. JuliaHealth and beyond

          +
            +
          1. Originally created JuliaHealth to bring people together in health
            2. +
          2. BioJulia folks are a great source of inspiration for packages!
            3. +
          3. +

        3. Birds of a Feather!!! COME VISIT! – Friday July 28th, 4PM EST in Boston, MA!

          4. +
        2. +

      2. Asher Wasserman – Astronomy PhD, Data Scientist in BioTech

        +
          +

        1. Julia Community Involvement

          +
            +
          1. Differential Equations
            2. +
          2. One off deployments
            3. +
          2. +
        3. +

      3. Fareeda Abdelazeez – GSoC JuliaHealth (First GSoC Student!!!!!)

        +
          +

        1. Julia Community Involvement

          +
            +
          1. Observational Health tooling JuliaHealth!
            2. +
          2. +
        4. +

      4. Kyle Beggs – Software Engineer in small Optics company, Finishing PhD in MechE

        +
          +

        1. Julia Community Involvement

          +
            +
          1. PDEs
            2. +
          2. Hemodynamics research focus
            3. +
          3. Take advantage of these tools for imaging, segmentation
            4. +
          2. +
        5. +
      2. +

    2. What people are using Julia for in health research

      +
        +

      1. Asher: Cancer patient data

        +
          +

        1. PDFs and other data formats 

          +
            +
          1. CDA documents
            2. +
          2. +

        2. How to structure this ad hoc type of data into common data model

          3. +

        3. Developing processes to automatically make these documents useful

          4. +

        4. How do we clean the data to match actual reality

          5. +

        5. How do we make this data actionable/useful

          6. +

        6. Could match towards goals of OHDSI/observational health

          +
            +
          1. Analyses at population level?
            2. +
          2. Outcome propensity scores?
            3. +
          3. Patient phenotype development?
            4. +
          7. +

        7. Role of Julia:

          +
            +

          1. Mainly as a scripting language

            2. +

          2. Supplement to a lot of SQL scripting (FunSQL discovered)

            3. +

          3. Python is generally being deployed because of software devs

            +
              +
            1. How to not crash AWS, etc.
              2. +
            4. +

          4. Julia deployment for risk (?)

            5. +

          5. Survival Analysis in Julia; lifelines in Python otherwise

            6. +
          8. +
        2. +

      2. Kyle: Vascular Surgical Planning

        +
          +

        1. Unobvious on where to place graft, etc – educated guesses

          2. +

        2. Creating a tool to simulate operations

          3. +

        3. Why Julia?

          +
            +

          1. Existing tools are open source but really GUI-driven

            2. +

          2. Integration across ecosystem would be even better for hemodynamics in Julia

            3. +

          3. Give a base to simulate the mechanics involved with this

            +
              +
            1. JuliaFEM, etc. 
              2. +
            4. +
          4. +

        4. Mesh list methods

          +
            +
          1. Point clouds
            2. +
          2. Main application is within hemodynamics
            3. +
          5. +
        3. +

      3. Fareeda: JuliaHealth GSoC Student

        +
          +

        1. Working on OMOP Common Data Model

          2. +

        2. Standard model for observational health patient data

          3. +

        3. Develop infrastructure of JuliaHealth to work with OMOP CDM data

          +
            +
          1. Improve DBConnector
            2. +
          2. OMOPCDMCohortCreator.jl – add tooling
            3. +
          3. OHDSIAPI.jl – creating interfaces for ATHENA/ATLAS
            4. +
          4. +

        4. Patient Level Prediction tooling

          +
            +

          1. Using MLJ algorithms

            2. +

          2. Attempting to solve a research question

            +
              +
            1. Evaluate success of package
              2. +
            3. +
          5. +

        5. Stretch goals:

          +
            +
          1. Cohort Quality and underlying data is β€œgood”
            2. +
          2. Build support for OBDC connections
            3. +
          6. +
        4. +

      4. Overlap with other organizations

        +
          +

        1. Doesn’t happen in a vacuum

          2. +

        2. Serving as a bridge between a bridge and a community between other groups

          3. +

        3. What should be JuliaHealth?

          +
            +
          1. Bringing together people 
            2. +
          4. +
        5. +
      3. +

    3. Selected topics and state within the Julia ecosystem:

      +
        +
      1. Observational Health
        2. +
      2. Medical Imaging
        3. +
      3. Machine Learning and Health
        4. +
      4. Interoperability Standards
        5. +
      5. Drug Discovery
        6. +
      4. +

    4. Standard Interfaces

      5. +
    +

    June 30th, 2023

    +

    Attending:

    +

    Agenda:

    +
      +

    1. New member welcomes!

      2. +

    2. Planning JuliaHealth Birds of a Feather

      +
        +
      1. Topics?
        2. +
      2. Facilitators?
        3. +
      3. Creating actionable outcomes?
        4. +
      3. +

    3. Open discussion on Julia Orgs, How Do You Manage Logistics?

      4. +

    4. Misc topics

      +
        +

      1. Julia for Health Informatics Research & Bridging community organizations

        +
          +
        1. Open Discussion on The Graphs Ecosystem
          2. +
        2. +
      5. +
    +

    Notes: 

    +
      +

    • New members:

      +
        +

      • Zachary Christensen

        +
          +

        • Neuroimaging research

          • +

        • MD/PhD

          +
            +
          • Trying to finish this year!!!
            • +
          • +

        • Lots of background work like in JuliaData

          • +

        • Works on making Julia interface

          • +
        • +
      • +

    • Announcement: JuliaCon about 1 month away!

      +
        +
      • We have our own track: biology and medicine
        • +
      • Many people working on different things
        • +
      • +

    • JuliaHealth Birds of a Feather Discussion

      +
        +

      • Possible Topics:

        +
          +

        • Neural decoding 

          +
          • +

        • OMOP Tooling for Real World Data

          • +

        • How to start collaborations?

          +
            +

          • Maybe grant collaborations?

            • +

          • Getting access to datasets

            +
              +
            • Coming up with different research questions
              • +
            • +
          • +

        • How can we integrate across the community?

          +
            +

          • What problem can we solve?

            +
              +

            • Become a community resource to point to packages

              • +

            • Don’t need to keep recreating or developing new packages

              +
                +
              • Packages could be applications built on top of a specific use case
                • +
              • Combining old packages in new ways
                • +
              • +
            • +
          • +
        • +
      • +

    • Open discussion on Julia Orgs, How Do You Manage Logistics?

      +
        +

      • Have multiple persons part of the organizations

        • +

      • Sharing meeting documentation

        +
          +

        • Share Google Doc at the beginning or before a meeting in announcement

          • +

        • Publish notes on website publicly

          +
            +

          • PR to update the JuliaHealth website with new tab for meeting minutes

            +
              +
            • ACTION: Using HackMD to take notes going forward
              • +
            • Copy and paste meeting minutes over to JuliaHealth PR to update at end of meetings
              • +
            • +
          • +
        • +

      • Consistent APIs for JuliaHealth

        +
          +

        • Initial first pass with HealthBase.jl: https://github.com/JuliaHealth/HealthBase.jl 

          • +

        • As free as possible from niche

          • +

        • Could become quickly overwhelming or run risk of bikeshedding

          • +

        • ArrayInterface is a learning example in this context

          • +

        • Light dependency package is great with a well-described API 

          • +

        • How to move forward and get momentum

          +
            +
          • Without it turning into a mess
            • +
          • +

        • Common ontologies: http://obofoundry.org 

          • +
        • +

      • JuliaHealth PR Reviews

        +
        • +
      • +
    + + +
    +
    + +
    + +
    +
    +
    +
    +

    Copyright 2024-25, JuliaHealth.

    +
    +
    +   +
    +
    + +
    +
    +
    + + + + + \ No newline at end of file diff --git a/docs/about.html b/docs/pages/related_organizations.html similarity index 58% rename from docs/about.html rename to docs/pages/related_organizations.html index 8e5b6b4..cae2712 100644 --- a/docs/about.html +++ b/docs/pages/related_organizations.html @@ -2,12 +2,12 @@ - + -About the JuliaHealth Blog – The JuliaHealth Blog +Related Organizations – JuliaHealth - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + - - - - - - - - + @@ -115,11 +81,8 @@ @@ -193,59 +131,72 @@
    -
    - +

    This is a (not necessarily comprehensive) list of organizations that focus primarily on developing and maintaining open-source Julia packages related to the life sciences and health sciences.

    +

    If you would like to add an organization to this list, please feel free to make a pull request.

    +
    +

    Julia community organizations

    +
    +
    +

    Labs and research groups

    +
      +
    • BCBI – Center for Biomedical Informatics at Brown University (website)
      • +
    • Holy Lab - Holy Lab at Washington University in St. Louis (website)
      • +
    • InPhyT - Interdisciplinary Physics Team
      • +
    - Back to top - - +
    +

    Companies

    + + + +
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    GSoC ’24: Adding dataset-wide functions and integrations of augmentations

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    analysis
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    - MedPipe3D - Medical segmentation pipeline with dataset-wide functions and augmentations. -
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    Author
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    Jan Zubik

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    Published
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    November 3, 2024

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    πŸ“πŸ©»πŸ“ŽπŸ“‰ ➑️ πŸ—ƒοΈπŸ“šβ™»οΈπŸ§‘β€πŸ« ➑️ πŸ€–πŸ‘οΈπŸ“ˆ ➑️ β€οΈβ€πŸ©Ή

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    These emoticons may resemble hieroglyphics, but very soon you will realize that they mean more than 1000s of lines of code.

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    - -Description of the emojis used in the title - -
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    • -πŸ“ Action Plan: A clear, structured plan that guides each step of the MedPipe3D pipeline. -
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    • -🩻 3D Medical Images: Medical imaging data, such as MRI scans in Nifti format. -
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    • -πŸ“Ž AI Model: The initial AI model that will be trained and refined within the pipeline. -
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    • -πŸ“‰ Loss Function: A function that measures the model’s performance during training, guiding the optimization process. -
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    • -πŸ—ƒοΈ Data Loading: Preparation and loading of data and metadata into HDF5 format. -
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    • -πŸ“š Data Splitting: Dividing data into training, validation, and test sets. -
      • -
    • -♻️ Data Augmentation: Increasing data variability through augmentation. -
      • -
    • -πŸ§‘β€πŸ« AI Training: Using Lux.jl framework to train the AI model. -
      • -
    • -πŸ€– Model: The trained AI model that can perform tasks like segmentation on medical images. -
      • -
    • -πŸ‘οΈ Data for Visualization: Output data, such as masks and segmentations. -
      • -
    • -πŸ“ˆ Performance Logs: Logs and metrics documenting the AI’s performance. -
      • -
    • -β€οΈβ€πŸ©Ή Purpose of MedPipe3D -
      • -
    -
    -
    -

    In this post, I’d like to summarize what I did this summer and everything I learned along the way, rebuilding the MedPipe3D medical imaging pipeline. I will not start typically, but so that anyone even a novice can visualize what this project has achieved, while the latter part is intended for more experienced readers. It will be easiest to divide it into 4 steps separated by ➑️ in the title above. Each emoji stands for a different piece of pipeliner and will be described below.

    -

    πŸ“πŸ©»πŸ“ŽπŸ“‰ What we need from the user

    -

    MedPipe3D requires four essential inputs from the user to get started: a clear action plan πŸ“, 3D medical images like MRI scans 🩻, an AI model πŸ“Ž, and a loss function πŸ“‰.

    -

    πŸ—ƒοΈπŸ“šβ™»οΈπŸ§‘β€πŸ« The Pipeline essential AI manufacturing line

    -

    Following the plan πŸ“, MedPipe3D loads data, pre-processes, and organizes it πŸ—ƒοΈ. Allowing data to be easily split πŸ“š, and efficiently augmented ♻️ in many ways for learning AI πŸ§‘β€πŸ« model effectively. In the end, performing testing and post-processing for better determination of AI skills.
    -It’s designed to transform raw medical data into a format that your AI can learn from, segmenting meaningful patterns and structures.

    -

    πŸ€–πŸ‘οΈπŸ“ˆ Results and Insights

    -

    MedPipe3D is a tool for researchers and for that, it cannot do without analysis, testing, and evaluation. The result of the pipeline is a model πŸ€– as well as data πŸ‘οΈ and logs πŸ“ˆ needed in MedEval3D that are ready for visualization and further analysis with MedEye3D. In a nutshell, it makes visualizing results easy, tumor locations or other medical features directly as masks on the scans.

    -

    β€οΈβ€πŸ©Ή Purpose-Driven Technology

    -

    MedPipe3D’s mission goes beyond technology. It’s about providing the tools to create AIs that support healthcare professionals in making faster, more accurate decisions, with the ultimate goal of saving lives.

    -

    This four-part journey captures the heart of the MedPipe3D toolkit for advancing medical AI, from raw data to life-saving insight.

    -
    -

    Introduction

    -

    MedPipe3D is a framework created from hundreds of hours over summer vacation, thousands of lines of code, hundreds of mistakes, and most importantly the guidance of my mentor and author of all of these libraries Dr. Jakub Mitura. At its core, MedPipe3D combines sophisticated data handling from MedImage thanks to the hard work of Divyansh Goyal. Newly developed pipeline for model training, validation, and testing with existing MedEval3D, and result visualization with MedEye3D. Unfortunately, not all of the project’s goals have been fully achieved, and thereby there is one section ➑️ too many. Hopefully not for long. My name is Jan Zubik, and I wrote this entire library from scratch, which is currently my most complex project.

    -

    If you are a data scientist, programmer, or code enthusiast, I invite you to read the next section where I go into detail and present version 1 of this tool in detail.

    -

    I’m a 3rd-year student of BSc in Data Science and Machine Learning, I know that many things can be done better, expanded, debugged, and optimized. Now it just works, but don’t hesitate to write to me personally on LinkedIn, Julia’s Slack or GitHub! With your comments, and direct critique you will help me to be a better programmer and one day MedPipe3D will contribute in a tiny way to save someone’s life!

    -

    Exact work from the Google Summer of Code project you will find in GitHub the repository.

    -
    -
    -
    -

    Project Goals

    -

    The primary goal was to develop MedPipe3D and enhance MedImage, a Julia package designed to streamline the process of GPU-accelerated medical image segmentation. The project aimed to merge existing librariesβ€”MedEye3D, MedEval3D, and MedImageβ€”into a cohesive pipeline that facilitates advanced data handling, preprocessing, augmentation, model training, validation, testing with post-processing and visualization for medical imaging applications.

    -
    -
    -

    Tasks

    -
      -
    • πŸ†™ - Fully finished, with great potential for further development
      • -
    • βœ… - Fully completed
      • -
    • ⚠️ - Partially uncompleted
      • -
    • ❌ - Unreached
      • -
    -Full list of all major parts and minor tasks (all tasks set up in the original GSOC plan were completed at least minimum level, and many additional improvements above minimum were implemented) -
    -
      -
    1. Helpful functions to support the MedImage format βœ…
      2. -
    -
      -
    • Debugging rotations βœ…
      • -
    • Crop MedImage or 3D array βœ…
      • -
    • Pad MedImage or 3D array βœ…
      • -
    • Pad with edge values βœ…
      • -
    • Calculating the average of the edges of the picture πŸ†™
      • -
    -
      -
    1. Integrate Augmentations for Medical Data βœ…
      2. -
    -
      -
    • Brightness transform βœ…
      • -
    • Contrast augmentation transform βœ…
      • -
    • Gamma Transform βœ…
      • -
    • Gaussian noise transform βœ…
      • -
    • Rician noise transform βœ…
      • -
    • Mirror transform βœ…
      • -
    • Scale transform πŸ†™
      • -
    • Gaussian blur transform βœ…
      • -
    • Simulate low-resolution transform πŸ†™
      • -
    • Elastic deformation transform πŸ†™
      • -
    -
      -
    1. Develop a Pipeline ⚠️
      2. -
    -
      -
    • Structured configuration of all hyperparameters πŸ†™
      • -
    • Interactive creation of configuration βœ…
      • -
    • Creating a structured configuration of hyperparameters in JSON πŸ†™
      • -
    • Loading data into HDF5 βœ… -
        -
      • Cropping and padding to real coordinates of the main picture βœ…
        • -
      • Calculate Median and Mean Spacing with resampling πŸ†™
        • -
      • Cropping and padding to specific or average dimensions βœ…
        • -
      • Standardization and normalization βœ…
        • -
      • -
    • Managing index groups (channels) for batch requirements in HDF5 βœ… -
        -
      • Divide into train, validation, test specified as % βœ…
        • -
      • Divide with a specific division specified in JSON βœ…
        • -
      • Equal distribution when there are multiple classes βœ…
        • -
      • -
    • Extracting data and creating 5-dimensional tensors for batched learning βœ… -
        -
      • Hole images data loading βœ…
        • -
      • Patch-based data loading with probabilistic oversampling βœ…
        • -
      • -
    • Obtaining the necessary elements for learning βœ… -
        -
      • Get optimizer, loss function, and performance metrics βœ…
        • -
      • -
    • Apply augmentations βœ…
      • -
    • Train βœ… -
        -
      • Initializing model βœ…
        • -
      • The learning epoch βœ…
        • -
      • Epoch with early stopping functionality βœ…
        • -
      • -
    • Inferring βœ…
      • -
    • Validation βœ… -
        -
      • Evaluate metric βœ…
        • -
      • Evaluate validation loss βœ…
        • -
      • Validation with largest connected componentβœ…
        • -
      • -
    • Testing βœ… -
        -
      • Evaluate test set βœ…
        • -
      • Invertible augmentations evaluation βœ…
        • -
      • Patch-based invertible augmentations evaluation βœ…
        • -
      • -
    • Logging ⚠️ -
        -
      • Returning the necessary results ⚠️
        • -
      • Logging connection to TensorBoard ❌
        • -
      • Logging errors and warnings ❌
        • -
      • -
    • Visualization ⚠️ -
        -
      • Returning data in Nifti format βœ…
        • -
      • Automated visualization in MedEye3D ❌
        • -
      • -
    -
      -
    1. Optimize Performance with GPU Acceleration -
        -
      • Augmentations βœ…
        • -
      • Learning, Validation, Testing βœ…
        • -
      • Largest connected component βœ…
        • -
      2. -
    2. Documentation ⚠️ -
        -
      • Comments in important places in the code ⚠️
        • -
      • Documentation of the function ⚠️
        • -
      • Read me ⚠️
        • -
      • Documentation on juliahealth.org ❌
        • -
      3. -
    -
    -
    -

    Integrate augmentations for medical data πŸ†™

    -

    Augmenting medical data is a crucial step for enhancing model robustness, especially given the variations in imaging conditions and patient anatomy.

    -
      -
    • This pipeline currently supports multiple augmentation techniques: -
        -
      • Brightness transform βœ…
        • -
      • Contrast augmentation transform βœ…
        • -
      • Gamma Transform βœ…
        • -
      • Gaussian noise transform βœ…
        • -
      • Rician noise transform βœ…
        • -
      • Mirror transform βœ…
        • -
      • Scale transform πŸ†™
        • -
      • Gaussian blur transform βœ…
        • -
      • Simulate low-resolution transform πŸ†™
        • -
      • Elastic deformation transform πŸ†™
        • -
      • -
    -

    Which have been fully integrated. Each of these methods helps the model generalize better by simulating diverse imaging scenarios.

    -

    -

    Comments:

    -

    Augmentations such as scaling, and low-resolution simulation use interpolation that is not yet GPU-accelerated.

    -

    Elastic deformation with simulation of different tissue elasticities is a potential development opportunity that would further improve the model’s adaptability by mimicking more complex variations found in medical imaging.

    -
    -
    -

    Invertible augmentations and support test time augmentations πŸ†™

    -

    This section focuses on the ability to apply reversible augmentations to test data, allowing the model to be evaluated with different transformations. Only rotation is available at this time. The function evaluate_patches performs this evaluation by applying specified augmentations, dividing the test data into patches, and reconstructing the full image from the patches. During testing, one can choose to use of largest connected component post-processing. Metrics are calculated and results are saved for analysis.

    -
    - -evaluate_test: - -
    # ...
-for test_group in test_groups
-    test_data, test_label, attributes = fetch_and_preprocess_data([test_group], h5, config)
-    results, test_metrics = evaluate_patches(test_data, test_label,  tstate, model, config)
-    y_pred, metr = process_results(results, test_metrics, config)
-    save_results(y_pred, attributes, config)
-    push!(all_test_metrics, metr)
-end
-# ...
    -
    function evaluate_patches(test_data, test_label, tstate, model, config, axis, angle)
-    println("Evaluating patches...")
-    results = []
-    test_metrics = []
-    tstates = [tstate]
-    test_time_augs = []
-
-    for i in config["learning"]["n_invertible"]
-        data = rotate_mi(test_data, axis, angle)
-        for tstate_curr in tstates
-            patch_results = []
-            patch_size = Tuple(config["learning"]["patch_size"])
-            idx_and_patches, paded_data_size = divide_into_patches(test_data, patch_size)
-            coordinates = [patch[1] for patch in idx_and_patches]
-            patch_data = [patch[2] for patch in idx_and_patches]
-            for patch in patch_data
-                y_pred_patch, _ = infer_model(tstate_curr, model, patch)
-                push!(patch_results, y_pred_patch)
-            end
-            idx_and_y_pred_patch = zip(coordinates, patch_results)
-            y_pred = recreate_image_from_patches(idx_and_y_pred_patch, paded_data_size, patch_size, size(test_data))
-            if config["learning"]["largest_connected_component"]
-                y_pred = largest_connected_component(y_pred, config["learning"]["n_lcc"])
-            end
-            metr = evaluate_metric(y_pred, test_label, config["learning"]["metric"])
-            push!(test_metrics, metr)
-        end
-    end
-    return results, test_metrics
-end
    -
    function divide_into_patches(image::AbstractArray{T, 5}, patch_size::Tuple{Int, Int, Int}) where T
-    println("Dividing image into patches...")
-    println("Size of the image: ", size(image)) 
-
-    # Calculate the required padding for each dimension (W, H, D)
-    pad_size = (
-        (size(image, 1) % patch_size[1]) != 0 ? patch_size[1] - size(image, 1) % patch_size[1] : 0,
-        (size(image, 2) % patch_size[2]) != 0 ? patch_size[2] - size(image, 2) % patch_size[2] : 0,
-        (size(image, 3) % patch_size[3]) != 0 ? patch_size[3] - size(image, 3) % patch_size[3] : 0
-    )
-
-    # Pad the image if necessary
-    padded_image = image
-    if any(pad_size .> 0)
-        padded_image = crop_or_pad(image, (size(image, 1) + pad_size[1], size(image, 2) + pad_size[2], size(image, 3) + pad_size[3]))
-    end
-
-    # Extract patches
-    patches = []
-    for x in 1:patch_size[1]:size(padded_image, 1)
-        for y in 1:patch_size[2]:size(padded_image, 2)
-            for z in 1:patch_size[3]:size(padded_image, 3)
-                patch = view(
-                    padded_image,
-                    x:min(x+patch_size[1]-1, size(padded_image, 1)),
-                    y:min(y+patch_size[2]-1, size(padded_image, 2)),
-                    z:min(z+patch_size[3]-1, size(padded_image, 3)),
-                    :,
-                    :
-                )
-                push!(patches, [(x, y, z), patch])
-            end
-        end
-    end
-    println("Size of padded image: ", size(padded_image))
-    return patches, size(padded_image)
-end
-
-function recreate_image_from_patches(
-    coords_with_patches,
-    padded_size,
-    patch_size,
-    original_size
-)
-    println("Recreating image from patches...")
-    reconstructed_image = zeros(Float32, padded_size...)
-    
-    # Place patches back into their original positions
-    for (coords, patch) in coords_with_patches
-        x, y, z = coords
-        reconstructed_image[
-            x:x+patch_size[1]-1,
-            y:y+patch_size[2]-1,
-            z:z+patch_size[3]-1,
-            :,
-            :
-        ] = patch
-    end
-
-    # Crop the reconstructed image to remove any padding
-    final_image = reconstructed_image[
-        1:original_size[1],
-        1:original_size[2],
-        1:original_size[3],
-        :,
-        :
-    ]
-    println("Size of the final image: ", size(final_image))
-    return final_image
-end
    -
    -

    Comment:
    In this section, there is significant potential to incorporate additional types of invertible augmentations.

    -
    -
    -

    Patch-based data loading with probabilistic oversampling βœ…

    -

    In this section, patches are extracted using extract_patch from the medical images for model training, with a probability-based method to decide between a random patch or a patch with non-zero labels. Helper functions like get_random_patch and get_centered_patch determine the starting indices and dimensions for the patches based on given configurations, while padding methods ensure consistency even if the patch exceeds the original image dimensions. Probabilistic oversampling, as configured, allows for more balanced and informative data sampling, which improves the model’s ability to detect specific medical features.

    -
    - -extract_patch: - -
    function extract_patch(image, label, patch_size, config)
-    # Fetch the oversampling probability from the config
-    println("Extracting patch.")
-    oversampling_probability = config["learning"]["oversampling_probability"]
-    # Generate a random number to decide which patch extraction method to use
-    random_choice = rand()
-
-    if random_choice <= oversampling_probability
-        return extract_nonzero_patch(image, label, patch_size)
-    else
-
-        return get_random_patch(image, label, patch_size)
-    end
-end
-#Helper function, in case the mask is emptyClick to apply
-function extract_nonzero_patch(image, label, patch_size)
-    println("Extracting a patch centered around a non-zero label value.")
-    indices = findall(x -> x != 0, label)
-    if isempty(indices)
-        # Fallback to random patch if no non-zero points are found
-        return get_random_patch(image, label, patch_size)
-    else
-        # Choose a random non-zero index to center the patch around
-        center = indices[rand(1:length(indices))]
-        return get_centered_patch(image, label, center, patch_size)
-    end
-end
-# Function to get a patch centered around a specific index
-function get_centered_patch(image, label, center, patch_size)
-    center_coords = Tuple(center)
-    half_patch = patch_size .Γ· 2
-    start_indices = center_coords .- half_patch
-    end_indices = start_indices .+ patch_size .- 1
-
-    # Calculate padding needed
-    pad_beg = (
-        max(1 - start_indices[1], 0),
-        max(1 - start_indices[2], 0),
-        max(1 - start_indices[3], 0)
-    )
-    pad_end = (
-        max(end_indices[1] - size(image, 1), 0),
-        max(end_indices[2] - size(image, 2), 0),
-        max(end_indices[3] - size(image, 3), 0)
-    )
-
-    # Adjust start_indices and end_indices after padding
-    start_indices_adj = start_indices .+ pad_beg
-    end_indices_adj = end_indices .+ pad_beg
-
-    # Convert padding values to integers
-    pad_beg = Tuple(round.(Int, pad_beg))
-    pad_end = Tuple(round.(Int, pad_end))
-
-    # Pad the image and label using pad_mi
-    image_padded = pad_mi(image, pad_beg, pad_end, 0)
-    label_padded = pad_mi(label, pad_beg, pad_end, 0)
-
-    # Extract the patch
-    image_patch = image_padded[
-        start_indices_adj[1]:end_indices_adj[1],
-        start_indices_adj[2]:end_indices_adj[2],
-        start_indices_adj[3]:end_indices_adj[3]
-    ]
-    label_patch = label_padded[
-        start_indices_adj[1]:end_indices_adj[1],
-        start_indices_adj[2]:end_indices_adj[2],
-        start_indices_adj[3]:end_indices_adj[3]
-    ]
-
-    return image_patch, label_patch
-end
-
-function get_random_patch(image, label, patch_size)
-    println("Extracting a random patch.")
-    # Check if the patch size is greater than the image dimensions
-    if any(patch_size .> size(image))
-        # Calculate the needed size to fit the patch
-        needed_size = map(max, size(image), patch_size)
-        # Use crop_or_pad to ensure the image and label are at least as large as needed_size
-        image = crop_or_pad(image, needed_size)
-        label = crop_or_pad(label, needed_size)
-    end
-
-    # Calculate random start indices within the new allowable range
-    start_x = rand(1:size(image, 1) - patch_size[1] + 1)
-    start_y = rand(1:size(image, 2) - patch_size[2] + 1)
-    start_z = rand(1:size(image, 3) - patch_size[3] + 1)
-    start_indices = [start_x, start_y, start_z]
-    end_indices = start_indices .+ patch_size .- 1
-
-    # Extract the patch directly when within bounds
-    image_patch = image[start_indices[1]:end_indices[1], start_indices[2]:end_indices[2], start_indices[3]:end_indices[3]]
-    label_patch = label[start_indices[1]:end_indices[1], start_indices[2]:end_indices[2], start_indices[3]:end_indices[3]]
-
-    return image_patch, label_patch
-end
    -
    -
    -
    -

    Calculate Median and Mean Spacing with resampling πŸ†™

    -

    This part ensures that all images in the dataset have consistent real coordinates, spacing, and shape. It’s a critical factor in medical imaging for accurate analysis. Calculating and applying set values, median or mean across images ensures uniformity.

    -
    -

    Resample images to target image πŸ†™

    -

    This step aligns each image to the reference coordinates of the main image, ensuring that all images share a common spatial alignment. The resample_to_image function from MedImage.jl is used here, applying interpolation to adjust each image.

    -
    - -resample_images_to_target: - -
    if resample_images_to_target && !isempty(Med_images)
-    println("Resampling $channel_type files in channel '$channel_folder' to the first $channel_type in the channel.")
-    reference_image = Med_images[1]
-    Med_images = [resample_to_image(reference_image, img, interpolator) for img in Med_images]
-end
    -
    -

    Comment:
    Resample_to_image uses interpolation that is not yet GPU-accelerated in this implementation, this step slows down the data preparation phase significantly.

    -
    -
    -

    Ensure uniform spacing across the entire dataset πŸ†™

    -

    This step brings all images to a consistent voxel spacing across the dataset using resample_to_spacing from MedImage.jl. This uniform spacing is crucial for creating a standardized dataset where each image voxel represents the same physical volume.

    -
    - -esample_to_spacing: - -
    if resample_images_spacing == "set"
-    println("Resampling all $channel_type files to target spacing: $target_spacing")
-    target_spacing = Tuple(Float32(s) for s in target_spacing)
-    channels_data = [[resample_to_spacing(img, target_spacing, interpolator) for img in channel] for channel in channels_data]
-elseif resample_images_spacing == "avg"
-    println("Calculating average spacing across all $channel_type files and resampling.")
-    all_spacings = [img.spacing for channel in channels_data for img in channel]
-    avg_spacing = Tuple(Float32(mean(s)) for s in zip(all_spacings...))
-    println("Average spacing calculated: $avg_spacing")
-    channels_data = [[resample_to_spacing(img, avg_spacing, interpolator) for img in channel] for channel in channels_data]
-elseif resample_images_spacing == "median"
-    println("Calculating median spacing across all $channel_type files and resampling.")
-    all_spacings = [img.spacing for channel in channels_data for img in channel]
-    median_spacing = Tuple(Float32(median(s)) for s in all_spacings)
-    println("Median spacing calculated: $median_spacing")
-    channels_data = [[resample_to_spacing(img, median_spacing, interpolator) for img in channel] for channel in channels_data]
-elseif resample_images_spacing == false
-    println("Skipping resampling of $channel_type files.")
-    # No resampling will be applied, channels_data remains unchanged.
-end
    -
    -

    Comment:
    Resample_to_spacing uses interpolation that is not yet GPU-accelerated in this implementation, this step slows down the data preparation phase significantly.

    -
    -
    -

    Resizing all channel files to average or target size βœ…

    -

    To create a cohesive 5D tensor, all images in each channel are resized to a uniform shape, either the average size of all images or a specific target size. This resizing process uses crop_or_pad, ensuring that all images match the specified dimensions, making them suitable for model input.

    -
    - -crop_or_pad: - -
    if resample_size == "avg"
-    sizes = [size(img.voxel_data) for img in channels_data for img in img]  # Get sizes from all images
-    avg_dim = map(mean, zip(sizes...))
-    avg_dim = Tuple(Int(round(d)) for d in avg_dim)
-    println("Resizing all $channel_type files to average dimension: $avg_dim")
-    channels_data = [[crop_or_pad(img, avg_dim) for img in channel] for channel in channels_data]
-elseif resample_size != "avg"
-    target_dim = Tuple(resample_size)
-    println("Resizing all $channel_type files to target dimension: $target_dim")
-    channels_data = [[crop_or_pad(img, target_dim) for img in channel] for channel in channels_data]
-end
    -
    -
    -
    -
    -

    Basic Post-processing operations

    -

    Post-processing operations involve the algorithm largest_connected_components. It is achieved by label initialization and propagation in the segmented mask. The initialize_labels_kernel function assigns unique labels to different regions.

    -
    - -initialize_labels_kernel: - -
    @kernel function initialize_labels_kernel(mask, labels, width, height, depth)
-    idx = @index(Global, Cartesian)
-    i = idx[1]
-    j = idx[2]
-    k = idx[3]
-    
-    if i >= 1 && i <= width && j >= 1 && j <= height && k >= 1 && k <= depth
-        if mask[i, j, k] == 1
-            labels[i, j, k] = i + (j - 1) * width + (k - 1) * width * height
-        else
-            labels[i, j, k] = 0
-        end
-    end
-end
    -
    -Propagate_labels_kernel iteratively updates the labels to maintain connected regions. propagate_labels_kernel: -
    -
    @kernel function propagate_labels_kernel(mask, labels, width, height, depth)
-    idx= @index(Global, Cartesian)
-    i = idx[1]
-    j = idx[2]
-    k = idx[3]
-
-    if i >= 1 && i <= width && j >= 1 && j <= height && k >= 1 && k <= depth
-        if mask[i, j, k] == 1
-            current_label = labels[i, j, k]
-            for di in -1:1
-                for dj in -1:1
-                    for dk in -1:1
-                        if di == 0 && dj == 0 && dk == 0
-                            continue
-                        end
-                        ni = i + di
-                        nj = j + dj
-                        nk = k + dk
-                        if ni >= 1 && ni <= width && nj >= 1 && nj <= height && nk >= 1 && nk <= depth
-                            if mask[ni, nj, nk] == 1 && labels[ni, nj, nk] < current_label
-                                labels[i, j, k] = labels[ni, nj, nk]
-                            end
-                        end
-                    end
-                end
-            end
-        end
-    end
-end
    -
    -

    This process facilitates the identification of the largest connected components in 3D space, helping to isolate relevant medical structures, such as tumors, in the segmented mask. Allowing determining how many such areas are to be returned.

    -
    - -largest_connected_components: - -
    function largest_connected_components(mask::Array{Int32, 3}, n_lcc::Int)
-    width, height, depth = size(mask)
-    mask_gpu = CuArray(mask)
-    labels_gpu = CUDA.fill(0, size(mask))
-    dev = get_backend(labels_gpu)
-    ndrange = (width, height, depth)
-    workgroupsize = (3, 3, 3)
-
-    # Initialize labels
-    initialize_labels_kernel(dev)(mask_gpu, labels_gpu, width, height, depth, ndrange = ndrange)
-    CUDA.synchronize()
-
-    # Propagate labels iteratively
-    for _ in 1:10 
-        propagate_labels_kernel(dev, workgroupsize)(mask_gpu, labels_gpu, width, height, depth, ndrange = ndrange)
-        CUDA.synchronize()
-    end
-
-    # Download labels back to CPU
-    labels_cpu = Array(labels_gpu)
-    
-    # Find all unique labels and their sizes
-    unique_labels = unique(labels_cpu)
-    label_sizes = [(label, count(labels_cpu .== label)) for label in unique_labels if label != 0]
-
-    # Sort labels by size and get the top n_lcc
-    sort!(label_sizes, by = x -> x[2], rev = true)
-    top_labels = label_sizes[1:min(n_lcc, length(label_sizes))]
-
-    # Create a mask for each of the top n_lcc components
-    components = [labels_cpu .== label[1] for label in top_labels]
-    return components
-end
    -
    -
    -
    -

    Structured configuration of all hyperparameters πŸ†™

    -

    Hyperparameters for the entire pipeline are stored in a JSON configuration file, enabling straightforward adjustments for experimentation (just swap values, save and resume the study). This structured setup allows easy modification of key parameters, such as data set preparation, training settings, data augmentation, and resampling options.

    -
    - -Example configuration: - -
    {
-    "model": {
-        "patience": 10,
-        "early_stopping_metric": "val_loss",
-        "optimizer_name": "Adam",
-        "loss_function_name": "l1",
-        "early_stopping": true,
-        "early_stopping_min_delta": 0.01,
-        "optimizer_args": "lr=0.001",
-        "num_epochs": 10
-    },
-    "data": {
-        "batch_complete": false,
-        "resample_size": [200,101,49],
-        "resample_to_target": false,
-        "resample_to_spacing": false,
-        "batch_size": 3,
-        "standardization": false,
-        "target_spacing": null,
-        "channel_size": 1,
-        "normalization": false,
-        "has_mask": true
-    },
-    "augmentation": {
-        "augmentations": {
-            "Brightness transform": {
-                "mode": "additive",
-                "value": 0.2
-            }
-        },
-        "p_rand": 0.5,
-        "processing_unit": "GPU",
-        "order": [
-            "Brightness transform"
-        ]
-    },
-    "learning": {
-        "Train_Val_Test_JSON": false,
-        "largest_connected_component": false,
-        "n_lcc": 1,
-        "n_folds": 3,
-        "invertible_augmentations": false,
-        "n_invertible": true,
-        
-        "class_JSON_path": false,
-        "additional_JSON_path": false,
-        "patch_size": [50,50,50],
-        "metric": "dice",
-        "n_cross_val": false,
-        "patch_probabilistic_oversampling": false,
-        "oversampling_probability": 1.0,
-        "test_train_validation": [
-            0.6,
-            0.2,
-            0.2
-        ],
-        "shuffle": false
-    }
-}
    -
    -

    Comments:
    The current configuration is loaded as a dictionary, which simplifies access and modification. This setup presents a strong foundation for integrating automated search algorithms for hyperparameter tuning, enabling more efficient model optimization.
    The configuration structure could be reorganized and re-named to improve readability, making it easier for users to locate and adjust specific parameters.

    -
    -
    -

    Visualization of algorithm outputs ⚠️

    -

    This module provides basic visualization functionality by saving output masks and images first to MedImage format and then to Nifti format. The create_nii_from_medimage function from MedImage.jl generates Nifti files, which can be loaded into MedEye3D for 3D visualization.

    -

    Comments:
    Integrating this visualization module more fully with the pipeline could eliminate unnecessary steps. By automatically loading output masks and images as raw data into MedEye3D for 3D visualization and supporting a more efficient end-to-end workflow.

    -
    -
    -

    K-fold cross-validation functionality βœ…

    -

    K-fold cross-validation is implemented to evaluate model performance more robustly. The data is split into multiple folds, with each fold serving as a validation set once, while the others form the training set. This functionality provides a better assessment of model performance across different subsets of the data.

    -
    - -K-fold cross-validation functionality: - -
    ...
-  tstate = initialize_train_state(rng, model, optimizer)
-  if config["learning"]["n_cross_val"]
-      n_folds = config["learning"]["n_folds"]
-      all_tstate = []
-      combined_indices = [indices_dict["train"]; indices_dict["validation"]]
-      shuffled_indices = shuffle(rng, combined_indices)
-      for fold in 1:n_folds
-          println("Starting fold $fold/$n_folds")
-          train_groups, validation_groups = k_fold_split(combined_indices, n_folds, fold, rng)
-          
-          tstate = initialize_train_state(rng, model, optimizer)
-          final_tstate = epoch_loop(num_epochs, train_groups, validation_groups, h5, model, tstate, config, loss_function, num_classes)
-          
-          push!(all_tstate, final_tstate)
-      end
-  else
-      final_tstate = epoch_loop(num_epochs, train_groups, validation_groups, h5, model, tstate, config, loss_function, num_classes)
-  end
-  return final_tstate
-...
    -
    -

    The k_fold_split function organizes the indices for each fold, ensuring comprehensive coverage of the dataset during training.

    -
    - -k_fold_split - -
    function k_fold_split(data, n_folds, current_fold)
-    fold_size = length(data) Γ· n_folds
-    validation_start = (current_fold - 1) * fold_size + 1
-    validation_end = validation_start + fold_size - 1
-    validation_indices = data[validation_start:validation_end]
-    train_indices = [data[1:validation_start-1]; data[validation_end+1:end]]
-    return train_indices, validation_indices
-end
    -
    -
    -
    -
    -

    Conclusions and Future Development

    -

    I have successfully established a foundation for a medical imaging pipeline, addressing significant challenges in data handling, model training, and augmentation integration. The integration of dataset-wide functions has significantly enhanced the reproducibility and handling of batched data with GPU support enabling scalability of experiments, making it easier for researchers and practitioners to produce better results.

    -
    -
    -

    Future Development

    -

    As we look to the future, there are several areas where MedPipe3D can be expanded and improved to better serve the medical AI community. These include:

    -
    -

    Necessary Enhancements

    -

    Comprehensive Logging: Develop detailed logging mechanisms that capture a wide range of events, including system statuses, model performance metrics, and user activities, to facilitate debugging and system optimization. This is currently executed as a simple println function.

    -

    TensorBoard Integration: Implement an interface for TensorBoard to allow users to visualize training dynamics in real time, providing insights into model behavior and performance trends.

    -

    Error and Warning Logs: Introduce advanced error and warning logging capabilities to alert users of potential issues before they affect the pipeline’s performance, ensuring smoother operations and maintenance.

    -

    Automated Visualization: Integrate MedEye3D directly into MedPipe3D to enable automated visualization of outputs, such as segmentation masks or other relevant medical imaging features. This feature would provide users with real-time visual feedback on model performance and data quality. Code-Level Documentation: Due to needed changes in the fundamental structure of the pipeline in the final phase of the project, it is necessary to reevaluate all documentation.

    -

    Official JuliaHealth Documentation: Extend the documentation efforts to include official entries on juliahealth.org, providing a centralized and authoritative resource for users seeking to learn more about MedPipe3D and its capabilities with examples shown

    -
    -
    -

    Potential Enhancements

    -

    GPU support for interpolation will allow for significant acceleration of such functions as Scale transform, Simulate, Low-resolution transform, Elastic deformation transform, and Resampling spacing.

    -

    Add more reversible augmentations to test time.

    -

    Calculating the average of the edges of the picture: checking the type of photo and calculating more correctly on this basis

    -

    Elastic deformation transforms with the simulation of different tissue elasticities.

    -
    -
    -
    -

    Acknowledgments πŸ™‡β€β™‚οΈ

    -

    I would like to express my deepest gratitude to my mentor Dr. Jakub Mitura for his invaluable guidance and support throughout this project. His expertise and encouragement were instrumental in overcoming challenges and achieving project milestones.

    - - - - -
    - - Back to top

    Citation

    BibTeX citation:
    @online{zubik2024,
-  author = {Zubik, Jan},
-  title = {GSoC ’24: {Adding} Dataset-Wide Functions and Integrations of
-    Augmentations},
-  date = {2024-11-03},
-  url = {https://juliahealth.org/JuliaHealthBlog/posts/JZubik-gsoc/GSoC_Jan_Zubik_MedPipe3D.html},
-  langid = {en}
-}
-
    For attribution, please cite this work as:
    -Zubik, Jan. 2024. β€œGSoC ’24: Adding Dataset-Wide Functions and -Integrations of Augmentations.” November 3, 2024. https://juliahealth.org/JuliaHealthBlog/posts/JZubik-gsoc/GSoC_Jan_Zubik_MedPipe3D.html. -
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    GSoC ’24: Adding functionalities to medical imaging visualizations

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    - A summary of my project for Google Summer of Code - 2024 -
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    Author
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    Divyansh Goyal

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    Published
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    November 1, 2024

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    Hello Everyone! πŸ‘‹

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    I am Divyansh, an undergraduate student from Guru Gobind Singh Indraprastha university, majoring in Artificial Intelligence and Machine Learning. Stumbling upon projects under the Juliahealth sub-ecosystem of medical imaging packages, the intricacies of imaging modalities and file formats, reflected in their relevant project counterparts, captured my interest. Working with standards such as NIfTI (Neuroimaging Informatics Technology Initiative) and DICOM (Digital Imaging and Communications in Medicine) with MedImages.jl, I became interested in the visualization routines of such imaging datasets and their integration within the segmentation pipelines for modern medical-imaging analysis.

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    In this post, I’d like to summarize what I did this summer and everything I learned along the way, contributing to MedEye3d.jl medical imaging visualizer under GSOC-2024!

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    If you want to learn more about me, you can connect with me on LinkedIn and follow me on GitHub

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    Background

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    What is MedEye3d.jl?

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    MedEye3D.jl is a package under the Julia language ecosystem designed to facilitate the visualization and annotation of medical images. Tailored specifically for medical applications, it offers a range of functionalities to enhance the interpretation and analysis of medical images. MedEye3D aims to provide an essential tool for 3D medical imaging workflow within Julia. The underlying combination of Rocket.jl and ModernGL.jl ensures the high-performance robust visualizations that the package has to offer.

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    MedEye3d.jl is open-source and comes with an intuitive user interface (To learn more about MedEye3d, you can read the paper introducing it here [1]).

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    What features does this project encompass?

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    This project covers implementation of several tasks that will enable the establishment of additional important functionalities within the MedEye3D package, facilitating enhancements within the visualization’s windowing for MRI and PET data, support for super voxels (sv), improved load times, high-level functionality implementation and robust viewing for multiple images.

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    Project Goals

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    The goals outlined by Dr. Jakub Mitura (my project mentor) and I, beginning of this summer were:

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    1. Migration of package reliance from Rocket.jl to base Julia channel and macros: The first decision that was made was to fix the issue of screen tearing and flicker, resulting from the Rocket.jl’s actor-subscription mechanism present at the core of MedEye3d.jl’s event-driven programming. Here, Julia’s threadsafe and asynchronous channels provided a way to introduce reactive programming and state management within MedEye3d without the tradeoffs resulting from external packages such as Rocket

      2. -

    2. Implementation of high level functions with simplified basic usage: Prior to this, MedEye3d involved initialization of data, texture specifications and text display for a final visualization. To reduce complexity, methods to abstract such chores were devised and implemented which resulted in the exposure of functions for loading images, accessing display data and modification of display data. This also encompassed the loading of images via MedImages.jl which required prior work for the integration of C++ ITK backend for image I/O.

      3. -

    3. Improved precompilation with decreased outputs to reduce start time

      4. -

    4. Automatic windowing for most common MRI and PET modalities: This task is a step in the direction of maintaining consistent visualizations across MRI and PET’s most common modalities, to mimic images similar to what is displayed within 3dSlicer for the same.

      5. -

    5. Adding support for multi-image viewing with crosshair marker for image registration

      6. -

    6. Adding support for the display of SuperVoxels sv with borders within the image slices to better understand anatomical regions within slices: Supervoxels, described either through indicator masks or meshes, encapsulate regions of interest with distinct image characteristics.

      7. -
    -

    Additionally, we had a few stretch goals which are going to be a work in progress:

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    1. Visualization of structures by 3D rendering using OpenGL,

      2. -

    2. Support for MedVoxelHD visualization by voxel-based Hausdorff distance computation.

      3. -

    3. Support for OSX users

      4. -
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    Tasks

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    1. Migration of package from Rocket to Julia’s Base.Channel

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    Initially, there was significant screen-tearing evident from the pixelated display of the rendered text and main image which, furthermore exhibited flickering upon scrolling through the slices in the relevant displayed image’s planar views i.e (Transversal, Coronal and Saggital). Troubleshooting along the way, we narrowed down the issue within the Rocket’s actor-subscription mechanism and decided to integrate Julia’s Base.Channel within MedEye3d.jl for handling the event and state management routine. Julia has asynchronous, threadsafe channels which facilitate in asynchronous programming with the help of a producer-consumer mechanism. An example usage of Base.Channel is as follows:

    -
    function consumer(channel::Base.Channel)
-    while(true)
-    channelData::String = take!(channel)
-    println("Channel got " * channelData)
-    end
-end
-
-newChannel = Base.Channel(100)
-
-@async consumer(newChannel)
-put!(newChannel, "apples")
    -

    Julia’s multiple dispatch made for the architectural setup of MedEye3d, facilitated fixing the issue of screen tearing. Below is how the on_next! function, invokes different reactive components based on the types of arguments it is dealing with.

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    Dump data in channel -> fetch data from the channel in an event loop -> invoke on_next!(state, channelData) -> invoke relevant functionality based on the type of arguments passed

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    The end result was a visualizer with a seamless display of a CT image without any pixelating artifacts.

    -

    -
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    2. Implementation of high level functions with simplified basic usage

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    Implementing a bare-bones image visualization required a lot of function calls and definitions, in order to execute the following phases:

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    1. Rendering an image-plane with OpenGL

      2. -

    2. Loading data slices from the image

      3. -

    3. Creating texture specifications for modalities

      4. -

    4. Producing the final segmentation display

      5. -
    -

    In order to simplify basic usage, high-level abstractions were put in place with the help of MedImages.jl (under ongoing development) library to load images in the form of MedImage objects to formulate a single display function for the user. Further simplifications were made to accommodate options for the user to manipulate the imaging data that is displayed currently in the visualizer i.e retrieval of voxel arrays and their modification. Taking this in mind, the following relevant functions were exposed:

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    MedEye3d.SegmentationDisplay.displayImage()
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    MedEye3d.DisplayDataManag.getDisplayedData()
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    MedEye3d.DisplayDataManag.setDisplayedData()
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    Putting all of the above functions to use together, we can launch the visualizer, retrieve the displayed voxel data and modify it to our liking. A sample script to achieve the former, is highlighted below:

    -
    using MedEye3d
-ctNiftiImage = "/home/hurtbadly/Downloads/ct_soft_study.nii.gz"
-medEyeStruct = MedEye3d.SegmentationDisplay.displayImage(ctNiftiImage)
-displayData = MedEye3d.DisplayDataManag.getDisplayedData(medEyeStruct, [Int32(1), Int32(2)]) #passing the active texture number
-
-# We need to check if the return type of the displayData is a single Array{Float32,3} or a vector{Array{Float32,3}}
-# Now in this case we are setting Gaussian noise over the manualModif Texture voxel layer, and the manualModif texture defaults to 2 for active number
-
-displayData[2][:, :, :] = randn(Float32, size(displayData[2]))
-MedEye3d.DisplayDataManag.setDisplayedData(medEyeStruct, displayData)
    -

    The result of this Gaussian noise within the annotation layer, made for an outcome like the following:

    -

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    3. Improved precompilation with decreased outputs to reduce start time

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    Previously, the package’s precompilation was failing in Julia v1.9 and v1.10 due to pattern matching errors arising after the usage of match macros from the Match.jl pkg in MedEye3d’s keymapping workflow between GLFW callbacks from mouse and keyboard. The relevant equivalent native conditional (if-else) statements, resolved the issue and facilitated in successful precompilation of the package. Further, only following minimal outputs were produced during precompilation:

    -

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    Changes highlighted within the following pull-request:

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    https://github.com/JuliaHealth/MedEye3d.jl/pull/12

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    4. Automatic windowing for most common MRI and PET modalities

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    Windowing is a crucial aspect of medical imaging, particularly in MRI (Magnetic Resonance Imaging) and PET (Positron Emission Tomography) modalities. It enables radiologists to enhance the contrast of images, highlighting specific features and improving the overall diagnostic accuracy. Windowing involves controlling the display range of pixel values to optimize the contrast between different tissues or structures. The display range is defined by two values: the minimum (min) and maximum (max) values that contribute to the final range of pixels that are displayed. By adjusting these values, radiologists can enhance or suppress specific features in the image, facilitating a more accurate diagnosis.

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    The setTextureWindow function utilizes a set of predefined keymap controls to simplify the windowing process. The F1-F7 keys are designated for controlling windowing in MRI and PET modalities. The keymap controls are as follows:

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    • F1: Display wide window for bone (CT) or increase minimum value for PET

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    • F2: Display window for soft tissues (CT) or increase minimum value for PET

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    • F3: Display wide window for lung viewing (CT) or increase minimum value for PET

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    • F4: Decrease minimum value for display

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    • F5: Increase minimum value for display

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    • F6: Decrease maximum value for display

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    • F7: Increase maximum value for display

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    Implementation of setTextureWindow Function

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    The setTextureWindow function is designed to update the texture window settings based on the input keymap control. The function takes three arguments:

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    • activeTextur: The current texture specification
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    • stateObject: The state data fields
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    • windowControlStruct: The window control structure containing the letter code for the keymap control
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    The function performs the following steps:

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    1. Checks the letter code of the keymap control and updates the minimum and maximum values of the texture specification accordingly.
      2. -
    2. Updates the uniforms for the texture specification using the controlMinMaxUniformVals function.
      3. -
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    function setTextureWindow(activeTextur::TextureSpec, stateObject::StateDataFields, windowControlStruct::WindowControlStruct)
-    activeTexturName = activeTextur.name
-    displayRange = activeTextur.minAndMaxValue[2] - activeTextur.minAndMaxValue[1]
-    activeTexturStudyType = activeTextur.studyType
-    if windowControlStruct.letterCode == "F1"
-        if activeTexturStudyType == "CT"
-            #Bone windowing in CT
-            activeTextur.minAndMaxValue = Float32.([400, 1000])
-        elseif activeTexturStudyType == "PET"
-            activeTextur.minAndMaxValue[1] += 0.10 * displayRange #windowing for pet, in the case of PET simply increase the minimum by 20% , doing the same in f1,f2 and f3
-        end
-    elseif windowControlStruct.letterCode == "F2"
-        if activeTexturStudyType == "CT"
-            activeTextur.minAndMaxValue = Float32.([-40, 350])
-        elseif activeTexturStudyType == "PET"
-            activeTextur.minAndMaxValue[1] += 0.10 * displayRange
-        end
-    elseif windowControlStruct.letterCode == "F3"
-        if activeTexturStudyType == "CT"
-            activeTextur.minAndMaxValue = Float32.([-426, 1000])
-        elseif activeTexturStudyType == "PET"
-            activeTextur.minAndMaxValue[1] += 0.10 * displayRange
-        end
-    elseif windowControlStruct.letterCode == "F4"
-        activeTextur.minAndMaxValue[1] -= 0.20 * displayRange
-    elseif windowControlStruct.letterCode == "F5"
-        activeTextur.minAndMaxValue[1] += 0.20 * displayRange
-    elseif windowControlStruct.letterCode == "F6"
-        activeTextur.minAndMaxValue[2] -= 0.20 * displayRange
-    elseif windowControlStruct.letterCode == "F7"
-        activeTextur.minAndMaxValue[2] += 0.20 * displayRange
-    elseif windowControlStruct.letterCode == "F8"
-        activeTextur.uniforms.maskContribution -= 0.10
-    elseif windowControlStruct.letterCode == "F9"
-        activeTextur.uniforms.maskContribution += 0.10
-    end
-
-    stateObject.mainForDisplayObjects.listOfTextSpecifications = map(texture -> texture.name == activeTexturName ? activeTextur : texture, stateObject.mainForDisplayObjects.listOfTextSpecifications)
-    coontrolMinMaxUniformVals(activeTextur)
-end
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    Bone windowing in CT

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    Bone windowing in PET

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    5. Adding support for multi-image viewing with crosshair marker for image registration

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    Following the mid-term evaluation, MedEye3d.jl underwent a significant enhancement, whereby a multi-image display capability was implemented through a series of refinements. Specifically, a novel approach was adopted, whereby separate OpenGL fragment shaders were introduced to concurrently render images on either side of the visualizer, namely the left and right views. Prior to integrating voxel data into the fragment shaders, an initial series of tests involved evaluating individual colors to validate the integrity of the double image display. A screenshot from one of these critical testing phases is presented below:

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    The shaders were further manipulated to automatically initialize for each of the images separately. Further, the reactive aspect of the visualizer in multi-image display mode was iterated upon and now, instead of a single state management struct, a vector of states was being passed around, facilitating the user to scroll each of the images separately just by simply hovering their mouse over either of the image, activating its relevant associated state struct.

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    Down below, is the struct for state that handles all of the things currently related with an image:

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    @with_kw mutable struct StateDataFields
-  currentDisplayedSlice::Int = 1 # stores information what slice number we are currently displaying
-  mainForDisplayObjects::forDisplayObjects = forDisplayObjects() # stores objects needed to  display using OpenGL and GLFW
-  onScrollData::FullScrollableDat = FullScrollableDat()
-  textureToModifyVec::Vector{TextureSpec} = [] # texture that we want currently to modify - if list is empty it means that we do not intend to modify any texture
-  isSliceChanged::Bool = false # set to true when slice is changed set to false when we start interacting with this slice - thanks to this we know that when we start drawing on one slice and change the slice the line would star a new on new slice
-  textDispObj::ForWordsDispStruct = ForWordsDispStruct()# set of objects and constants needed for text diplay
-  currentlyDispDat::SingleSliceDat = SingleSliceDat() # holds the data displayed or in case of scrollable data view for accessing it
-  calcDimsStruct::CalcDimsStruct = CalcDimsStruct()   #data for calculations of necessary constants needed to calculate window size , mouse position ...
-  valueForMasToSet::valueForMasToSetStruct = valueForMasToSetStruct() # value that will be used to set  pixels where we would interact with mouse
-  lastRecordedMousePosition::CartesianIndex{3} = CartesianIndex(1, 1, 1) # last position of the mouse  related to right click - usefull to know onto which slice to change when dimensions of scroll change
-  forUndoVector::AbstractArray = [] # holds lambda functions that when invoked will  undo last operations
-  maxLengthOfForUndoVector::Int64 = 15 # number controls how many step at maximum we can get back
-  fieldKeyboardStruct::KeyboardStruct = KeyboardStruct()
-  displayMode::DisplayMode = SingleImage
-  imagePosition::Int64 = 1
-  switchIndex::Int = 1
-  mainRectFields::GlShaderAndBufferFields = GlShaderAndBufferFields()
-  crosshairFields::GlShaderAndBufferFields = GlShaderAndBufferFields()
-  textFields::GlShaderAndBufferFields = GlShaderAndBufferFields()
-  spacingsValue::Union{Vector{Tuple{Float64,Float64,Float64}},Tuple{Float64,Float64,Float64}} = [(1.0, 1.0, 1.0)]
-  originValue::Union{Vector{Tuple{Float64,Float64,Float64}},Tuple{Float64,Float64,Float64}} = [(1.0, 1.0, 1.0)]
-  supervoxelFields::GlShaderAndBufferFields = GlShaderAndBufferFields()
-end
    -

    After the integrity of the fragment shaders was verified in multi-image, voxel data for the images was integrated and further modifications to the high-level functions were made and eventually the following script produced a rather appealing result.

    -

    Script for loading the same NIFTI image twice in the visualizer for side-by-side display:

    -
    using MedEye3d
-ctNiftiImage = "/home/hurtbadly/Downloads/ct_soft_study.nii.gz"
-MedEye3d.SegmentationDisplay.displayImage([[ctNiftiImage],[ctNifitImage]])
    -
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    Results in :

    -
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    Crosshair marker for image registration are displayed in the relevant passive image to hightlight the same anatomical regions based on the spatial meta-data of the images i.e spacing, origin and direction. In order to achive the crosshair rendering in the passive image, the following action items were devised:

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    1. Retrieval of GLFW Mouse Callbacks for x and y position of the cursor in window coordinates (0 to window-width) from the active image

      2. -

    2. Conversion of these x and y window coordinates into their relevant active image x and y texture coordinates

      3. -

    3. Conversion of these texture coordinates into real space point with the help of spatial metadata

      4. -

    4. Conversion of the real space point into the texture coordinates of the passive image

      5. -

    5. Conversion of the passive image texture coordinates into their relevant OpenGL coordinate system values (-1 to 1)

      6. -

    6. Rendering of crosshair on OpenGL coordinate in passive image

      7. -
    -

    Conversion between different coordinate systems and accounting for the image’s spatial metadata during calculating proved to be challenging at first, but with multiple revisions, a final solution was achieved with seemingly no noticeable amount of lag or delay. One such frame of [CT] images with crosshair display in multi-image is depicted below:

    -

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    Another frame from the openGL rendering cycle, highlighting PET images with crosshair display in multi-image mode:

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    6. Adding support for the display of SuperVoxels sv with borders within the image slices to better understand anatomical regions within slices

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    In enhancing MedEye3d’s functionality, supporting super voxels (sv) with boundaries becomes paramount. The sv rendering, effectively capturing gradients, serves as the cornerstone for detecting these boundaries within both MRI and PET volumes. Supervoxels, described either through indicator masks or meshes, encapsulate regions of interest with distinct image characteristics. By integrating boundary detection for super-voxels, MedEye3d can offer enhanced segmentation capabilities, enabling more precise delineation and analysis of anatomical structures and pathological regions within medical imaging data.

    -

    Supervoxels are basically a collection of voxels that share similar image properties. For example: in MRI scans of the brain cortex, super voxels could represent clusters of voxels corresponding to specific anatomical regions or functional areas. The main objective of this task was to add support for the display of super voxel-based segmentation of images, followed by some janitorial tasks:

    -
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    1. Display of the borders of super-voxels (sv), extracted using the machine learning algorithms.

      2. -

    2. Checking image gradient agreement with super-voxel borders.

      3. -
    -

    This initial workflow involved, the initialization of relevant buffers in OpenGL for dynamic rendering of lines over the image display, namely vertex array buffers (vao), vertex buffers (vbo) and edge buffers (ebo). Further, these buffers are updated on a scroll event, where the information from the currently displayed slice is passed to the event handler, which invokes a function that updates the vertex buffer (vbo) with new vertices pertaining to the relevant slice number and planar view, precalculated from an HDF5 file during initialization of the visualizer. For instance, if the user is scrolling in the 3rd axis (transversal plane) and is currently on slice 40, the supervoxel display will pertain to edges specifically calculated for that specific slice in that plane.

    -

    Eventually, with ever so increasing number of attempts and a few hurdles along the way, one of which particularly stood out since it marked our first step towards a good direction:

    -
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    Challenges in rendering

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    At last, an appealing result hit our sight.

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    Final result

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    Note: The image borders are intentional to emphasize the size of the visualizer which is currently defaulted to a certain width and height.

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    Note: However, There are a few things left to cover here, most of which revolve around MedImages.jl and documentation for the same. List of PRs that facilitated the completion of the tasks highlighted above:

    -
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    1. https://github.com/JuliaHealth/MedEye3d.jl/pull/21

      2. -

    2. https://github.com/JuliaHealth/MedEye3d.jl/pull/20

      3. -

    3. https://github.com/JuliaHealth/MedEye3d.jl/pull/16

      4. -

    4. https://github.com/JuliaHealth/MedEye3d.jl/pull/14

      5. -

    5. https://github.com/JuliaHealth/MedEye3d.jl/pull/13

      6. -

    6. https://github.com/JuliaHealth/MedEye3d.jl/pull/12

      7. -
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    Contributions Beyond Coding

    -
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    1. Mentoring and Guidance

    -

    I regularly organized meetings with my mentor to seek guidance on project direction and troubleshooting issues in the visualizer. This ensured that I stayed on track, received timely feedback, and addressed any challenges that arose.

    -
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    -

    2. Package Documentation and Community Contribution

    -

    I contributed to other medical imaging sub-ecosystem packages in JuliaHealth, including MedImages.jl and MedEval3D.jl. Specifically, I set up documentation for these packages using DocuementerVitepress.jl. This not only enhanced the functionality of these packages but also helped maintain a coherent and organized package ecosystem.

    -
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    3. Multirepo Management and Collaboration

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    In addition to my work on the MedEye3d visualizer, I made significant contributions to other JuliaHealth repositories, including MedImages.jl and worked over an Insight Toolkit wrapper library ITKIOWrapper.jl for support in image I/O down the road in MedImages.jl. I also maintained relevant documentation and ensured continuous collaboration and synchronization across these packages.

    -
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    Conclusions and Future Development

    -

    Within the scope of this 350-hour project, a comprehensive range of objectives were successfully addressed. Noteworthy achievements include:

    -
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    1. Fixed screen tear and flicker within the visualizer. Integration of threadsafe Julia channels.

      2. -

    2. Achieved multi-image display over CT and PET modalities with crosshair rendering (Although, only one modality can be visualize at a time, i.e either CT | CT or PET | PET).

      3. -

    3. Achieved supervoxel display in single image display mode.

      4. -

    4. Achieved automatic windowing of MRI and PET most common modalities.

      5. -
    -

    Future work would include:

    -
      -

    • Support for the users on Darwin (Apple-based platforms).

      • -

    • Apart from that, we would need to add a function that dynamically allocates the texture number to the manual modification mask, regardless of the number of images passed for display, which is currently defaulted to 2.

      • -

    • Also, in the future, we would explore the stretch goals a bit more rigorously, particularly the implementation of MedVoxelHD within MedEye3d.

      • -
    -
    -
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    Acknowledgements πŸ™‡β€β™‚οΈ

    -
      -

    1. Jakub Mitura: aka, Dr. Jakub Mitura

      2. -

    2. Carlos Castillo Passi: aka, cncastillo

      3. -
    -

    I would like to thank my mentor Dr. Jakub Mitura, for his help through out every phase of this project. The troubleshooting routines around problems would have rendered the project unsuccessful, if not for the support and guidance of my mentor throughout each part of this project. I would also like to thank Jacob Zelko, for leading the Juliahealth community with such vast expertise and leading efforts for engagement amongst the members through monthly meetings. My sincere gratitude towards your support, help and guidance through out the fellowship.

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    - - Back to top

    References

    -
    -
    [1]
    J. Mitura and B. E. Chrapko, β€œ3D medical segmentation visualization in julia with MedEye3d,” Zeszyty Naukowe Warszawskiej WyΕΌszej SzkoΕ‚y Informatyki, vol. nr 25, pp. 57–67, 2021, doi: 10.26348/znwwsi.25.57
    -
    -

    Citation

    BibTeX citation:
    @online{goyal2024,
-  author = {Goyal, Divyansh},
-  title = {GSoC ’24: {Adding} Functionalities to Medical Imaging
-    Visualizations},
-  date = {2024-11-01},
-  url = {https://juliahealth.org/JuliaHealthBlog/posts/divyansh-gsoc/gsoc-2024-fellows.html},
-  langid = {en}
-}
-
    For attribution, please cite this work as:
    -
    D. -Goyal, β€œGSoC ’24: Adding functionalities to medical imaging -visualizations,” Nov. 01, 2024. Available: https://juliahealth.org/JuliaHealthBlog/posts/divyansh-gsoc/gsoc-2024-fellows.html
    -
    - - -
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    GSoC ’24: Developing Tooling for Observational Health Research in Julia

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    gsoc
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    sql
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    observational health
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    analysis
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    - A summary of my project for Google Summer of Code - 2024 -
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    Author
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    Jay Sanjay Landge

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    Published
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    September 7, 2024

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    Hi Everyone! πŸ‘‹

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    I am Jay Sanjay, and I am pursuing a Bachelor’s degree in Computational Sciences and Engineering at the Indian Institute of Technology (IIT) in Hyderabad, India. Coming from a mathematics and data analysis background, I was initially introduced to Julia at my university lectures. Later, I delved more into the language and the JuliaHealth community - an intersection of Julia, Health Research, Data Sciences, and Informatics. Here, I met some of the great folks in JuliaHealth and I decided to take it on as a full-fledged summer project. In this blog, I will briefly describe what my project is and what I did as a part of it.

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    1. You can find my GSoC project archive link

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    2. You can also find the related publication of my work on Zenodo

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    3. If you want to know more about me, you can connect with me on LinkedIn and follow me on GitHub

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    Background

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    What Is Observational Health Research?

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    Observational Health Research refers to studies that analyze real-world data (such as patient medical claims, electronic health records, etc.) to understand patient health. These studies often encompass a vast amount of data concerning patient care. An outstanding challenge here is that these datasets can become very complex and grow large enough to require advanced computing methods to process this information.

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    What Are Patient Pathways?

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    Patient pathways refer to the journey that patients with specific medical conditions undergo in terms of their treatment. This concept goes beyond simple drug uptake statistics and looks at the sequence of treatments patients receive over time, including first-line treatments and subsequent therapies. Understanding patient pathways is essential for analyzing treatment patterns, adherence to clinical guidelines, and the disbursement of drugs. To analyze patient pathways, one would typically use real-world data from sources such as electronic health records, claims data, and registries. However, barriers such as data interoperability and resource requirements have hindered the full utilization of real-world data for this purpose.

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    So to address these challenges we (the JuliaHealth organization and I) want to develop a set of tools to extract and analyze these patient pathways. These sets of tools are based on the Observational Medical Outcomes Partnership (OMOP) Common Data Model, which standardizes healthcare data to promote interoperability.

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    Project Description

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    As part of this project with JuliaHealth, I developed a new package called OMOPCDMPathways.jl. This package is designed for deployment in research projects, particularly those related to health and medical data analysis. This project takes much inspiration from the paper TreatmentPatterns: An r package to facilitate the standardized development and analysis of treatment patterns across disease domains [1] and explores the implementation of some of those ideas to develop new tools within the JuliaHealth Observational Health Subecosystem for exploring patient pathways. Additional new features and approaches were added and explored within this project. Additionally, I have authored a developer guide for the package, providing instructions on its use and contribution. This project provided me with hands-on experience in developing production-level code and exposed me to open-source software development practices. I had the opportunity to work in a team, under my mentors, and ensured the integration of the package with the rest of JuliaHealth, facilitating its adoption and usability within the community.

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    Project Goals

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    As a part of the development, I was majorly engaged in crafting the following functionalities:

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    1. Selecting treatments of interest: The first decision that was made was to decide the time from which the desired treatments of interest should be included in the treatment pathway study. Here the periodPriorToIndex specifies the period (i.e. number of days) before the index date from which treatments should be included.

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    2. Find Treatment History of Patients: Create the treatment history of a patient based on target, event, and exit cohorts. Then filter patient events based on the start and end dates of the target cohort. Third, Calculate the duration of treatment eras and the gap between treatments.

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    3. Filters: Filter the treatment history based on the minEraDuration parameter and EraCollapse parameter.

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    4. Create a Continuous Integration and Continuous Development pipeline for the package.

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    5. Implement the combinationWindow function, which combines treatments with various overlapping strategies.

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    Additionally, we had a few stretch goals which were:

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    1. Composing with JuliaStats Ecosystem

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    2. Novel Visualizations for Pathways

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    Tasks

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    1. Setting Up the Package in JuliaHealth Channel

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    Initially, there was no package as such for generating pathways, so I had to build it from scratch. First, I created the repository with the name OMOPCDMPathways.jl. Once the repository was created, we needed to have a skeleton for a standard Julia repository. For this, we used the PkgTemplates.jl this provided a basic skeleton for the repository that included - folders for test suites, documentation, src code files, GitHub files, README and LICENSE file, TOML and citation files. All this we can further edit and modify as per our work. By default, PkgTemplate.jl uses Documenter.jl for the documentation part but as suggested and discussed with my mentor we decided to shift to DocumenterVitepress.jl for the documentation part. However, we still faced some deployment issues in the new documentation due to a few mistakes in the make.jl file, thanks to Anshul Singhvi for helping fix the Deployment issues with DocumenterVitepress. With this, we were ready with the documentation set up and fully functional. After we had shifted to DocumenterVitepress the main task now was to host the documentation, this was done using Github-Actions, detailed steps for hosting are provided at this page. Then we added the CodeCov to our package by triggering it via a dummy function and a corresponding test case for it. Also, the CI for the package was set up with it. And, now finally the repository was ready with test coverage, CI, and documentation fully functional repository ready. Here’s some snapshots of the documentation set-up:

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    Initial documentation with Documenter.jl

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    New documentation using DocumenterVitepress.jl

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    So, as a part of it, I created this documentation which provides detailed steps for converting docs from Documenter to DocumenterVitepress.

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    2. Loading the PostgreSQL Database

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    The main database we worked on/built analysis was the freely available OMOPCDM Database. The Database was formatted within a PostgreSQL database with installation instructions here are some instructions on how to set up Postgres in a Linux machine. However, I was provided with some more extra synthetic data from my mentor for further testing of the functionalities. Being a very large database we had to strategically download it further, my mentor helped me in setting up the Postgres on my local machine. Once, the database was set up proper testing was performed on it to check if things were as expected. With this, we were done with the database setup as well and could finally dive into the actual code logic for the Pathways synthesis.

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    3. Testing and Development setup on my local computer

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    To get a proper environment for functionality creation and concurrent testing we required a proper testing setup so that we could test the new functions made at the same time. This was done using Revise.jl, which helps to keep Julia sessions running without frequent restarts when making changes to code. It allowed me to edit my code, update packages, or switch git branches during a session, with changes applied immediately in the next command. My mentor helped me set it up, added Revise.jl to the global Julia environment, also PackageCompatUI that provides a terminal text interface to the [compat] section of a Julia Project.toml file, and finally made a Julia script by the name β€œstartup.jl” out of it. This script was then added to /home/jay-sanjay/.julia/config/ path in my local computer.

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    Here is the sample for the startup.jl file:

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    using PackageCompatUI
-using PkgTemplates
-using Revise
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-###################################
-# HELPER FUNCTIONS
-###################################
-function template()
-    Template(;
-        user="jay-sanjay",
-        dir="~/FOSS",
-        authors="jaysanjay <jaysanjay@gmail.com> and contributors",
-        julia=v"1.6",
-        plugins=[
-            ProjectFile(; version=v"0.0.1"),
-            Git(),
-            Readme(),
-            License(; name="MIT"),
-            GitHubActions(; extra_versions=["1.6", "1", "nightly"]),
-            TagBot(),
-            Codecov(),
-            Documenter{GitHubActions}(),
-            Citation(; readme = true),
-            RegisterAction(),
-            BlueStyleBadge(),
-            Formatter(;style = "blue")
-        ],
-    )
-end
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    4. Selecting Treatments of Interest

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    So, as a part of this, we used the previously mentioned research paper and discussion with the mentors we came up with logic for it. The first thing to do was to determine the moment in time from which selected treatments of interest should be included in the treatment pathway. The default is all treatments starting after the index date of the target cohort. For example, for a target cohort consisting of newly diagnosed patients, treatments after the moment of first diagnosis are included. However, it would also be desirable to include (some) treatments before the index date, for instance in case a specific disease diagnosis is only confirmed after initiating treatment. Therefore, periodPriorToIndex specifies the period (i.e. number of days) before the index date from which treatments should be included. We have created two dispatches for this function. After that proper testing and documentation are also added.

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    A basic implementation for it is:

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    1. Construct a SQL query to select cohort_definition_id, subject_id, and cohort_start_date from a specified table, filtering by cohort_id.

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    2. The SQL query construction and execution was done using the FunSQL.jl library, in the below-shown manner:

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    sql = From(tab) |>
-            Where(Fun.in(Get.cohort_definition_id, cohort_id...)) |>
-            Select(Get.cohort_definition_id, Get.subject_id, Get.cohort_start_date) |>
-            q -> render(q, dialect=dialect)
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    1. Executes the constructed SQL query using a database connection, fetching the results into a data frame.

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    2. If the DataFrame is not empty, convert cohort_start_date to DateTime and subtract date_prior from each date, then return the modified DataFrame.

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    This was then be called this:

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    period_prior_to_index(
-        cohort_id = [1, 1, 1, 1, 1], 
-        conn; 
-        date_prior = Day(100), 
-        tab=cohort
-    )
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    5. Filters Applied

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    After this, we where needed to get the patient’s database filtered more finely so that there are minimal variations that can be ignored. The duration of the above extracted event eras may vary a lot and it can be preferable to limit to only treatments exceeding a minimum duration. Hence, minEraDuration specifies the minimum time an event era should last to be included in the analysis. All these implementations were more of Dataframe manipulation where I used DataFrames.jl package.

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    After that proper testing and documentation are also added.

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    A basic implementation for the minEraDuration is: It filters the treatment history DataFrame to retain only those rows where the duration between drug_exposure_end and drug_exposure_start is at least minEraDuration. This function can be used as follows:

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-calculate_era_duration(test_df, 920000)
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-#= ... =#
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-4Γ—3 DataFrame
- Row β”‚ person_id  drug_exposure_start  drug_exposure_end 
-     β”‚ Int64      Float64              Int64             
-─────┼───────────────────────────────────────────────────
-   1 β”‚         1           -3.7273e8          -364953600
-   2 β”‚         1            2.90304e7           31449600
-   3 β”‚         1           -8.18208e7          -80006400
-   4 β”‚         1            1.32918e9         1330387200
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    Another filter we worked on is the EraCollapse. So, let’s suppose a case where an individual receives the same treatment for a long period of time (e.g. need for chronic treatment). Then it’s highly likely that the person would require refills. Now as patients are not 100% adherent, there might be a gap between two subsequent event eras. Usually, these eras are still considered as one treatment episode, and the eraCollapseSize deals with the maximum gap within which two eras of the same event cohort would be collapsed into one era (i.e. seen as a continuous treatment instead of a stop and re-initiation of the same treatment). After that proper testing and documentation are also added.

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    A basic implementation for the eraCollapseSize is: (a) Sorts the data frame by event_start_date and event_end_date. (b) Calculates the gap between each era and the previous era. (c) Filters out rows with gap_same > eraCollapseSize.

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    These functions can be used as follows:

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-#= ... =#
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-EraCollapse(treatment_history = test_df, eraCollapseSize = 400000000)
-4Γ—4 DataFrame
- Row β”‚ person_id  drug_exposure_start  drug_exposure_end  gap_same   
-     β”‚ Int64      Float64              Int64              Float64    
-─────┼───────────────────────────────────────────────────────────────
-   1 β”‚         1           -5.33347e8         -532483200  -1.86373e9
-   2 β”‚         1           -3.7273e8          -364953600   1.59754e8
-   3 β”‚         1           -8.18208e7          -80006400   2.83133e8
-   4 β”‚         1            2.90304e7           31449600   1.09037e8
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    6. Treatment History of the Patients

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    The create_treatment_history function constructs a detailed treatment history for patients in a target cohort by processing and filtering event cohort data from a given DataFrame. It begins by isolating the target cohort based on its cohort_id, adding a new column for the index_year derived from the cohort’s start date. Then, it selects relevant event cohorts based on a provided list of cohort IDs and merges them with the target cohort on the subject_id to associate events with individuals in the target group. The function applies different filtering criteria depending on whether the user is interested in treatments starting or ending within a specified period before the target cohort’s start date (defined by periodPriorToIndex). It keeps only the event cohorts that match the filtering condition, ensuring that only relevant treatments are considered. After filtering, the function calculates time gaps between consecutive cohort events for each patient, adding these gaps to the DataFrame. The final DataFrame provides a history of treatments, including the dates of events and the time intervals between them, offering a clear timeline of treatment for each patient. After that proper testing and documentation are also added.

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    7. CombinationWindow Functionality To Combine Overlapping Treatments

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    Now once we have the filtering of the treatments done, we need to combine the overlapping treatments based on some set of rules. The combinationWindow specifies the time that two event eras need to overlap to be considered a combination treatment. If there are more than two overlapping event eras, we sequentially combine treatments, starting from the first two overlapping event eras.

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    The combination_Window function processes a patient’s treatment history by identifying overlapping treatment events and combining them into continuous treatment periods based on certain rules. It first converts event_cohort_id into strings and sorts the treatment data by person_id, event_start_date, and event_end_date. The helper function selectRowsCombinationWindow calculates gaps between consecutive treatments, marking rows where treatments overlap or occur too closely. In the main loop, the function checks these overlaps and gaps against a specified combinationWindow. If treatments overlap (or nearly overlap), the function adjusts the treatment periods by either merging adjacent rows or splitting rows to create continuous treatment periods. The process continues until all overlapping treatments are combined into one, creating an updated and accurate treatment history. The function ensures the final output reflects realistic treatment windows by handling special cases where gaps between treatments are smaller than the treatment durations themselves.

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    It mainly covers the three cases mentioned in the R-research paper:

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    Switch Case:

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    Condition: If the gap between the two treatment events is smaller than the combinationWindow, but the gap is not equal to the duration of either event. Action: The event_end_date of the previous treatment is set to the event_start_date of the current treatment. This effectively β€œshifts” the previous treatment’s end date to eliminate the gap, merging the treatments into one continuous period. Purpose: This ensures that treatment gaps that are too small (less than combinationWindow) are treated as part of the same treatment window.

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-#= ... =#
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-if -gap_previous < combinationWindow && !(-gap_previous in [duration_era, prev_duration_era])
-    treatment_history[i-1, :event_end_date] = treatment_history[i, :event_start_date]
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    Here is the pictorial representation for the same:

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    FRFS (First Row, First Shortened):

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    Condition: If the gap is larger than or equal to the combinationWindow, or the gap equals the duration of one of the two treatments, and the first treatment ends before or on the same date as the second treatment. Action: A new row is created where the second treatment’s event_end_date is set to the end date of the first treatment. This preserves the overlap but ensures that the earlier treatment period stays intact. Purpose: This prevents unnecessary truncation of the first treatment if it spans the entire overlap window.

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-#= ... =#
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-elseif -gap_previous >= combinationWindow || -gap_previous in [duration_era, prev_duration_era]
-    if treatment_history[i-1, :event_end_date] <= treatment_history[i, :event_end_date]
-        new_row = deepcopy(treatment_history[i, :])
-        new_row.event_end_date = treatment_history[i-1, :event_end_date]
-        append!(treatment_history, DataFrame(new_row'))
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    Here is the pictorial representation for the same:

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    LRFS (Last Row, First Shortened):

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    Condition: If the gap is larger than or equal to the combinationWindow, or the gap equals the duration of one of the treatments, and the first treatment ends after the second treatment. Action: The current treatment’s event_end_date is adjusted to match the event_end_date of the previous treatment. Purpose: This handles cases where the second treatment’s window should be shortened to prevent overlap with the previous treatment, merging them into a single continuous window.

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-#= ... =#
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-else
-    treatment_history[i, :event_end_date] = treatment_history[i-1, :event_end_date]
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    Here is the pictorial representation for the same:

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    Note: However, There are a few things left to cover here, most of which are the documentation and writing the test suite for the same.

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    -
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    Contributions Beyond Coding

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    1. Organizing Meetings and Communication

    -

    Throughout the project, I regularly met with my mentor, [Jacob Zelko], and co-mentor, [Mounika], via weekly Zoom calls to discuss progress and seek guidance. During these meetings, we reviewed my work, identified areas where I needed help, and set clear goals for the upcoming weeks. We used Trello to organize and track these goals, ensuring that nothing was overlooked. My mentors provided detailed insights into specific technical aspects and guided me through the logic behind various functions. Outside of our scheduled meetings, they were always available for quick queries via Slack, ensuring constant support.

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    2. Personal Documentation

    -

    In addition to the notes from our meetings, I maintained personal documentation where I recorded every step I took, including the challenges I faced and the mistakes I made. This helped me reflect on my progress and stay organized throughout the fellowship. Following my selection for GSoC 2024, I also published a blog post on Medium to share my journey and experiences with the Julia Language community.

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    3. Contributions To the Rest of the JuliaHealth Repositories

    -

    Earlier I have contributed a lot to the OMOPCDMCohortCreator.jl including adding new functionalities writing test suites, adding blogs including - Patient Pathways within JuliaHealth. Apart from that I also initiated 3 new releases of this package.

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    Conclusions and Future Development

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    This project was a 350-hour large project since there were many goals to accomplish. Here is what we accomplished:

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    1. Built a new repository in JuliaHealth land dedicated especially to treatment pathways synthesis.

      2. -

    2. CI/CD for the Package and Documentation hosting.

      3. -

    3. All required basic functionalities required to build the pathways.

      4. -

    4. Documentation and test suites added for each.

      5. -
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    Future work would include:

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    • Finish this PR test-suites and documentation are remaining for this PR.

      • -

    • Apart from that, we would need to add a function that sews up all the functionalities of the package so that the user can run the complete pathways analysis by running just one function instead of running each of the functions manually.

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    • Also, in the future, we would explore what statistical functionalities we would need to further explore pathways.

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    • Then, we could explore how to compose JuliaHealth with packages from ecosystems like JuliaStats and JuliaDSP (for time series analysis) that are mentioned in this PR.

      • -

    • And finally work on creating novel visualizations for the Pathways. Commonly used visualizations for treatment pathways (such as sunburst or icicle plots) showing which patients fall under what treatment pathways could be developed as plotting recipes to visualize various aspects of a patient’s care pathway rapidly.

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    Acknowledgements πŸ™‡β€β™‚οΈ

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    1. Jacob S. Zelko: aka, TheCedarPrince

      2. -

    2. Mounika Thakkallapally

      3. -
    -

    Thank you for your continuous help and support throughout the fellowship. Note: This blog post was also written with the assistance of LLM technologies to help with grammar, flow, and spelling!

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    - - Back to top

    References

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    [1]
    A. F. Markus, K. M. Verhamme, J. A. Kors, and P. R. Rijnbeek, β€œTreatmentPatterns: An r package to facilitate the standardized development and analysis of treatment patterns across disease domains,” Computer Methods and Programs in Biomedicine, vol. 225, p. 107081, 2022.
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    Citation

    BibTeX citation:
    @online{sanjay_landge2024,
-  author = {Sanjay Landge, Jay},
-  title = {GSoC ’24: {Developing} {Tooling} for {Observational} {Health}
-    {Research} in {Julia}},
-  date = {2024-09-07},
-  url = {https://juliahealth.org/JuliaHealthBlog/posts/jay-gsoc/gsoc-2024-fellows.html},
-  langid = {en}
-}
-
    For attribution, please cite this work as:
    -
    J. -Sanjay Landge, β€œGSoC ’24: Developing Tooling for Observational -Health Research in Julia,” Sep. 07, 2024. Available: https://juliahealth.org/JuliaHealthBlog/posts/jay-gsoc/gsoc-2024-fellows.html
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    - - -
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    GSoC ’24: IPUMS.jl Small Project

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    geospatial
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    census
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    - A summary of my project for Google Summer of Code -
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    Author
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    Michela Rocchetti

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    Published
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    August 26, 2024

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    Hello! πŸ‘‹

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    Hi! I am Michela, I have a Master’s degree in Physics of Complex Systems and I am currently working as a software engineer in Rome, where I am from. During my studies, I became interested in the use of modeling and AI methods to improve healthcare and how these tools can be used to better understand how cultural and social backgrounds influence the health of individuals. I am also interested in the computational modeling of the brain and the human body and its implications for a better understanding of certain pathological conditions.

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    With these motivations in mind, I heard about Google Summer of Code. Since I had studied Julia in some courses and given that the language is expanding rapidly, I decided to find a project within Julia. As a result, I found the project of Jacob Zelko (@TheCedarPrince) to start this experience.

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    If you want to learn more about me, you can connect with me here: LinkedIn, GitHub

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    Project Description

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    IPUMS is the β€œworld’s largest available single database of census microdata”, providing survey and census data from around the world. It includes several projects that provide a wide variety of datasets. The information and data collected by IPUMS are useful for comparative research, as well as for the analysis of individuals in their life contexts. These data can be used to create a more comprehensive dataset that will facilitate research on the social determinants of health for different types of diseases, social communities, and geographical areas.

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    To learn more about IPUMS, visit the website

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    Tasks and Goals

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    The primary objectives of this proposal are to:

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    1. Develop a native Julia package to interact with the APIs available around the datasets IPUMS provides.

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    2. Provide useful utilities within this package for manipulating IPUMS datasets.

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    3. Compose this package with the wider Julia ecosystem to enable novel research in health, economics, and more.

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    To achieve this, the work was distributed as follows:

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    1. Expand some of the functionality developed in ipumsr IPUMS NHGIS -
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      • Create a link between OpenAPI documentation and the functions internally used in IPUMS.jl: updating already present functions, determining if updating is needed, and testing them
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      • Develop functionality similar to the get_metadata_nghis function present in ipumsr
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    2. Update IPUMS documentation -
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      • Set up and deploy DocumenterVitepress.jl
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      • Write a blog post on how IPUMS.jl can be composed within the ecosystem.
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    How the work was done

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    The first task was to migrate documents from Documenter to DocumenterVitepress.This issue aims to support the significant refactoring underway across JuliaHealth, aimed at improving the discoverability and cohesion of the JuliaHealth ecosystem, particularly about documentation. This issue is intended to create a more attractive entry point for new Julia users interested in health research within the Julia community. To accomplish this task, a dependency of DocumenterVitepress was added to the docs directory of the IPUMS.jl repository. Once this was done, the Documenter.jl make.jl file was migrated into a DocumenterVitepress.jl make.jl file. Working on the make.jl file, the pages structure were added to the web page explaining the IPUMS.jl package. With this in mind, those were added: 1. Home: to explain the main purpose of the package 2. Workflows: to explain the working process 3. How to: to give general information 4. Tutorials: to show how to use IPUMS.jl
    -5. Examples: some examples of activities 6. Mission: to explain why the package is useful for the community 7. References: references used to write the pages.

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    This first task takes some time, especially setting up GitHub and cloning the repository locally. At this point, my experience with GitHub was really limited and I had to learn how to use the Git environment from scratch, for example how to do continuous integration (to commit code to a shared repository), documentation release and merge, and local testing. I found the support of my mentors and searching for material online was really helpful.

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    The second task was to update the documentation of IPUMS.jl by modifying the functionality within the model folder in the IPUMS.jl folder. The main aim of this task was to a description of the function and its attributes, an example of possible implementation and result, and finally to show how to use it. The documentation to be updated as of several types of functions: 1. Data extract 2. Data set 3. Data Table 4. Time series table 5. Error 6. Shapefile. Each of these macro-categories (from 1 to 4) contains a set of functions, each signaling the different expected output and specific purpose. Information about what each function does, and the meaning of each specific input variable, has been found on the IPUMS website and references have been made in the written documentation.

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    How to work with IPUMS

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    After writing down the description of the function and the inputs, examples were formulated, starting from the IPUMS website: when you register at IPUMS, an API key is given. which is used, among other things, to run pre-written code on the website. This code contains examples of these functions, and these examples have been adapted by changing some input values and adapting them to work in the Julia framework. The latter task was done by simply rewriting some structures, such as dictionaries, maps, or lists, in the Julia language. Here is a small guide on how to set up working with the API: 1. Create an IPUMS account 2. Log in to your account 3. Copy the API key, which can be obtained from the website 4. Use the key to run the code that is already available on the IPUMS Developer Portal, where you will also find information about the variables and packages.

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    Functions testing

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    A final task was to test the functions in the β€˜api_IPUMSAPI.jl’ file. In this file, the function to be tested and other functions are defined and the most important ones are extracted to be available in the available throughout the framework. Some of the functions to be tested were the following:

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    1. metadata_nhgis_data_tables_get
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    Before working on the Julia files, testing and understanding the original R function was done using R studio.

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    Each function was then tested using the API key from the IPUMS registration as well as other input examples taken from the documentation or the IPUMS website. or from the IPUMS website. All functions were displayed successfully, giving the expected result, so it can be concluded that the translation from R to Julia is successful.

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    using IPUMS
-using OpenAPI
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-api_key = "insert your key here"
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-version = "2"
-page_number = 1
-page_size = 2500
-#media_type = 
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-api = IPUMSAPI("https://api.ipums.org", Dict("Authorization" => api_key));
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-res1 = metadata_nhgis_data_tables_get(api, version)
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-res2 = metadata_nhgis_datasets_dataset_get(api, "2022_ACS1", "2");
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-res3 = metadata_nhgis_datasets_dataset_data_tables_data_table_get(api, "2022_ACS1","B01001", "2");
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    An example of the output is:

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-{
-  "name": "NT1",
-  "nhgisCode": "AAA",
-  "description": "Total Population",
-  "universe": "Persons",
-  "sequence": 1,
-  "datasetName": "1790_cPop",
-  "nVariables": [
-    1
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-}
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    Accomplished Goals and Future Development

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    The project was a 90-hour small project and during this time the documentation was completed and the testing of the metadata function was done, as well as the migration from Documenter.jl to DocumenterVitepress.jl. During these months some things took longer than I expected because of some problems that occurred, so some things were missing in relation to the original plan. However, this time was useful for learning new things: - I saw how to work with a package under development, how to work with large datasets, and how to write documentation - I had the opportunity to better understand how to work with Git and GitHub - I learned some new things about R, which was a completely unknown language to me. - I deepened my knowledge of Julia, a language I had worked with during my time at university. - I had the chance to work on a large open-source project, to be part of a large community, and to learn how to communicate with it efficiently.

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    A special thanks goes to my mentors, Jacob Zelko and Krishna Bhogaonker, for helping me through this process.

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    Future developments of this work could include deepening the work that my mentors and I have started, with the possibility of integrating this package with other machine learning packages in Julia and, from there, doing new analyses of the data in terms of social and geographical implications for health.

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    Citation

    BibTeX citation:
    @online{rocchetti2024,
-  author = {Rocchetti, Michela},
-  title = {GSoC ’24: {IPUMS.jl} {Small} {Project}},
-  date = {2024-08-26},
-  url = {https://juliahealth.org/JuliaHealthBlog/posts/michela-gsoc/Michela_JSoC.html},
-  langid = {en}
-}
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    For attribution, please cite this work as:
    -Rocchetti, Michela. 2024. β€œGSoC ’24: IPUMS.jl Small -Project.” August 26, 2024. https://juliahealth.org/JuliaHealthBlog/posts/michela-gsoc/Michela_JSoC.html. -
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    GSoC Co-Mentoring Experience

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    Mounika Thakkallapally

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    September 12, 2024

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    Introduction

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    Hello πŸ‘‹, I am Mounika. I am a Data Engineer at Brown Center for Biomedical Informatics. This summer, I had the privilege of co-mentoring a talented student, Jay Sanjay alongside Jacob Zelko (@TheCedarPrince) on a project for Google Summer of Code (aka GSoC). Here, I would like to share my experience as a co-mentor, offering insights for future mentors and students alike.

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    Before diving into my experience, let me provide some background on how it all started. At JuliaCon 2023, I had the chance to meet Jacob Zelko and have been following his work at JuliaHealth ever since. One day, I received a message from Jacob asking if I’d be interested in co-mentoring Jay for his GSoC project. Fortunately, I was already working on several projects at BCBI involving Julia programming, OMOP CDM databases and OHDSI tools, all of which were closely aligned with Jay’s project.

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    Feel free to visit Jay’s work on OMOPCDMPathways.jl or read about his fellowship experience from this post.

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    Mentor-Mentee Relationship

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    Jay, being a proactive student with a strong involvement in JuliaHealth, worked closely with Jacob to build a proposal for the project several months before GSoC began this year. His early involvement and familiarity with the community set a solid foundation for the project. Jacob, with his extensive experience mentoring GSoC students over the years, brought invaluable insights not only for Jay but also for me, as I was just beginning my journey as a mentor.

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    Jacob established regular weekly Zoom meetings for the three of us to discuss Jay’s progress, review his accomplishments, and plan the next steps. During these meetings, I focused on taking detailed notes to ensure we stayed organized and up to date with all the tasks. We used Trello, a project management tool, to track progress and manage project tasks efficiently. Additionally, we stayed connected thoughout the week via a dedicated slack channel for any ongoing discussions or questions (on the Julia Slack).

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    Technical Discussion

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    Jay’s project β€œDeveloping Tooling for Observational Health Research in Julia” was inspired by the TreatmentPatterns R package [1]. The main goal of the project was to enhance observational research capabilities within the JuliaHealth ecosystem. To help Jay get started, Jacob created 10 to 15 GitHub issues, each linked to a specific function that Jay planned to work on.

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    During our weekly meeting, we discussed the challenges Jay encountered, any roadblacks in his progress, and reviewed the pull requests he submitted on GitHub. These sessions allowed us to provide timely feedback and guide Jay through complex technical issues, ensuring steady progress throughout the project.

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    Learnings and Observations

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    Jay’s proactive approach, steady progress, thoughtful questions, and clear focus on completing the project are qualities from which every student can benefit. His dedication to learning and problem-solving made a significant impact on the success of the project.

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    Tips for Mentees

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    From a mentee’s perspective having the following qualities would be helpful

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    1. Stick to the proposal: While it’s natural to feel the urge explore new ideas beyond the original proposal, it’s essential to remain focused on the original proposal due to time constrains.

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    2. Adaptability and open-mindedness: Be open to feedback and willing to adjust the tasks as you face challenges.

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    3. Time Management: Many students juggle internships, interviews and other commitments during the summer. So it’s to manage time effectively and discuss with the mentor about the progress during those times.

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    4. Effective communication: Stay up to date with any updates from GSoC or from the mentor. Keeping your mentor updated about your progress or any challenges helps build a collaborative and supportive mentor relationship.

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    Tips for Mentors

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    On the other hand, Jacob demonstrated what it means to be an effective mentor. He showed me how to foster a supportive, collaborative relationship with the student. These are the lessons that I will carry forward in future mentorship roles:

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    From a mentor’s perspective having the following qualities would be helpful

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    1. Clear communication: Communicating well in advance about the availability to meet or to review the work, having frequent meetings with the mentee would be helpful.

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    2. Encouragement: While offering support, it’s important to encourage the mentee to take ownership of the project.

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    3. Commitment and time: Mentoring GSoC is a voluntary role, often taken on in addition to regular professional work. Balancing GSoC with other work commitments requires effective time management and commitment.

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    4. Structured Guidance: Providing a well-organized plan, such as using task management tools like Trello and GitHub issues, ensures that the mentee can follow a clear path towards success completion of the project.

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    Conclusion

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    Google Summer of Code offers an incredible opportunity for students to hone their programming skills while contributing to impactful open-source projects. It was a rewarding experience to be part of this journey as a co-mentor, and I am grateful to Jacob for giving me the chance to be involved in such a meaning project with the JuliaHealth community.

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    Through this experience, I not only gained insights into effective mentorship but also deepened my understanding of open-source collaboration and its potential to drive innovation in healthcare. I’m excited to explore further ways I can contribute to the JuliaHealth ecosystem and continue supporting the community.

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    Let’s Keep in Touch!

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    If you would like to know more about me, you can connect with me on Linkedin.

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    References

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    [1]
    A. F. Markus, K. M. Verhamme, J. A. Kors, and P. R. Rijnbeek, β€œTreatmentPatterns: An r package to facilitate the standardized development and analysis of treatment patterns across disease domains,” Computer Methods and Programs in Biomedicine, vol. 225, p. 107081, 2022.
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    Citation

    BibTeX citation:
    @online{thakkallapally2024,
-  author = {Thakkallapally, Mounika},
-  title = {GSoC {Co-Mentoring} {Experience}},
-  date = {2024-09-12},
-  url = {https://juliahealth.org/JuliaHealthBlog/posts/mounika-gsoc-mentor/},
-  langid = {en}
-}
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    For attribution, please cite this work as:
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    M. -Thakkallapally, β€œGSoC Co-Mentoring Experience,” Sep. 12, -2024. Available: https://juliahealth.org/JuliaHealthBlog/posts/mounika-gsoc-mentor/
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    GSoC ’24: Enhancements to KomaMRI.jl GPU Support

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    gsoc
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    gpu
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    simulation
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    - A summary of my project for Google Summer of Code -
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    Author
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    Ryan Kierulf

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    Published
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    August 30, 2024

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    Hi! πŸ‘‹

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    I am Ryan, an MS student currently studying computer science at the University of Wisconsin-Madison. Looking for a project to work on this summer, my interest in high-performance computing and affinity for the Julia programming language drew me to Google Summer of Code, where I learned about this project opportunity to work on enhancing GPU support for KomaMRI.jl.

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    In this post, I’d like to summarize what I did this summer and everything I learned along the way!

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    If you want to learn more about me, you can connect with me here: LinkedIn, GitHub

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    What is KomaMRI?

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    KomaMRI is a Julia package for efficiently simulating Magnetic Resonance Imaging (MRI) acquisitions. MRI simulation is a useful tool for researchers, as it allows testing new pulse sequences to analyze the signal output and image reconstruction quality without needing to actually take an MRI, which may be time or cost-prohibitive.

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    In contrast to many other MRI simulators, KomaMRI.jl is open-source, cross-platform, and comes with an intuitive user interface (To learn more about KomaMRI, you can read the paper introducing it here). However, being developed fairly recently, there are still new features that can be added and optimization to be done.

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    Project Goals

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    The goals outlined by Carlos (my project mentor) and I the beginning of this summer were:

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    1. Extend GPU support beyond CUDA to include AMD, Intel, and Apple Silicon GPUs, through the packages AMDGPU.jl, oneAPI.jl, and Metal.jl

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    2. Create a CI pipeline to be able to test each of the GPU backends

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    3. Create a new kernel-based simulation method optimized for the GPU, which we expected would outperform array broadcasting

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    4. (Stretch Goal) Look into ways to support running distributed simulations across multiple nodes or GPUs

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    Step 1: Support for Different GPU backends

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    Previously, KomaMRI’s support for GPU acceleration worked by converting each array used within the simulation to a CuArray, the device array type defined in CUDA.jl. This was done through a general gpu function. The inner simulation code is GPU-agnostic, as the same operations can be performed on a CuArray or a plain CPU Array. This approach is good for extensibility, as it does not require writing different simulation code for the CPU / GPU, or different GPU backends, and would only work in a language like Julia based on runtime dispatch!

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    To extend this to multiple GPU backends, all that is needed is to generalize the gpu function to convert to either the device types of CUDA.jl, AMDGPU.jl, Metal.jl, or oneAPI.jl, depending on which backend is being used. To give an idea of what the gpu conversion code looked like before, here is a snippet:

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    struct KomaCUDAAdaptor end
-adapt_storage(to::KomaCUDAAdaptor, x) = CUDA.cu(x)
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-function gpu(x)
-    check_use_cuda()
-    return use_cuda[] ? fmap(x -> adapt(KomaCUDAAdaptor(), x), x; exclude=_isleaf) : x
-end
-
-#CPU adaptor
-struct KomaCPUAdaptor end
-adapt_storage(to::KomaCPUAdaptor, x::AbstractArray) = adapt(Array, x)
-adapt_storage(to::KomaCPUAdaptor, x::AbstractRange) = x
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-cpu(x) = fmap(x -> adapt(KomaCPUAdaptor(), x), x)
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    The fmap function is from the package Functors.jl and can recursively apply a function to a struct tagged with @functor. The function being applied is adapt from Adapt.jl, which will call the lower-level adapt_storage function to actually convert to / from the device type. The second parameter to adapt is what is being adapted, and the first is what it is being adapted to, which in this case is a custom adapter struct KomaCUDAAdapter.

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    One possible approach to generalize to different backends would be to define additional adapter structs for each backend and corresponding adapt_storage functions. This is what the popular machine learning library Flux.jl does. However, there is a simpler way!

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    Each backend package (CUDA.jl, Metal.jl, etc.) already defines adapt_storage functions for converting different types to / from corresponding device type. Reusing these functions is preferable to defining our own since, not only does it save work, but it allows us to rely on the expertise of the developers who wrote those packages! If there is an issue with types being converted incorrectly that is fixed in one of those packages, then we would not need to update our code to get this fix since we are using the definitions they created.

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    Our final gpu and cpu functions are very simple. The backend parameter is a type derived from the abstract Backend type of KernelAbstractions.jl, which is extended by each of the backend packages:

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    import KernelAbstractions as KA
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-function gpu(x, backend::KA.GPU)
-    return fmap(x -> adapt(backend, x), x; exclude=_isleaf)
-end
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-cpu(x) = fmap(x -> adapt(KA.CPU(), x), x, exclude=_isleaf)
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    The other work needed to generalize our GPU support involved switching to use package extensions to avoid having each of the backend packages as an explicit dependency, and defining some basic GPU functions for backend selection and printing information about available GPU devices. The pull request for adding support for multiple backends is linked below:

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    https://github.com/JuliaHealth/KomaMRI.jl/pull/405

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    Step 2: Buildkite CI

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    At the time the above pull request was merged, we weren’t sure whether the added support for AMD and Intel GPUs actually worked, since we only had access to CUDA and Apple Silicon GPUs. So the next step was to set up a CI to test each GPU backend. To do this, we used Buildkite, which is a CI platform that many other Julia packages also use. Since there were many examples to follow, setting up our testing pipeline was not too difficult. Each step of the pipeline does the required environment setup and then calls Pkg.test() for KomaMRICore. As an example, here is what the AMDGPU step of our pipeline looks like:

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          - label: "AMDGPU: Run tests on v{{matrix.version}}"
-        matrix:
-          setup:
-            version:
-              - "1"
-        plugins:
-          - JuliaCI/julia#v1:
-              version: "{{matrix.version}}"
-          - JuliaCI/julia-coverage#v1:
-              codecov: true
-              dirs:
-                - KomaMRICore/src
-                - KomaMRICore/ext
-        command: |
-          julia -e 'println("--- :julia: Instantiating project")
-              using Pkg
-              Pkg.develop([
-                  PackageSpec(path=pwd(), subdir="KomaMRIBase"),
-                  PackageSpec(path=pwd(), subdir="KomaMRICore"),
-              ])'
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-          julia --project=KomaMRICore/test -e 'println("--- :julia: Add AMDGPU to test environment")
-              using Pkg
-              Pkg.add("AMDGPU")'
-          
-          julia -e 'println("--- :julia: Running tests")
-              using Pkg
-              Pkg.test("KomaMRICore"; coverage=true, test_args=["AMDGPU"])'
-        agents:
-          queue: "juliagpu"
-          rocm: "*"
-        timeout_in_minutes: 60
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    We also decided that in addition to a testing CI, it would also be helpful to have a benchmarking CI to track performance changes resulting from each commit to the main branch of the repository. Lux.jl had a very nice-looking benchmarking page, so I decided to look into their approach. They were using github-action-benchmark, a popular benchmarking action that integrates with the Julia package BenchmarkTools.jl. github-action-benchmark does two very useful things:

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    1. Collects benchmarking data into a json file and provides a default index.html to display this data. If put inside a relative path in the gh-pages branch of a repository, this results in a public benchmarking page which is automatically updated after each commit!

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    2. Comments on a pull request with the benchmarking results compared with before the pull request. Example: https://github.com/JuliaHealth/KomaMRI.jl/pull/442#pullrequestreview-2213921334

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    The only issue was that since github-action-benchmark is a github action, it is meant to be run within github by one of the available github runners. While this works for CPU benchmarking, only Buildkite has the CI setup for each of the GPU backends we are using, and Lux.jl’s benchmarks page only included CPU benchmarks, not GPU benchmarks (Note: we talked with Avik, the repository owner of Lux.jl, and Lux.jl has since adopted the approach outlined below to display GPU and CPU benchmarks together). I was not able to find any examples of other julia packages using github-action-benchmark for GPU benchmarking.

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    Fortunately, there is a tool someone developed to download results from Buildkite into a github action (https://github.com/EnricoMi/download-buildkite-artifact-action). This repository only had 1 star when I found it, but it does exactly what we needed: it identifies the corresponding Buildkite build for a commit, waits for it to finish, and then downloads the artifacts for the build into the github action it is being run from. With this, we were able to download the Buildkite benchmark results from a final aggregation step into our benchmarking action and upload to github-action-benchmark to publish to either the main data.js file for our benchmarking website, or pull request.

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    Our final benchmarking page looks like this and is publicly accessible:

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    One neat thing about github-action-benchmark is that the default index.html is extensible, so even though by deault it only shows time, the information for memory usage and number of allocations is also collected into the json file, and can be displayed as well.

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    A successful CI run on Buildkite Looks like this:

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    The pull requests for creating the CI testing and benchmarking pipeline, and changing the index.html for our benchmark page are listed below:

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    1. https://github.com/JuliaHealth/KomaMRI.jl/pull/411
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    2. https://github.com/JuliaHealth/KomaMRI.jl/pull/418
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    Step 3: Optimization

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    With support for multiple backends enabled, and a robust CI, the next step was to optimize our simulation code as much as possible. Our original idea was to create a new GPU-optimized simulation method, but before doing this we wanted to look more at the existing code and optimize for the CPU.

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    The simulation code is solving a differential equation (the [Bloch equations(https://en.wikipedia.org/wiki/Bloch_equations)]) over time. Most differential equation solvers step through time, updating the current state at each time step, but our previous simulation code, more optimized for the GPU, did a lot of computations across all time points in a simulation block, allocating a matrix of size Nspins by NΞ”t each time this was done. Although this is beneficial for the GPU, where there are millions of threads available on which to parallelize these computations, for the CPU it is more important to conserve memory, and the aforementioned approach of stepping through time is preferable.

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    After seeing that this approach did help speed up simulation time on the CPU, but was not faster on the GPU (7x slower for Metal!) we decided to separate our simulation code for the GPU and CPU, dispatching based on the KernelAbstractions.Backend type depending on if it is <:KernelAbstractions.CPU or <:KernelAbstractions.GPU.

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    Other things we were able to do to speed up CPU computation time:

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    1. Preallocating each array used inside the core simulation code so it can be re-used from one simulation block to the next.

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    2. Skipping an expensive computation if the magnetization at that time point is not added to the final signal

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    3. Ensuring that each statement is fully broadcasted. We were surprised to see the difference between the following examples:

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    #Fast
-Bz = x .* seq.Gx' .+ y .* seq.Gy' .+ z .* seq.Gz' .+ p.Ξ”w ./ T(2Ο€ .* Ξ³)
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-#Slow
-Bz = x .* seq.Gx' .+ y .* seq.Gy' .+ z .* seq.Gz' .+ p.Ξ”w / T(2Ο€ * Ξ³)
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    1. Using the cis function for complex exponentiation, which is faster than exp
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    With these changes, the mean improvement in simulation time aggregating across each of our benchmarks for 1, 2, 4, and 8 CPU threads was ~4.28. For 1 thread, the average improvement in memory usage was 90x!

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    The next task was optimizing the simulation code for the GPU. Although our original idea was to put everything into one GPU kernel, we found that the existing broadcasting operations were already very fast, and that custom kernels we wrote were not able to outperform the previous implementation. The Julia GPU compiler team deserves a lot of credit for developing such fast broadcasting implementations!

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    However, this does not mean that we were unable to improve the GPU simulation time. Similar to with the CPU, preallocation made a substantial difference. Parallelizing as much work as possible across the time points for a simulation block was also found to beneficial. For the parts that needed to be done sequentially, a custom GPU kernel was written which used the KernelAbstractions.@localmem macro for arrays being updated at each time step to yield faster memory access.

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    The mean speedup we saw across the 4 supported GPU backends was 4.16, although this varied accross each backend (for example, CUDA was only 2.66x faster while oneAPI was 28x faster). There is a remaining bottleneck in the run_spin_preceession! function having to do with logical indexing that I was not able to resolve, but could be solved in the future to speed up the GPU simulation time even further!

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    The pull requests optimizing code for the CPU and GPU are below:

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    1. https://github.com/JuliaHealth/KomaMRI.jl/pull/443

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    2. https://github.com/JuliaHealth/KomaMRI.jl/pull/459

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    3. https://github.com/JuliaHealth/KomaMRI.jl/pull/462

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    4. Step 4: Distributed Support

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    This last step was a stretch goal for exploring how to add distributed support to KomaMRI. MRI simulations can become quite large, so it is useful to be able to distribute work across either multiple GPUs or multiple compute nodes.

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    A nice thing about MRI simulation is the independent spin property: if a phantom object (representing, for example a brain tissue slice) is divided into two parts, and each part is simulated separately, the signal result from simulating the whole phantom will be equal to the sum of the signal results from simulating each subdivision of the original phantom. This makes it quite easy to distribute work, either across more than one GPU or accross multiple compute nodes.

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    The following scripts worked, with the only necessary code change to the repository being a new + function to add two RawAcquisitionData structs:

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    #Use multiple GPUs:
-using Distributed
-using CUDA
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-#Add workers based on the number of available devices
-addprocs(length(devices()))
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-#Define inputs on each worker process
-@everywhere begin
-    using KomaMRI, CUDA
-    sys = Scanner()
-    seq = PulseDesigner.EPI_example()
-    obj = brain_phantom2D()
-    #Divide phantom
-    parts = kfoldperm(length(obj), nworkers())
-end
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-#Distribute simulation across workers
-raw = Distributed.@distributed (+) for i=1:nworkers()
-    KomaMRICore.set_device!(i-1) #Sets device for this worker, note that CUDA devices are indexed from 0
-    simulate(obj[parts[i]], seq, sys)
-end
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    #Use multiple compute nodes
-using Distributed
-using ClusterManagers
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-#Add workers based on the specified number of SLURM tasks
-addprocs(SlurmManager(parse(Int, ENV["SLURM_NTASKS"])))
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-#Define inputs on each worker process
-@everywhere begin
-    using KomaMRI
-    sys = Scanner()
-    seq = PulseDesigner.EPI_example()
-    obj = brain_phantom2D()
-    parts = kfoldperm(length(obj), nworkers())
-end
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-#Distribute simulation across workers
-raw = Distributed.@distributed (+) for i=1:nworkers()
-    simulate(obj[parts[i]], seq, sys)
-end
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    Pull reqeust for adding these examples to the KomaMRI documentation: https://github.com/JuliaHealth/KomaMRI.jl/pull/468

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    Conclusions / Future Work

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    This project was a 350-hour large project, since there were many goals to accomplish. To summarize what changed since the beginning of the project:

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    1. Added support for AMDGPU.jl, Metal.jl, and oneAPI.jl GPU backends

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    2. CI for automated testing and benchmarking accross each backend + public benchmarks page

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    3. Significantly faster CPU and GPU performance

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    4. Demonstrated distributed support and examples added in documentation

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    Future work could look at ways to further optimize the simulation code, since despite the progress made, I believe there is more work to be done! The aforementioned logical indexing issue is still not resolved, and the kernel used inside the run_spin_excitation! function has not been profiled in depth. KomaMRI is also looking into adding support for higher-order ODE methods, which could require more GPU kernels being written.

    -
    -
    -

    Acknowledgements

    -

    I would like to thank my mentor, Carlos Castillo, for his help and support on this project. I would also like to thank Jakub Mitura, who attended some of our meetings to help with GPU optimization, Dilum Aluthge who helped set up our BuildKite pipeline, and Tim Besard, who answered many GPU-related questions that Carlos and I had.

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    - - Back to top

    Citation

    BibTeX citation:
    @online{kierulf2024,
-  author = {Kierulf, Ryan},
-  title = {GSoC ’24: {Enhancements} to {KomaMRI.jl} {GPU} {Support}},
-  date = {2024-08-30},
-  url = {https://juliahealth.org/JuliaHealthBlog/posts/ryan-gsoc/Ryan_GSOC.html},
-  langid = {en}
-}
-
    For attribution, please cite this work as:
    -Kierulf, Ryan. 2024. β€œGSoC ’24: Enhancements to KomaMRI.jl GPU -Support.” August 30, 2024. https://juliahealth.org/JuliaHealthBlog/posts/ryan-gsoc/Ryan_GSOC.html. -
    - - -
    - - - - - - \ No newline at end of file diff --git a/docs/robots.txt b/docs/robots.txt deleted file mode 100644 index 72763ff..0000000 --- a/docs/robots.txt +++ /dev/null @@ -1 +0,0 @@ -Sitemap: https://juliahealth.org/JuliaHealthBlog/sitemap.xml diff --git a/docs/search.json b/docs/search.json index 2ea616f..9f3abb2 100644 --- a/docs/search.json +++ b/docs/search.json @@ -1,331 +1,303 @@ [ { - "objectID": "about.html", - "href": "about.html", - "title": "About the JuliaHealth Blog", + "objectID": "index.html", + "href": "index.html", + "title": "JuliaHealth", "section": "", - "text": "Yes! We use GoatCounter which is an open-source web analytics platform. It has a very strong privacy policy that forbids tracking users." + "text": "Transforming Health Research!\n \n \n Improving medicine, health and bio-medical research using the power of Julia.\n \n\n\n Explore Packages...\n Below are some of the powerful packages developed by the community.\n \n \n \n Card title\n \n This is a wider card with supporting text below as a natural lead-in to additional\n content. This content is a little bit longer.\n \n Last updated 3 mins ago\n \n \n \n \n Card title\n \n This is a wider card with supporting text below as a natural lead-in to additional\n content. This content is a little bit longer.\n \n Last updated 3 mins ago\n \n \n \n \n Card title\n \n This is a wider card with supporting text below as a natural lead-in to additional\n content. This content is a little bit longer.\n \n Last updated 3 mins ago\n \n \n \n \n Card title\n \n This is a wider card with supporting text below as a natural lead-in to additional\n content. This content is a little bit longer.\n \n Last updated 3 mins ago\n \n \n \n \n Card title\n \n This is a wider card with supporting text below as a natural lead-in to additional\n content. This content is a little bit longer.\n \n Last updated 3 mins ago\n \n \n \n \n \n \n And many more...\n \n \n \n \n \n\n\n Similar Organizations\n We are not the only one, there are other communities researching on various similar aspects.\n \n \n \n Card title\n \n This is a wider card with supporting text below as a natural lead-in to additional\n content. This content is a little bit longer.\n \n Last updated 3 mins ago\n \n \n \n \n Card title\n \n This is a wider card with supporting text below as a natural lead-in to additional\n content. This content is a little bit longer.\n \n Last updated 3 mins ago\n \n \n \n \n Card title\n \n This is a wider card with supporting text below as a natural lead-in to additional\n content. This content is a little bit longer.\n \n Last updated 3 mins ago\n \n \n \n \n \n \n And many more...\n \n \n \n \n \n\n\n FAQs\n \n \n \n \n Accordion Item #1\n \n \n \n \n This is the first item's accordion body. It is shown by default,\n until the collapse plugin adds the appropriate classes that we use to style each\n element. These classes control the overall appearance, as well as the showing and\n hiding via CSS transitions. You can modify any of this with custom CSS or overriding\n our default variables. It's also worth noting that just about any HTML can go within\n the .accordion-body, though the transition does limit overflow.\n \n \n \n \n \n \n Accordion Item #2\n \n \n \n \n This is the second item's accordion body. It is hidden by default,\n until the collapse plugin adds the appropriate classes that we use to style each\n element. These classes control the overall appearance, as well as the showing and\n hiding via CSS transitions. You can modify any of this with custom CSS or overriding\n our default variables. It's also worth noting that just about any HTML can go within\n the .accordion-body, though the transition does limit overflow.\n \n \n \n \n \n \n Accordion Item #3\n \n \n \n \n This is the third item's accordion body. It is hidden by default,\n until the collapse plugin adds the appropriate classes that we use to style each\n element. These classes control the overall appearance, as well as the showing and\n hiding via CSS transitions. You can modify any of this with custom CSS or overriding\n our default variables. It's also worth noting that just about any HTML can go within\n the .accordion-body, though the transition does limit overflow." }, { - "objectID": "about.html#can-i-trust-my-privacy", - "href": "about.html#can-i-trust-my-privacy", - "title": "About the JuliaHealth Blog", + "objectID": "pages/meeting_notes.html", + "href": "pages/meeting_notes.html", + "title": "Meeting Notes", "section": "", - "text": "Yes! We use GoatCounter which is an open-source web analytics platform. It has a very strong privacy policy that forbids tracking users." + "text": "These are the public notes for the JuliaHealth Community. Notes are published publicly here and are available for comments and review on the public HackMD. Additionally, the notes are hosted publicly on the GitHub and are open for PRs or edits as needed" }, { - "objectID": "posts/mounika-gsoc-mentor/index.html", - "href": "posts/mounika-gsoc-mentor/index.html", - "title": "GSoC Co-Mentoring Experience", - "section": "", - "text": "Hello πŸ‘‹, I am Mounika. I am a Data Engineer at Brown Center for Biomedical Informatics. This summer, I had the privilege of co-mentoring a talented student, Jay Sanjay alongside Jacob Zelko (@TheCedarPrince) on a project for Google Summer of Code (aka GSoC). Here, I would like to share my experience as a co-mentor, offering insights for future mentors and students alike.\nBefore diving into my experience, let me provide some background on how it all started. At JuliaCon 2023, I had the chance to meet Jacob Zelko and have been following his work at JuliaHealth ever since. One day, I received a message from Jacob asking if I’d be interested in co-mentoring Jay for his GSoC project. Fortunately, I was already working on several projects at BCBI involving Julia programming, OMOP CDM databases and OHDSI tools, all of which were closely aligned with Jay’s project.\n\nFeel free to visit Jay’s work on OMOPCDMPathways.jl or read about his fellowship experience from this post." + "objectID": "pages/meeting_notes.html#meeting-summary-americaseuropeafrica-specific", + "href": "pages/meeting_notes.html#meeting-summary-americaseuropeafrica-specific", + "title": "Meeting Notes", + "section": "Meeting Summary (Americas/Europe/Africa Specific)", + "text": "Meeting Summary (Americas/Europe/Africa Specific)\nIn Attendance: Jacob Zelko, Anshul Singhvi, Adam WysokiΕ„ski, Aurora Rossi, Dan Getz, Luna Fazio, Jay Landge, Edwin Mkwanazi, Alice Piller, Thembi Ndimande, Siyabonga Nxumalo, Hlengiwe, Muhammad Mahmoud, Jan Zubik, Sfundo Khumalo, Carlos Castillo Passi, Ram Samarth, Dina Khalid\nLocation: Virtual (Northeastern University Zoom)\nSummary: Introducing new JuliaHealth projects, JuliaHealth blog, Google Summer of Code, and planning a JuliaHealth Day\nKeywords: #juliahealth #meeting #americas #africa #europe #neuro #imaging #gsoc #planning" }, { - "objectID": "posts/mounika-gsoc-mentor/index.html#tips-for-mentees", - "href": "posts/mounika-gsoc-mentor/index.html#tips-for-mentees", - "title": "GSoC Co-Mentoring Experience", - "section": "Tips for Mentees", - "text": "Tips for Mentees\nFrom a mentee’s perspective having the following qualities would be helpful\n\nStick to the proposal: While it’s natural to feel the urge explore new ideas beyond the original proposal, it’s essential to remain focused on the original proposal due to time constrains.\nAdaptability and open-mindedness: Be open to feedback and willing to adjust the tasks as you face challenges.\nTime Management: Many students juggle internships, interviews and other commitments during the summer. So it’s to manage time effectively and discuss with the mentor about the progress during those times.\nEffective communication: Stay up to date with any updates from GSoC or from the mentor. Keeping your mentor updated about your progress or any challenges helps build a collaborative and supportive mentor relationship." + "objectID": "pages/meeting_notes.html#meeting-outcomes", + "href": "pages/meeting_notes.html#meeting-outcomes", + "title": "Meeting Notes", + "section": "Meeting Outcomes", + "text": "Meeting Outcomes\n\nShort-Term Outcomes\n\nJacob follows up with Carlos and Boris about synthetic MRI generation\n\n\n\nLong-Term Outcomes" }, { - "objectID": "posts/mounika-gsoc-mentor/index.html#tips-for-mentors", - "href": "posts/mounika-gsoc-mentor/index.html#tips-for-mentors", - "title": "GSoC Co-Mentoring Experience", - "section": "Tips for Mentors", - "text": "Tips for Mentors\nOn the other hand, Jacob demonstrated what it means to be an effective mentor. He showed me how to foster a supportive, collaborative relationship with the student. These are the lessons that I will carry forward in future mentorship roles:\nFrom a mentor’s perspective having the following qualities would be helpful\n\nClear communication: Communicating well in advance about the availability to meet or to review the work, having frequent meetings with the mentee would be helpful.\nEncouragement: While offering support, it’s important to encourage the mentee to take ownership of the project.\nCommitment and time: Mentoring GSoC is a voluntary role, often taken on in addition to regular professional work. Balancing GSoC with other work commitments requires effective time management and commitment.\nStructured Guidance: Providing a well-organized plan, such as using task management tools like Trello and GitHub issues, ensures that the mentee can follow a clear path towards success completion of the project." + "objectID": "pages/meeting_notes.html#notes", + "href": "pages/meeting_notes.html#notes", + "title": "Meeting Notes", + "section": "Notes", + "text": "Notes\n\nAnnouncements:\n\n\nMeeting recording logistics\n\n\nNew member introductions\n\nLuna Fazio\n\nStatistics PhD\nComing back to epidemiology\nComing back to health roots\n\nAdam WysokiΕ„ski\n\nCreator of NeuroAnalyzer.jl\nPsychiatrist\nMany different modalities of research\n\nAurora Rossi\n\nFunctional MRI\nPhD student\n\nAlice Piller\n\nApplying Julia in bioinformatics\n\nEdwin Mkwanazi\n\nJulia in clinical trials\nLearn more about how to implement more in Julia\n\nCarlos Castillo\n\nCreator of KomaMRI\nPhD student\n\n\nNew contributor round-up!\n\nKomaMRI\nNeuroAnalyzer Adam Wysokinski\n\nJuliaHealth News\n\nNortheastern University RISE Conference\nA JuliaHealth Blog?!?!?\n\nTask Follow-ups\n\nJacob follows up with Carlos and Boris about synthetic MRI generation\n\nGSoC + JuliaHealth\n\nProjects\nImportant dates\nOpen discussion\n\nBrainstorming a JuliaHealth Day\n\nJuliaHealth is growing rapidly!!!\nMight be confusing about where to go/get started\n\nThree core ares\n\nLuna\n\nHad a mixture of working with different data\nPublic health approach\nI as a doctor want to predict for patients\nPerhaps it would be interesting to see what problems they have\n\nPossible approaches\n\n\nJan\n\nMore about pipelines\nWhat’s their strength in practice\nSeeing pipelines in action\n\nRam\n\nHow is Julia being used in health already?\n\n\nGlass Notebooks\n\nCreated by Dale Black\nLink: https://glassnotebook.io\n\nUpcoming and ongoing research opportunities\n\nObservational Health Research at Northeastern Uni\n\nUpcoming Events\n\n\nJuliaCon 2024\n\n\nOpen Discussion" }, { - "objectID": "posts/mounika-gsoc-mentor/index.html#lets-keep-in-touch", - "href": "posts/mounika-gsoc-mentor/index.html#lets-keep-in-touch", - "title": "GSoC Co-Mentoring Experience", - "section": "Let’s Keep in Touch!", - "text": "Let’s Keep in Touch!\nIf you would like to know more about me, you can connect with me on Linkedin." + "objectID": "pages/meeting_notes.html#meeting-summary-americaseuropeafrica-specific-1", + "href": "pages/meeting_notes.html#meeting-summary-americaseuropeafrica-specific-1", + "title": "Meeting Notes", + "section": "Meeting Summary (Americas/Europe/Africa Specific)", + "text": "Meeting Summary (Americas/Europe/Africa Specific)\nIn Attendance: Jay Sanjay, Abhirath Anand, Carlos Castillo, Boris Enrique, Jacob Zelko\nLocation: Virtual (JuliaHealth Google Meet)\nSummary: Medical imagining, fairness and health equity in observational health, and dashboards!\nKeywords: #juliahealth #meeting #americas #africa #europe #fairness #koma #fairness #dashboards" }, { - "objectID": "posts/JZubik-gsoc/GSoC_Jan_Zubik_MedPipe3D.html", - "href": "posts/JZubik-gsoc/GSoC_Jan_Zubik_MedPipe3D.html", - "title": "GSoC ’24: Adding dataset-wide functions and integrations of augmentations", - "section": "", - "text": "These emoticons may resemble hieroglyphics, but very soon you will realize that they mean more than 1000s of lines of code.\n\n\nDescription of the emojis used in the title\n\n\n\nπŸ“ Action Plan: A clear, structured plan that guides each step of the MedPipe3D pipeline.\n\n\n🩻 3D Medical Images: Medical imaging data, such as MRI scans in Nifti format.\n\n\nπŸ“Ž AI Model: The initial AI model that will be trained and refined within the pipeline.\n\n\nπŸ“‰ Loss Function: A function that measures the model’s performance during training, guiding the optimization process.\n\n\nπŸ—ƒοΈ Data Loading: Preparation and loading of data and metadata into HDF5 format.\n\n\nπŸ“š Data Splitting: Dividing data into training, validation, and test sets.\n\n\n♻️ Data Augmentation: Increasing data variability through augmentation.\n\n\nπŸ§‘β€πŸ« AI Training: Using Lux.jl framework to train the AI model.\n\n\nπŸ€– Model: The trained AI model that can perform tasks like segmentation on medical images.\n\n\nπŸ‘οΈ Data for Visualization: Output data, such as masks and segmentations.\n\n\nπŸ“ˆ Performance Logs: Logs and metrics documenting the AI’s performance.\n\n\nβ€οΈβ€πŸ©Ή Purpose of MedPipe3D\n\n\n\n\nIn this post, I’d like to summarize what I did this summer and everything I learned along the way, rebuilding the MedPipe3D medical imaging pipeline. I will not start typically, but so that anyone even a novice can visualize what this project has achieved, while the latter part is intended for more experienced readers. It will be easiest to divide it into 4 steps separated by ➑️ in the title above. Each emoji stands for a different piece of pipeliner and will be described below.\nπŸ“πŸ©»πŸ“ŽπŸ“‰ What we need from the user\nMedPipe3D requires four essential inputs from the user to get started: a clear action plan πŸ“, 3D medical images like MRI scans 🩻, an AI model πŸ“Ž, and a loss function πŸ“‰.\nπŸ—ƒοΈπŸ“šβ™»οΈπŸ§‘β€πŸ« The Pipeline essential AI manufacturing line\nFollowing the plan πŸ“, MedPipe3D loads data, pre-processes, and organizes it πŸ—ƒοΈ. Allowing data to be easily split πŸ“š, and efficiently augmented ♻️ in many ways for learning AI πŸ§‘β€πŸ« model effectively. In the end, performing testing and post-processing for better determination of AI skills.\nIt’s designed to transform raw medical data into a format that your AI can learn from, segmenting meaningful patterns and structures.\nπŸ€–πŸ‘οΈπŸ“ˆ Results and Insights\nMedPipe3D is a tool for researchers and for that, it cannot do without analysis, testing, and evaluation. The result of the pipeline is a model πŸ€– as well as data πŸ‘οΈ and logs πŸ“ˆ needed in MedEval3D that are ready for visualization and further analysis with MedEye3D. In a nutshell, it makes visualizing results easy, tumor locations or other medical features directly as masks on the scans.\nβ€οΈβ€πŸ©Ή Purpose-Driven Technology\nMedPipe3D’s mission goes beyond technology. It’s about providing the tools to create AIs that support healthcare professionals in making faster, more accurate decisions, with the ultimate goal of saving lives.\nThis four-part journey captures the heart of the MedPipe3D toolkit for advancing medical AI, from raw data to life-saving insight.\n\n\nMedPipe3D is a framework created from hundreds of hours over summer vacation, thousands of lines of code, hundreds of mistakes, and most importantly the guidance of my mentor and author of all of these libraries Dr.Β Jakub Mitura. At its core, MedPipe3D combines sophisticated data handling from MedImage thanks to the hard work of Divyansh Goyal. Newly developed pipeline for model training, validation, and testing with existing MedEval3D, and result visualization with MedEye3D. Unfortunately, not all of the project’s goals have been fully achieved, and thereby there is one section ➑️ too many. Hopefully not for long. My name is Jan Zubik, and I wrote this entire library from scratch, which is currently my most complex project.\nIf you are a data scientist, programmer, or code enthusiast, I invite you to read the next section where I go into detail and present version 1 of this tool in detail.\nI’m a 3rd-year student of BSc in Data Science and Machine Learning, I know that many things can be done better, expanded, debugged, and optimized. Now it just works, but don’t hesitate to write to me personally on LinkedIn, Julia’s Slack or GitHub! With your comments, and direct critique you will help me to be a better programmer and one day MedPipe3D will contribute in a tiny way to save someone’s life!\nExact work from the Google Summer of Code project you will find in GitHub the repository." + "objectID": "pages/meeting_notes.html#meeting-outcomes-1", + "href": "pages/meeting_notes.html#meeting-outcomes-1", + "title": "Meeting Notes", + "section": "Meeting Outcomes", + "text": "Meeting Outcomes\n\nShort-Term Outcomes\n\nJacob follows up with Carlos and Boris about synthetic MRI generation\n\nPulls in Jakub and Zachary to discussion\n\n\n\n\nLong-Term Outcomes" }, { - "objectID": "posts/JZubik-gsoc/GSoC_Jan_Zubik_MedPipe3D.html#introduction", - "href": "posts/JZubik-gsoc/GSoC_Jan_Zubik_MedPipe3D.html#introduction", - "title": "GSoC ’24: Adding dataset-wide functions and integrations of augmentations", - "section": "", - "text": "MedPipe3D is a framework created from hundreds of hours over summer vacation, thousands of lines of code, hundreds of mistakes, and most importantly the guidance of my mentor and author of all of these libraries Dr.Β Jakub Mitura. At its core, MedPipe3D combines sophisticated data handling from MedImage thanks to the hard work of Divyansh Goyal. Newly developed pipeline for model training, validation, and testing with existing MedEval3D, and result visualization with MedEye3D. Unfortunately, not all of the project’s goals have been fully achieved, and thereby there is one section ➑️ too many. Hopefully not for long. My name is Jan Zubik, and I wrote this entire library from scratch, which is currently my most complex project.\nIf you are a data scientist, programmer, or code enthusiast, I invite you to read the next section where I go into detail and present version 1 of this tool in detail.\nI’m a 3rd-year student of BSc in Data Science and Machine Learning, I know that many things can be done better, expanded, debugged, and optimized. Now it just works, but don’t hesitate to write to me personally on LinkedIn, Julia’s Slack or GitHub! With your comments, and direct critique you will help me to be a better programmer and one day MedPipe3D will contribute in a tiny way to save someone’s life!\nExact work from the Google Summer of Code project you will find in GitHub the repository." + "objectID": "pages/meeting_notes.html#notes-1", + "href": "pages/meeting_notes.html#notes-1", + "title": "Meeting Notes", + "section": "Notes", + "text": "Notes\n\nNew member introductions\n\nCarlos Castillo\n\nKing’s College London\nPhD student\n\nAbhirath Anand\n\nFinal year undergraduate\nCurious about getting more into life sciences\nBiology and healthcare\n\n\nAnnouncements:\n\nNew meeting times\n\nLast Thursday of every month at 12PM EST\n\nWhy two separate meetings?\n\nOne for Asia/Oceania\n\nThanks Jay Sanjay for running this!!!\n\nOne for Americas/Africa/Europe\nTrying to improve accessibility and inclusion\n\nMeeting recordings\n\nGoing forward, meetings will be recorded\nAdded to a playlist on Julia YouTube page\n\n\nNew contributor round-up!\n\nNothing this meeting\n\nRunning tasks follow-ups:\n\nNothing this meeting\n\nPresentation by Carlos Castillo Passi on GSoC projects on medical imaging.\n\n\nWritten using CuDA\nDoing MRI simulation very quickly\n\nCan be used for machine learning overview\n\nBuilt around several packages with MRI\n\nIncredible work with coverage\n\nSuper friendly GUI\nBloch equations are hard to understand\nGSoC Project\n\nTrying to do actual kernel programming\nKernelAbstractions.jl\nSolving DifferentialEquations.jl\nBoost speed\nImplement new algorithms\nSuggested skills\n\nExperience with Julia\nMRI concepts\nGPU programming\n\nGoals:\n\nNew Bloch kernel methods\nFurther tests on build kite/GPU testing\nDocumentation\n\n\n\n\nFairness and health equity within Observational Health Research\n\nAssessing phenotype fairness\nForthcoming package\nWork done so far\nPaper reference: https://arxiv.org/pdf/2203.05174.pdf\n\nCreating dashboards for JuliaHealth\n\nAnnouncement from Genie.jl\nCustom dashboard components\nQuestion: What would this look like for JuliaHealth?\n\nCreate a standard interface across JuliaHealth packages\nCan interface with a JuliaHealthDashboards package\n\nHealthDashboard.jl?\nCustom components for the general JuliaHealth ecosystem could be housed in package\n\nResearchers can easily build together commonly used health dashboards\n\n\nEvent Reminders\n\nGoogle Summer of Code\nJuliaCon 2024\n\nUpcoming and ongoing research opportunities\n\nObservational Health Research at Northeastern Uni\nGlass Notebooks from Dale Black (Not Discussed; saved for next month)" }, { - "objectID": "posts/JZubik-gsoc/GSoC_Jan_Zubik_MedPipe3D.html#integrate-augmentations-for-medical-data", - "href": "posts/JZubik-gsoc/GSoC_Jan_Zubik_MedPipe3D.html#integrate-augmentations-for-medical-data", - "title": "GSoC ’24: Adding dataset-wide functions and integrations of augmentations", - "section": "Integrate augmentations for medical data πŸ†™", - "text": "Integrate augmentations for medical data πŸ†™\nAugmenting medical data is a crucial step for enhancing model robustness, especially given the variations in imaging conditions and patient anatomy.\n\nThis pipeline currently supports multiple augmentation techniques:\n\nBrightness transform βœ…\nContrast augmentation transform βœ…\nGamma Transform βœ…\nGaussian noise transform βœ…\nRician noise transform βœ…\nMirror transform βœ…\nScale transform πŸ†™\nGaussian blur transform βœ…\nSimulate low-resolution transform πŸ†™\nElastic deformation transform πŸ†™\n\n\nWhich have been fully integrated. Each of these methods helps the model generalize better by simulating diverse imaging scenarios.\n\nComments:\nAugmentations such as scaling, and low-resolution simulation use interpolation that is not yet GPU-accelerated.\nElastic deformation with simulation of different tissue elasticities is a potential development opportunity that would further improve the model’s adaptability by mimicking more complex variations found in medical imaging." + "objectID": "pages/meeting_notes.html#meeting-summary-oceaniaasia-specific", + "href": "pages/meeting_notes.html#meeting-summary-oceaniaasia-specific", + "title": "Meeting Notes", + "section": "Meeting Summary (Oceania/Asia specific)", + "text": "Meeting Summary (Oceania/Asia specific)\nIn Attendance: Jay Sanjay, Abhirath Anand, Jacob Zelko\nLocation: Virtual (JuliaHealth Google Meet)\nSummary: Overview of the Oceania/Asia specific JuliaHealth monthly meeting\nKeywords: #juliahealth #meeting #asia #oceania #llms #beginner" }, { - "objectID": "posts/JZubik-gsoc/GSoC_Jan_Zubik_MedPipe3D.html#invertible-augmentations-and-support-test-time-augmentations", - "href": "posts/JZubik-gsoc/GSoC_Jan_Zubik_MedPipe3D.html#invertible-augmentations-and-support-test-time-augmentations", - "title": "GSoC ’24: Adding dataset-wide functions and integrations of augmentations", - "section": "Invertible augmentations and support test time augmentations πŸ†™", - "text": "Invertible augmentations and support test time augmentations πŸ†™\nThis section focuses on the ability to apply reversible augmentations to test data, allowing the model to be evaluated with different transformations. Only rotation is available at this time. The function evaluate_patches performs this evaluation by applying specified augmentations, dividing the test data into patches, and reconstructing the full image from the patches. During testing, one can choose to use of largest connected component post-processing. Metrics are calculated and results are saved for analysis.\n\n\nevaluate_test:\n\n# ...\nfor test_group in test_groups\n test_data, test_label, attributes = fetch_and_preprocess_data([test_group], h5, config)\n results, test_metrics = evaluate_patches(test_data, test_label, tstate, model, config)\n y_pred, metr = process_results(results, test_metrics, config)\n save_results(y_pred, attributes, config)\n push!(all_test_metrics, metr)\nend\n# ...\nfunction evaluate_patches(test_data, test_label, tstate, model, config, axis, angle)\n println(\"Evaluating patches...\")\n results = []\n test_metrics = []\n tstates = [tstate]\n test_time_augs = []\n\n for i in config[\"learning\"][\"n_invertible\"]\n data = rotate_mi(test_data, axis, angle)\n for tstate_curr in tstates\n patch_results = []\n patch_size = Tuple(config[\"learning\"][\"patch_size\"])\n idx_and_patches, paded_data_size = divide_into_patches(test_data, patch_size)\n coordinates = [patch[1] for patch in idx_and_patches]\n patch_data = [patch[2] for patch in idx_and_patches]\n for patch in patch_data\n y_pred_patch, _ = infer_model(tstate_curr, model, patch)\n push!(patch_results, y_pred_patch)\n end\n idx_and_y_pred_patch = zip(coordinates, patch_results)\n y_pred = recreate_image_from_patches(idx_and_y_pred_patch, paded_data_size, patch_size, size(test_data))\n if config[\"learning\"][\"largest_connected_component\"]\n y_pred = largest_connected_component(y_pred, config[\"learning\"][\"n_lcc\"])\n end\n metr = evaluate_metric(y_pred, test_label, config[\"learning\"][\"metric\"])\n push!(test_metrics, metr)\n end\n end\n return results, test_metrics\nend\nfunction divide_into_patches(image::AbstractArray{T, 5}, patch_size::Tuple{Int, Int, Int}) where T\n println(\"Dividing image into patches...\")\n println(\"Size of the image: \", size(image)) \n\n # Calculate the required padding for each dimension (W, H, D)\n pad_size = (\n (size(image, 1) % patch_size[1]) != 0 ? patch_size[1] - size(image, 1) % patch_size[1] : 0,\n (size(image, 2) % patch_size[2]) != 0 ? patch_size[2] - size(image, 2) % patch_size[2] : 0,\n (size(image, 3) % patch_size[3]) != 0 ? patch_size[3] - size(image, 3) % patch_size[3] : 0\n )\n\n # Pad the image if necessary\n padded_image = image\n if any(pad_size .> 0)\n padded_image = crop_or_pad(image, (size(image, 1) + pad_size[1], size(image, 2) + pad_size[2], size(image, 3) + pad_size[3]))\n end\n\n # Extract patches\n patches = []\n for x in 1:patch_size[1]:size(padded_image, 1)\n for y in 1:patch_size[2]:size(padded_image, 2)\n for z in 1:patch_size[3]:size(padded_image, 3)\n patch = view(\n padded_image,\n x:min(x+patch_size[1]-1, size(padded_image, 1)),\n y:min(y+patch_size[2]-1, size(padded_image, 2)),\n z:min(z+patch_size[3]-1, size(padded_image, 3)),\n :,\n :\n )\n push!(patches, [(x, y, z), patch])\n end\n end\n end\n println(\"Size of padded image: \", size(padded_image))\n return patches, size(padded_image)\nend\n\nfunction recreate_image_from_patches(\n coords_with_patches,\n padded_size,\n patch_size,\n original_size\n)\n println(\"Recreating image from patches...\")\n reconstructed_image = zeros(Float32, padded_size...)\n \n # Place patches back into their original positions\n for (coords, patch) in coords_with_patches\n x, y, z = coords\n reconstructed_image[\n x:x+patch_size[1]-1,\n y:y+patch_size[2]-1,\n z:z+patch_size[3]-1,\n :,\n :\n ] = patch\n end\n\n # Crop the reconstructed image to remove any padding\n final_image = reconstructed_image[\n 1:original_size[1],\n 1:original_size[2],\n 1:original_size[3],\n :,\n :\n ]\n println(\"Size of the final image: \", size(final_image))\n return final_image\nend\n\nComment: In this section, there is significant potential to incorporate additional types of invertible augmentations." + "objectID": "pages/meeting_notes.html#meeting-outcomes-2", + "href": "pages/meeting_notes.html#meeting-outcomes-2", + "title": "Meeting Notes", + "section": "Meeting Outcomes", + "text": "Meeting Outcomes\n\nShort-Term Outcomes\n\n\nLong-Term Outcomes" }, { - "objectID": "posts/JZubik-gsoc/GSoC_Jan_Zubik_MedPipe3D.html#patch-based-data-loading-with-probabilistic-oversampling", - "href": "posts/JZubik-gsoc/GSoC_Jan_Zubik_MedPipe3D.html#patch-based-data-loading-with-probabilistic-oversampling", - "title": "GSoC ’24: Adding dataset-wide functions and integrations of augmentations", - "section": "Patch-based data loading with probabilistic oversampling βœ…", - "text": "Patch-based data loading with probabilistic oversampling βœ…\nIn this section, patches are extracted using extract_patch from the medical images for model training, with a probability-based method to decide between a random patch or a patch with non-zero labels. Helper functions like get_random_patch and get_centered_patch determine the starting indices and dimensions for the patches based on given configurations, while padding methods ensure consistency even if the patch exceeds the original image dimensions. Probabilistic oversampling, as configured, allows for more balanced and informative data sampling, which improves the model’s ability to detect specific medical features.\n\n\nextract_patch:\n\nfunction extract_patch(image, label, patch_size, config)\n # Fetch the oversampling probability from the config\n println(\"Extracting patch.\")\n oversampling_probability = config[\"learning\"][\"oversampling_probability\"]\n # Generate a random number to decide which patch extraction method to use\n random_choice = rand()\n\n if random_choice <= oversampling_probability\n return extract_nonzero_patch(image, label, patch_size)\n else\n\n return get_random_patch(image, label, patch_size)\n end\nend\n#Helper function, in case the mask is emptyClick to apply\nfunction extract_nonzero_patch(image, label, patch_size)\n println(\"Extracting a patch centered around a non-zero label value.\")\n indices = findall(x -> x != 0, label)\n if isempty(indices)\n # Fallback to random patch if no non-zero points are found\n return get_random_patch(image, label, patch_size)\n else\n # Choose a random non-zero index to center the patch around\n center = indices[rand(1:length(indices))]\n return get_centered_patch(image, label, center, patch_size)\n end\nend\n# Function to get a patch centered around a specific index\nfunction get_centered_patch(image, label, center, patch_size)\n center_coords = Tuple(center)\n half_patch = patch_size .Γ· 2\n start_indices = center_coords .- half_patch\n end_indices = start_indices .+ patch_size .- 1\n\n # Calculate padding needed\n pad_beg = (\n max(1 - start_indices[1], 0),\n max(1 - start_indices[2], 0),\n max(1 - start_indices[3], 0)\n )\n pad_end = (\n max(end_indices[1] - size(image, 1), 0),\n max(end_indices[2] - size(image, 2), 0),\n max(end_indices[3] - size(image, 3), 0)\n )\n\n # Adjust start_indices and end_indices after padding\n start_indices_adj = start_indices .+ pad_beg\n end_indices_adj = end_indices .+ pad_beg\n\n # Convert padding values to integers\n pad_beg = Tuple(round.(Int, pad_beg))\n pad_end = Tuple(round.(Int, pad_end))\n\n # Pad the image and label using pad_mi\n image_padded = pad_mi(image, pad_beg, pad_end, 0)\n label_padded = pad_mi(label, pad_beg, pad_end, 0)\n\n # Extract the patch\n image_patch = image_padded[\n start_indices_adj[1]:end_indices_adj[1],\n start_indices_adj[2]:end_indices_adj[2],\n start_indices_adj[3]:end_indices_adj[3]\n ]\n label_patch = label_padded[\n start_indices_adj[1]:end_indices_adj[1],\n start_indices_adj[2]:end_indices_adj[2],\n start_indices_adj[3]:end_indices_adj[3]\n ]\n\n return image_patch, label_patch\nend\n\nfunction get_random_patch(image, label, patch_size)\n println(\"Extracting a random patch.\")\n # Check if the patch size is greater than the image dimensions\n if any(patch_size .> size(image))\n # Calculate the needed size to fit the patch\n needed_size = map(max, size(image), patch_size)\n # Use crop_or_pad to ensure the image and label are at least as large as needed_size\n image = crop_or_pad(image, needed_size)\n label = crop_or_pad(label, needed_size)\n end\n\n # Calculate random start indices within the new allowable range\n start_x = rand(1:size(image, 1) - patch_size[1] + 1)\n start_y = rand(1:size(image, 2) - patch_size[2] + 1)\n start_z = rand(1:size(image, 3) - patch_size[3] + 1)\n start_indices = [start_x, start_y, start_z]\n end_indices = start_indices .+ patch_size .- 1\n\n # Extract the patch directly when within bounds\n image_patch = image[start_indices[1]:end_indices[1], start_indices[2]:end_indices[2], start_indices[3]:end_indices[3]]\n label_patch = label[start_indices[1]:end_indices[1], start_indices[2]:end_indices[2], start_indices[3]:end_indices[3]]\n\n return image_patch, label_patch\nend" + "objectID": "pages/meeting_notes.html#meeting-notes", + "href": "pages/meeting_notes.html#meeting-notes", + "title": "Meeting Notes", + "section": "Meeting Notes", + "text": "Meeting Notes\n\nAbhirath Anand\n\nFinal year CS student in India.\nFormer GSoCer.\n\nWorked on MetalHead.jl\nNo longer quite interested in Computer CV\n\nInterested in JuliaHealth.\n\nPeople excited about separate JuliaHealth meeting\n\nGrown to a separate JuliaHealth meeting for Oceania/Asia specific times.\nWanted more people to join .\n\nDifferent packages and ideas\n\nJuliaHealthLLMs\n\nHow can we use LLMs for JuliaHealth?\n\n\nHow to get started with JuliaHealth - Abhirath\n\nMedical imaging looks well-aligned but want to explore some different.\nWhat is the observational health subecosystem?\n\nGo through documentation of JuliaHealth.\nJay can send some." }, { - "objectID": "posts/JZubik-gsoc/GSoC_Jan_Zubik_MedPipe3D.html#calculate-median-and-mean-spacing-with-resampling", - "href": "posts/JZubik-gsoc/GSoC_Jan_Zubik_MedPipe3D.html#calculate-median-and-mean-spacing-with-resampling", - "title": "GSoC ’24: Adding dataset-wide functions and integrations of augmentations", - "section": "Calculate Median and Mean Spacing with resampling πŸ†™", - "text": "Calculate Median and Mean Spacing with resampling πŸ†™\nThis part ensures that all images in the dataset have consistent real coordinates, spacing, and shape. It’s a critical factor in medical imaging for accurate analysis. Calculating and applying set values, median or mean across images ensures uniformity.\n\nResample images to target image πŸ†™\nThis step aligns each image to the reference coordinates of the main image, ensuring that all images share a common spatial alignment. The resample_to_image function from MedImage.jl is used here, applying interpolation to adjust each image.\n\n\nresample_images_to_target:\n\nif resample_images_to_target && !isempty(Med_images)\n println(\"Resampling $channel_type files in channel '$channel_folder' to the first $channel_type in the channel.\")\n reference_image = Med_images[1]\n Med_images = [resample_to_image(reference_image, img, interpolator) for img in Med_images]\nend\n\nComment: Resample_to_image uses interpolation that is not yet GPU-accelerated in this implementation, this step slows down the data preparation phase significantly.\n\n\nEnsure uniform spacing across the entire dataset πŸ†™\nThis step brings all images to a consistent voxel spacing across the dataset using resample_to_spacing from MedImage.jl. This uniform spacing is crucial for creating a standardized dataset where each image voxel represents the same physical volume.\n\n\nesample_to_spacing:\n\nif resample_images_spacing == \"set\"\n println(\"Resampling all $channel_type files to target spacing: $target_spacing\")\n target_spacing = Tuple(Float32(s) for s in target_spacing)\n channels_data = [[resample_to_spacing(img, target_spacing, interpolator) for img in channel] for channel in channels_data]\nelseif resample_images_spacing == \"avg\"\n println(\"Calculating average spacing across all $channel_type files and resampling.\")\n all_spacings = [img.spacing for channel in channels_data for img in channel]\n avg_spacing = Tuple(Float32(mean(s)) for s in zip(all_spacings...))\n println(\"Average spacing calculated: $avg_spacing\")\n channels_data = [[resample_to_spacing(img, avg_spacing, interpolator) for img in channel] for channel in channels_data]\nelseif resample_images_spacing == \"median\"\n println(\"Calculating median spacing across all $channel_type files and resampling.\")\n all_spacings = [img.spacing for channel in channels_data for img in channel]\n median_spacing = Tuple(Float32(median(s)) for s in all_spacings)\n println(\"Median spacing calculated: $median_spacing\")\n channels_data = [[resample_to_spacing(img, median_spacing, interpolator) for img in channel] for channel in channels_data]\nelseif resample_images_spacing == false\n println(\"Skipping resampling of $channel_type files.\")\n # No resampling will be applied, channels_data remains unchanged.\nend\n\nComment: Resample_to_spacing uses interpolation that is not yet GPU-accelerated in this implementation, this step slows down the data preparation phase significantly.\n\n\nResizing all channel files to average or target size βœ…\nTo create a cohesive 5D tensor, all images in each channel are resized to a uniform shape, either the average size of all images or a specific target size. This resizing process uses crop_or_pad, ensuring that all images match the specified dimensions, making them suitable for model input.\n\n\ncrop_or_pad:\n\nif resample_size == \"avg\"\n sizes = [size(img.voxel_data) for img in channels_data for img in img] # Get sizes from all images\n avg_dim = map(mean, zip(sizes...))\n avg_dim = Tuple(Int(round(d)) for d in avg_dim)\n println(\"Resizing all $channel_type files to average dimension: $avg_dim\")\n channels_data = [[crop_or_pad(img, avg_dim) for img in channel] for channel in channels_data]\nelseif resample_size != \"avg\"\n target_dim = Tuple(resample_size)\n println(\"Resizing all $channel_type files to target dimension: $target_dim\")\n channels_data = [[crop_or_pad(img, target_dim) for img in channel] for channel in channels_data]\nend" + "objectID": "pages/meeting_notes.html#agenda", + "href": "pages/meeting_notes.html#agenda", + "title": "Meeting Notes", + "section": "Agenda", + "text": "Agenda\n\nNew member introductions\nNew contributor round-up!\nRunning tasks follow-ups:\nState of the JuliaHealth community discussion\n\nTalking about the different aspects of the JuliaHealth community\n\nMapping the JuliaHealth community\n\nAccomplishments throughout the year\n\nJuliaCon 2023\nGSoC\nPublications/etc.\n\nOpen Problems and ongoing work\n\nTechnical problems\nMaking JuliaHealth more accessible for all\n\nFuture goals for the JuliaHealth ecosystem\nOpen discussion\n\nJuliaCon 2024!\nGoogle Summer of Code Discussion\n\nWhat it is\nProposed projects and ideas\nOpen discussion\n\nCalls for collaboration\nOpen discussion" }, { - "objectID": "posts/JZubik-gsoc/GSoC_Jan_Zubik_MedPipe3D.html#basic-post-processing-operations", - "href": "posts/JZubik-gsoc/GSoC_Jan_Zubik_MedPipe3D.html#basic-post-processing-operations", - "title": "GSoC ’24: Adding dataset-wide functions and integrations of augmentations", - "section": "Basic Post-processing operations", - "text": "Basic Post-processing operations\nPost-processing operations involve the algorithm largest_connected_components. It is achieved by label initialization and propagation in the segmented mask. The initialize_labels_kernel function assigns unique labels to different regions.\n\n\ninitialize_labels_kernel:\n\n@kernel function initialize_labels_kernel(mask, labels, width, height, depth)\n idx = @index(Global, Cartesian)\n i = idx[1]\n j = idx[2]\n k = idx[3]\n \n if i >= 1 && i <= width && j >= 1 && j <= height && k >= 1 && k <= depth\n if mask[i, j, k] == 1\n labels[i, j, k] = i + (j - 1) * width + (k - 1) * width * height\n else\n labels[i, j, k] = 0\n end\n end\nend\n\nPropagate_labels_kernel iteratively updates the labels to maintain connected regions. propagate_labels_kernel:\n\n@kernel function propagate_labels_kernel(mask, labels, width, height, depth)\n idx= @index(Global, Cartesian)\n i = idx[1]\n j = idx[2]\n k = idx[3]\n\n if i >= 1 && i <= width && j >= 1 && j <= height && k >= 1 && k <= depth\n if mask[i, j, k] == 1\n current_label = labels[i, j, k]\n for di in -1:1\n for dj in -1:1\n for dk in -1:1\n if di == 0 && dj == 0 && dk == 0\n continue\n end\n ni = i + di\n nj = j + dj\n nk = k + dk\n if ni >= 1 && ni <= width && nj >= 1 && nj <= height && nk >= 1 && nk <= depth\n if mask[ni, nj, nk] == 1 && labels[ni, nj, nk] < current_label\n labels[i, j, k] = labels[ni, nj, nk]\n end\n end\n end\n end\n end\n end\n end\nend\n\nThis process facilitates the identification of the largest connected components in 3D space, helping to isolate relevant medical structures, such as tumors, in the segmented mask. Allowing determining how many such areas are to be returned.\n\n\nlargest_connected_components:\n\nfunction largest_connected_components(mask::Array{Int32, 3}, n_lcc::Int)\n width, height, depth = size(mask)\n mask_gpu = CuArray(mask)\n labels_gpu = CUDA.fill(0, size(mask))\n dev = get_backend(labels_gpu)\n ndrange = (width, height, depth)\n workgroupsize = (3, 3, 3)\n\n # Initialize labels\n initialize_labels_kernel(dev)(mask_gpu, labels_gpu, width, height, depth, ndrange = ndrange)\n CUDA.synchronize()\n\n # Propagate labels iteratively\n for _ in 1:10 \n propagate_labels_kernel(dev, workgroupsize)(mask_gpu, labels_gpu, width, height, depth, ndrange = ndrange)\n CUDA.synchronize()\n end\n\n # Download labels back to CPU\n labels_cpu = Array(labels_gpu)\n \n # Find all unique labels and their sizes\n unique_labels = unique(labels_cpu)\n label_sizes = [(label, count(labels_cpu .== label)) for label in unique_labels if label != 0]\n\n # Sort labels by size and get the top n_lcc\n sort!(label_sizes, by = x -> x[2], rev = true)\n top_labels = label_sizes[1:min(n_lcc, length(label_sizes))]\n\n # Create a mask for each of the top n_lcc components\n components = [labels_cpu .== label[1] for label in top_labels]\n return components\nend" + "objectID": "pages/meeting_notes.html#meeting-outcomes-3", + "href": "pages/meeting_notes.html#meeting-outcomes-3", + "title": "Meeting Notes", + "section": "Meeting Outcomes", + "text": "Meeting Outcomes\n\nShort-Term Outcomes\n\nJacob follows-up with Zach.\n\n\n\nLong-Term Outcomes\n\nIncreasing code ownership." }, { - "objectID": "posts/JZubik-gsoc/GSoC_Jan_Zubik_MedPipe3D.html#structured-configuration-of-all-hyperparameters", - "href": "posts/JZubik-gsoc/GSoC_Jan_Zubik_MedPipe3D.html#structured-configuration-of-all-hyperparameters", - "title": "GSoC ’24: Adding dataset-wide functions and integrations of augmentations", - "section": "Structured configuration of all hyperparameters πŸ†™", - "text": "Structured configuration of all hyperparameters πŸ†™\nHyperparameters for the entire pipeline are stored in a JSON configuration file, enabling straightforward adjustments for experimentation (just swap values, save and resume the study). This structured setup allows easy modification of key parameters, such as data set preparation, training settings, data augmentation, and resampling options.\n\n\nExample configuration:\n\n{\n \"model\": {\n \"patience\": 10,\n \"early_stopping_metric\": \"val_loss\",\n \"optimizer_name\": \"Adam\",\n \"loss_function_name\": \"l1\",\n \"early_stopping\": true,\n \"early_stopping_min_delta\": 0.01,\n \"optimizer_args\": \"lr=0.001\",\n \"num_epochs\": 10\n },\n \"data\": {\n \"batch_complete\": false,\n \"resample_size\": [200,101,49],\n \"resample_to_target\": false,\n \"resample_to_spacing\": false,\n \"batch_size\": 3,\n \"standardization\": false,\n \"target_spacing\": null,\n \"channel_size\": 1,\n \"normalization\": false,\n \"has_mask\": true\n },\n \"augmentation\": {\n \"augmentations\": {\n \"Brightness transform\": {\n \"mode\": \"additive\",\n \"value\": 0.2\n }\n },\n \"p_rand\": 0.5,\n \"processing_unit\": \"GPU\",\n \"order\": [\n \"Brightness transform\"\n ]\n },\n \"learning\": {\n \"Train_Val_Test_JSON\": false,\n \"largest_connected_component\": false,\n \"n_lcc\": 1,\n \"n_folds\": 3,\n \"invertible_augmentations\": false,\n \"n_invertible\": true,\n \n \"class_JSON_path\": false,\n \"additional_JSON_path\": false,\n \"patch_size\": [50,50,50],\n \"metric\": \"dice\",\n \"n_cross_val\": false,\n \"patch_probabilistic_oversampling\": false,\n \"oversampling_probability\": 1.0,\n \"test_train_validation\": [\n 0.6,\n 0.2,\n 0.2\n ],\n \"shuffle\": false\n }\n}\n\nComments: The current configuration is loaded as a dictionary, which simplifies access and modification. This setup presents a strong foundation for integrating automated search algorithms for hyperparameter tuning, enabling more efficient model optimization. The configuration structure could be reorganized and re-named to improve readability, making it easier for users to locate and adjust specific parameters." + "objectID": "pages/meeting_notes.html#notes-2", + "href": "pages/meeting_notes.html#notes-2", + "title": "Meeting Notes", + "section": "Notes", + "text": "Notes\n\nIntroductions\n\nDivital coder\n\nAspiring contributor for the 2024 Julia Organization.\n\n\nContributor Round-Up\n\nShout outs to Farreeda for working on JuliaHealth Observational Health Sub-ecosystem Juliacon proceddings paper.\nShout outs to Jay-Sanjay for tagging new release of OMOPCDMCohortCreator.\n\nState of the JuliaHealth community discussion\n\nTalking about the different aspects of the JuliaHealth community\n\nMapping the JuliaHealth community\n\nAccomplishments throughout the year\n\nJuliaCon 2023\n\nBirds of Feather: Julia for Health and Medicine – Dilum Aluthge, Jacob Zelko\n100 Million Patients: Julia for international Health studies\n\nFirst ever JuliaHealth GSoC fellow - Fareeda Abdelazeez\nODHSI Global Symposium 2023\n\nOpen Problems and ongoing work\n\nTechnical problems\nMaking JuliaHealth more accessible for all\nFuture goals for the JuliaHealth ecosystem\nExpanding the OMOPCDM for hospital price transparency and transparency coverage.\n\nOpen discussion\n\nOpen discussion on standards across JuliaHealth\nZach happy to support and think around this\nSchedule one-off discussion\nMaking juliahealth calls more Europe+asia/pacific friendly. Suggestions to have a one meet each for american time zone separate and one for asia/pacific time zone\n\n\nJuliaCon 2024!\n\nProposal-a-thon\n\nGoogle Summer of Code Discussion\n\nWhat is GSoC/JSoC ?\nProposed projects and ideas\nMedPipe3D\n\nLoading medical imaging data\nModeling perspective most generally developed\nSuper-voxels image mapping\nEdge matching; can make this code within Julia vs.Β Cpp\nDisplay borders of images\nIntegrate segmentation like rotations recalling gamma.\nAdd basic post-processing like largest corrected components.\nAdd patch based data loading with probabilistic oversampling.\n\nOpen discussion\n\nCalls for collaboration\nOpen discussion\n\nJuliaCon 2024 and Proposal-a-thon\nAddressing the β€œParadox of Composition”" }, { - "objectID": "posts/JZubik-gsoc/GSoC_Jan_Zubik_MedPipe3D.html#visualization-of-algorithm-outputs", - "href": "posts/JZubik-gsoc/GSoC_Jan_Zubik_MedPipe3D.html#visualization-of-algorithm-outputs", - "title": "GSoC ’24: Adding dataset-wide functions and integrations of augmentations", - "section": "Visualization of algorithm outputs ⚠️", - "text": "Visualization of algorithm outputs ⚠️\nThis module provides basic visualization functionality by saving output masks and images first to MedImage format and then to Nifti format. The create_nii_from_medimage function from MedImage.jl generates Nifti files, which can be loaded into MedEye3D for 3D visualization.\nComments: Integrating this visualization module more fully with the pipeline could eliminate unnecessary steps. By automatically loading output masks and images as raw data into MedEye3D for 3D visualization and supporting a more efficient end-to-end workflow." + "objectID": "pages/meeting_notes.html#agenda-1", + "href": "pages/meeting_notes.html#agenda-1", + "title": "Meeting Notes", + "section": "Agenda", + "text": "Agenda\n\nNew member introductions\nNew contributor round-up!\nRunning tasks follow-ups:\n\n\nShort-term task follow-ups:\n\nJacob shares info on waste water management + viral load information\n\nLong-term task follow-ups:\n\nCreating a template repository\n\n\n\nPresentation by Jakub Mitura on sub-ecosystem he created for working with CT, PET, and other medical imaging types of data.\nDebrief from OHDSI Symposium (Observational Health research venue)\nGoogle Summer of Code Project Discussion\n\n\nJuliaHealth documentation improvement\nObservational Health Tooling improvements and discussion\nVisualization tools\n\n\nUpcoming and ongoing research opportunities\n\n\nCall for collaboration on using JuliaHealth observational health tools for multi-site study\n\n\nMedical Imaging Extension for Real World Evidence exploration\nOpen discussion" }, { - "objectID": "posts/JZubik-gsoc/GSoC_Jan_Zubik_MedPipe3D.html#k-fold-cross-validation-functionality", - "href": "posts/JZubik-gsoc/GSoC_Jan_Zubik_MedPipe3D.html#k-fold-cross-validation-functionality", - "title": "GSoC ’24: Adding dataset-wide functions and integrations of augmentations", - "section": "K-fold cross-validation functionality βœ…", - "text": "K-fold cross-validation functionality βœ…\nK-fold cross-validation is implemented to evaluate model performance more robustly. The data is split into multiple folds, with each fold serving as a validation set once, while the others form the training set. This functionality provides a better assessment of model performance across different subsets of the data.\n\n\nK-fold cross-validation functionality:\n\n...\n tstate = initialize_train_state(rng, model, optimizer)\n if config[\"learning\"][\"n_cross_val\"]\n n_folds = config[\"learning\"][\"n_folds\"]\n all_tstate = []\n combined_indices = [indices_dict[\"train\"]; indices_dict[\"validation\"]]\n shuffled_indices = shuffle(rng, combined_indices)\n for fold in 1:n_folds\n println(\"Starting fold $fold/$n_folds\")\n train_groups, validation_groups = k_fold_split(combined_indices, n_folds, fold, rng)\n \n tstate = initialize_train_state(rng, model, optimizer)\n final_tstate = epoch_loop(num_epochs, train_groups, validation_groups, h5, model, tstate, config, loss_function, num_classes)\n \n push!(all_tstate, final_tstate)\n end\n else\n final_tstate = epoch_loop(num_epochs, train_groups, validation_groups, h5, model, tstate, config, loss_function, num_classes)\n end\n return final_tstate\n... \n\nThe k_fold_split function organizes the indices for each fold, ensuring comprehensive coverage of the dataset during training.\n\n\nk_fold_split\n\nfunction k_fold_split(data, n_folds, current_fold)\n fold_size = length(data) Γ· n_folds\n validation_start = (current_fold - 1) * fold_size + 1\n validation_end = validation_start + fold_size - 1\n validation_indices = data[validation_start:validation_end]\n train_indices = [data[1:validation_start-1]; data[validation_end+1:end]]\n return train_indices, validation_indices\nend" + "objectID": "pages/meeting_notes.html#meeting-outcomes-4", + "href": "pages/meeting_notes.html#meeting-outcomes-4", + "title": "Meeting Notes", + "section": "Meeting Outcomes", + "text": "Meeting Outcomes\n\nShort-Term Outcomes\n\nJacob intro’s Phil and Jakub\nJacob follows-up with Phil\n\n\n\nLong-Term Outcomes\n\nCreate a template repository for JuliaHealth" }, { - "objectID": "posts/JZubik-gsoc/GSoC_Jan_Zubik_MedPipe3D.html#necessary-enhancements", - "href": "posts/JZubik-gsoc/GSoC_Jan_Zubik_MedPipe3D.html#necessary-enhancements", - "title": "GSoC ’24: Adding dataset-wide functions and integrations of augmentations", - "section": "Necessary Enhancements", - "text": "Necessary Enhancements\nComprehensive Logging: Develop detailed logging mechanisms that capture a wide range of events, including system statuses, model performance metrics, and user activities, to facilitate debugging and system optimization. This is currently executed as a simple println function.\nTensorBoard Integration: Implement an interface for TensorBoard to allow users to visualize training dynamics in real time, providing insights into model behavior and performance trends.\nError and Warning Logs: Introduce advanced error and warning logging capabilities to alert users of potential issues before they affect the pipeline’s performance, ensuring smoother operations and maintenance.\nAutomated Visualization: Integrate MedEye3D directly into MedPipe3D to enable automated visualization of outputs, such as segmentation masks or other relevant medical imaging features. This feature would provide users with real-time visual feedback on model performance and data quality. Code-Level Documentation: Due to needed changes in the fundamental structure of the pipeline in the final phase of the project, it is necessary to reevaluate all documentation.\nOfficial JuliaHealth Documentation: Extend the documentation efforts to include official entries on juliahealth.org, providing a centralized and authoritative resource for users seeking to learn more about MedPipe3D and its capabilities with examples shown" + "objectID": "pages/meeting_notes.html#notes-3", + "href": "pages/meeting_notes.html#notes-3", + "title": "Meeting Notes", + "section": "Notes", + "text": "Notes\n\nNew member introductions\n\nPhil Vernes\n\nWorks at JuliaHub\nDeveloping platform for running Julia jobs\nMany people at JuliaHub using tools within epi\nCan solve many problems in DSL\n\nJay Sanjay\n\nStarted contributing to the JuliaHealth ecosystem\nLooking forward to collaborating\n\n\nRunning tasks follow-ups:\n\nShort-term task follow-ups:\n\nJacob shares info on waste water management + viral load information\n\nLong-term task follow-ups:\n\nCreating a template repository\n\nWe need to have a data structure to hold metadata (DICOM, NIFTI, etc.)\nJuliaNeuro\nHDF5 for long-term storage\n\nWould be great to see everyone using this\nTo work on this to bring this together\nMultiple packages could have same\n\n\n\n\nPresentation by Jakub Mitura on sub-ecosystem he created for working with CT, PET, and other medical imaging types of data.\n\nCreated three packages\nMainly talking about MedEye3D\nSegment data and iterate to see what is going on\nWanted to create tools for everything around model creation\nWanted to make a viewer that is well-suited for the Julia ecosystem\n\nMost medical viewers are quite β€œold”\nNot really dynamic\nHard to show changes within run-time\n\nEasy to get big increase in Julia\n\nUsually something like 10x’s faster\n\nWe do not yet standardize way to load data\nMetadata is saved to HDF5 format\nCan introduce dynamic annotations\nCan have layers and switch on and switch layers\nCan annotate for saying where is the problem in the viewer\nViewer can dynamically update\nQuestions\n\nTested some semi-automatic algorithms\nDo evaluate repeat\nMakes it faster for evaluation and reviewing of medical images\nDepends on OpenGL and NVIDIA drivers\nWorking on Docker container that keeps\nWhat segmentation algorithm? Approach?\n\nBased on Gaussian probability distributions\nSome relaxation applied\nBased mainly on the units and different kinds\nBecoming more interested in transformers\nImplemented in JAX but want to bring it into Julia\nSegmentation for bladder cancer in image analysis\nRestarted work recently in Julia\n\nWould be useful for others?\n\nNew segmentation for other ecosystem within Julia\n\n\n\nUpcoming and ongoing research opportunities\nCall for collaboration on using JuliaHealth observational health tools for multi-site study\nMedical Imaging Extension for Real World Evidence exploration\n\nIdea was to implement package for medical imaging\nPillars\n\nComputing statistics across medical imaging\nComplete datasets for experimenting\nFeature segmentation and scanning\n\nAlign probabilistic model between different scans\nBecome easier for physicians\n\nML model for complex models for image segmentation\n\nThing to consider – need more robustness for image alignment?\n\nSome transformations are relatively easier to repair\nElastic deformations" }, { - "objectID": "posts/JZubik-gsoc/GSoC_Jan_Zubik_MedPipe3D.html#potential-enhancements", - "href": "posts/JZubik-gsoc/GSoC_Jan_Zubik_MedPipe3D.html#potential-enhancements", - "title": "GSoC ’24: Adding dataset-wide functions and integrations of augmentations", - "section": "Potential Enhancements", - "text": "Potential Enhancements\nGPU support for interpolation will allow for significant acceleration of such functions as Scale transform, Simulate, Low-resolution transform, Elastic deformation transform, and Resampling spacing.\nAdd more reversible augmentations to test time.\nCalculating the average of the edges of the picture: checking the type of photo and calculating more correctly on this basis\nElastic deformation transforms with the simulation of different tissue elasticities." + "objectID": "pages/meeting_notes.html#agenda-2", + "href": "pages/meeting_notes.html#agenda-2", + "title": "Meeting Notes", + "section": "Agenda", + "text": "Agenda\n\nNew member introductions\nRunning tasks follow-ups:\n\n\nShort-term task follow-ups:\nLong-term task follow-ups:\n\ni. Creating a template repository\n\nInfectious Disease load for various sewage water data\nUpcoming research opportunities and events\n\n\nNot too early to start thinking about GSoC\nJulia and OHDSI Symposium\n\n\nOpen discussion" }, { - "objectID": "posts/ryan-gsoc/Ryan_GSOC.html", - "href": "posts/ryan-gsoc/Ryan_GSOC.html", - "title": "GSoC ’24: Enhancements to KomaMRI.jl GPU Support", - "section": "", - "text": "Hi! πŸ‘‹\nI am Ryan, an MS student currently studying computer science at the University of Wisconsin-Madison. Looking for a project to work on this summer, my interest in high-performance computing and affinity for the Julia programming language drew me to Google Summer of Code, where I learned about this project opportunity to work on enhancing GPU support for KomaMRI.jl.\nIn this post, I’d like to summarize what I did this summer and everything I learned along the way!\n\nIf you want to learn more about me, you can connect with me here: LinkedIn, GitHub\n\n\n\nWhat is KomaMRI?\nKomaMRI is a Julia package for efficiently simulating Magnetic Resonance Imaging (MRI) acquisitions. MRI simulation is a useful tool for researchers, as it allows testing new pulse sequences to analyze the signal output and image reconstruction quality without needing to actually take an MRI, which may be time or cost-prohibitive.\nIn contrast to many other MRI simulators, KomaMRI.jl is open-source, cross-platform, and comes with an intuitive user interface (To learn more about KomaMRI, you can read the paper introducing it here). However, being developed fairly recently, there are still new features that can be added and optimization to be done.\n\n\nProject Goals\nThe goals outlined by Carlos (my project mentor) and I the beginning of this summer were:\n\nExtend GPU support beyond CUDA to include AMD, Intel, and Apple Silicon GPUs, through the packages AMDGPU.jl, oneAPI.jl, and Metal.jl\nCreate a CI pipeline to be able to test each of the GPU backends\nCreate a new kernel-based simulation method optimized for the GPU, which we expected would outperform array broadcasting\n(Stretch Goal) Look into ways to support running distributed simulations across multiple nodes or GPUs\n\n\n\nStep 1: Support for Different GPU backends\nPreviously, KomaMRI’s support for GPU acceleration worked by converting each array used within the simulation to a CuArray, the device array type defined in CUDA.jl. This was done through a general gpu function. The inner simulation code is GPU-agnostic, as the same operations can be performed on a CuArray or a plain CPU Array. This approach is good for extensibility, as it does not require writing different simulation code for the CPU / GPU, or different GPU backends, and would only work in a language like Julia based on runtime dispatch!\nTo extend this to multiple GPU backends, all that is needed is to generalize the gpu function to convert to either the device types of CUDA.jl, AMDGPU.jl, Metal.jl, or oneAPI.jl, depending on which backend is being used. To give an idea of what the gpu conversion code looked like before, here is a snippet:\nstruct KomaCUDAAdaptor end\nadapt_storage(to::KomaCUDAAdaptor, x) = CUDA.cu(x)\n\nfunction gpu(x)\n check_use_cuda()\n return use_cuda[] ? fmap(x -> adapt(KomaCUDAAdaptor(), x), x; exclude=_isleaf) : x\nend\n\n#CPU adaptor\nstruct KomaCPUAdaptor end\nadapt_storage(to::KomaCPUAdaptor, x::AbstractArray) = adapt(Array, x)\nadapt_storage(to::KomaCPUAdaptor, x::AbstractRange) = x\n\ncpu(x) = fmap(x -> adapt(KomaCPUAdaptor(), x), x)\nThe fmap function is from the package Functors.jl and can recursively apply a function to a struct tagged with @functor. The function being applied is adapt from Adapt.jl, which will call the lower-level adapt_storage function to actually convert to / from the device type. The second parameter to adapt is what is being adapted, and the first is what it is being adapted to, which in this case is a custom adapter struct KomaCUDAAdapter.\nOne possible approach to generalize to different backends would be to define additional adapter structs for each backend and corresponding adapt_storage functions. This is what the popular machine learning library Flux.jl does. However, there is a simpler way!\nEach backend package (CUDA.jl, Metal.jl, etc.) already defines adapt_storage functions for converting different types to / from corresponding device type. Reusing these functions is preferable to defining our own since, not only does it save work, but it allows us to rely on the expertise of the developers who wrote those packages! If there is an issue with types being converted incorrectly that is fixed in one of those packages, then we would not need to update our code to get this fix since we are using the definitions they created.\nOur final gpu and cpu functions are very simple. The backend parameter is a type derived from the abstract Backend type of KernelAbstractions.jl, which is extended by each of the backend packages:\nimport KernelAbstractions as KA\n\nfunction gpu(x, backend::KA.GPU)\n return fmap(x -> adapt(backend, x), x; exclude=_isleaf)\nend\n\ncpu(x) = fmap(x -> adapt(KA.CPU(), x), x, exclude=_isleaf)\nThe other work needed to generalize our GPU support involved switching to use package extensions to avoid having each of the backend packages as an explicit dependency, and defining some basic GPU functions for backend selection and printing information about available GPU devices. The pull request for adding support for multiple backends is linked below:\n\nhttps://github.com/JuliaHealth/KomaMRI.jl/pull/405\n\n\n\nStep 2: Buildkite CI\nAt the time the above pull request was merged, we weren’t sure whether the added support for AMD and Intel GPUs actually worked, since we only had access to CUDA and Apple Silicon GPUs. So the next step was to set up a CI to test each GPU backend. To do this, we used Buildkite, which is a CI platform that many other Julia packages also use. Since there were many examples to follow, setting up our testing pipeline was not too difficult. Each step of the pipeline does the required environment setup and then calls Pkg.test() for KomaMRICore. As an example, here is what the AMDGPU step of our pipeline looks like:\n - label: \"AMDGPU: Run tests on v{{matrix.version}}\"\n matrix:\n setup:\n version:\n - \"1\"\n plugins:\n - JuliaCI/julia#v1:\n version: \"{{matrix.version}}\"\n - JuliaCI/julia-coverage#v1:\n codecov: true\n dirs:\n - KomaMRICore/src\n - KomaMRICore/ext\n command: |\n julia -e 'println(\"--- :julia: Instantiating project\")\n using Pkg\n Pkg.develop([\n PackageSpec(path=pwd(), subdir=\"KomaMRIBase\"),\n PackageSpec(path=pwd(), subdir=\"KomaMRICore\"),\n ])'\n \n julia --project=KomaMRICore/test -e 'println(\"--- :julia: Add AMDGPU to test environment\")\n using Pkg\n Pkg.add(\"AMDGPU\")'\n \n julia -e 'println(\"--- :julia: Running tests\")\n using Pkg\n Pkg.test(\"KomaMRICore\"; coverage=true, test_args=[\"AMDGPU\"])'\n agents:\n queue: \"juliagpu\"\n rocm: \"*\"\n timeout_in_minutes: 60\nWe also decided that in addition to a testing CI, it would also be helpful to have a benchmarking CI to track performance changes resulting from each commit to the main branch of the repository. Lux.jl had a very nice-looking benchmarking page, so I decided to look into their approach. They were using github-action-benchmark, a popular benchmarking action that integrates with the Julia package BenchmarkTools.jl. github-action-benchmark does two very useful things:\n\nCollects benchmarking data into a json file and provides a default index.html to display this data. If put inside a relative path in the gh-pages branch of a repository, this results in a public benchmarking page which is automatically updated after each commit!\nComments on a pull request with the benchmarking results compared with before the pull request. Example: https://github.com/JuliaHealth/KomaMRI.jl/pull/442#pullrequestreview-2213921334\n\nThe only issue was that since github-action-benchmark is a github action, it is meant to be run within github by one of the available github runners. While this works for CPU benchmarking, only Buildkite has the CI setup for each of the GPU backends we are using, and Lux.jl’s benchmarks page only included CPU benchmarks, not GPU benchmarks (Note: we talked with Avik, the repository owner of Lux.jl, and Lux.jl has since adopted the approach outlined below to display GPU and CPU benchmarks together). I was not able to find any examples of other julia packages using github-action-benchmark for GPU benchmarking.\nFortunately, there is a tool someone developed to download results from Buildkite into a github action (https://github.com/EnricoMi/download-buildkite-artifact-action). This repository only had 1 star when I found it, but it does exactly what we needed: it identifies the corresponding Buildkite build for a commit, waits for it to finish, and then downloads the artifacts for the build into the github action it is being run from. With this, we were able to download the Buildkite benchmark results from a final aggregation step into our benchmarking action and upload to github-action-benchmark to publish to either the main data.js file for our benchmarking website, or pull request.\nOur final benchmarking page looks like this and is publicly accessible:\n\nOne neat thing about github-action-benchmark is that the default index.html is extensible, so even though by deault it only shows time, the information for memory usage and number of allocations is also collected into the json file, and can be displayed as well.\nA successful CI run on Buildkite Looks like this:\n\nThe pull requests for creating the CI testing and benchmarking pipeline, and changing the index.html for our benchmark page are listed below:\n\nhttps://github.com/JuliaHealth/KomaMRI.jl/pull/411\nhttps://github.com/JuliaHealth/KomaMRI.jl/pull/418\nhttps://github.com/JuliaHealth/KomaMRI.jl/pull/421\n\n\n\nStep 3: Optimization\nWith support for multiple backends enabled, and a robust CI, the next step was to optimize our simulation code as much as possible. Our original idea was to create a new GPU-optimized simulation method, but before doing this we wanted to look more at the existing code and optimize for the CPU.\nThe simulation code is solving a differential equation (the [Bloch equations(https://en.wikipedia.org/wiki/Bloch_equations)]) over time. Most differential equation solvers step through time, updating the current state at each time step, but our previous simulation code, more optimized for the GPU, did a lot of computations across all time points in a simulation block, allocating a matrix of size Nspins by NΞ”t each time this was done. Although this is beneficial for the GPU, where there are millions of threads available on which to parallelize these computations, for the CPU it is more important to conserve memory, and the aforementioned approach of stepping through time is preferable.\nAfter seeing that this approach did help speed up simulation time on the CPU, but was not faster on the GPU (7x slower for Metal!) we decided to separate our simulation code for the GPU and CPU, dispatching based on the KernelAbstractions.Backend type depending on if it is <:KernelAbstractions.CPU or <:KernelAbstractions.GPU.\nOther things we were able to do to speed up CPU computation time:\n\nPreallocating each array used inside the core simulation code so it can be re-used from one simulation block to the next.\nSkipping an expensive computation if the magnetization at that time point is not added to the final signal\nEnsuring that each statement is fully broadcasted. We were surprised to see the difference between the following examples:\n\n#Fast\nBz = x .* seq.Gx' .+ y .* seq.Gy' .+ z .* seq.Gz' .+ p.Ξ”w ./ T(2Ο€ .* Ξ³)\n\n#Slow\nBz = x .* seq.Gx' .+ y .* seq.Gy' .+ z .* seq.Gz' .+ p.Ξ”w / T(2Ο€ * Ξ³)\n\nUsing the cis function for complex exponentiation, which is faster than exp\n\nWith these changes, the mean improvement in simulation time aggregating across each of our benchmarks for 1, 2, 4, and 8 CPU threads was ~4.28. For 1 thread, the average improvement in memory usage was 90x!\nThe next task was optimizing the simulation code for the GPU. Although our original idea was to put everything into one GPU kernel, we found that the existing broadcasting operations were already very fast, and that custom kernels we wrote were not able to outperform the previous implementation. The Julia GPU compiler team deserves a lot of credit for developing such fast broadcasting implementations!\nHowever, this does not mean that we were unable to improve the GPU simulation time. Similar to with the CPU, preallocation made a substantial difference. Parallelizing as much work as possible across the time points for a simulation block was also found to beneficial. For the parts that needed to be done sequentially, a custom GPU kernel was written which used the KernelAbstractions.@localmem macro for arrays being updated at each time step to yield faster memory access.\nThe mean speedup we saw across the 4 supported GPU backends was 4.16, although this varied accross each backend (for example, CUDA was only 2.66x faster while oneAPI was 28x faster). There is a remaining bottleneck in the run_spin_preceession! function having to do with logical indexing that I was not able to resolve, but could be solved in the future to speed up the GPU simulation time even further!\nThe pull requests optimizing code for the CPU and GPU are below:\n\nhttps://github.com/JuliaHealth/KomaMRI.jl/pull/443\nhttps://github.com/JuliaHealth/KomaMRI.jl/pull/459\nhttps://github.com/JuliaHealth/KomaMRI.jl/pull/462\n\n\n\n4. Step 4: Distributed Support\nThis last step was a stretch goal for exploring how to add distributed support to KomaMRI. MRI simulations can become quite large, so it is useful to be able to distribute work across either multiple GPUs or multiple compute nodes.\nA nice thing about MRI simulation is the independent spin property: if a phantom object (representing, for example a brain tissue slice) is divided into two parts, and each part is simulated separately, the signal result from simulating the whole phantom will be equal to the sum of the signal results from simulating each subdivision of the original phantom. This makes it quite easy to distribute work, either across more than one GPU or accross multiple compute nodes.\nThe following scripts worked, with the only necessary code change to the repository being a new + function to add two RawAcquisitionData structs:\n#Use multiple GPUs:\nusing Distributed\nusing CUDA\n\n#Add workers based on the number of available devices\naddprocs(length(devices()))\n\n#Define inputs on each worker process\n@everywhere begin\n using KomaMRI, CUDA\n sys = Scanner()\n seq = PulseDesigner.EPI_example()\n obj = brain_phantom2D()\n #Divide phantom\n parts = kfoldperm(length(obj), nworkers())\nend\n\n#Distribute simulation across workers\nraw = Distributed.@distributed (+) for i=1:nworkers()\n KomaMRICore.set_device!(i-1) #Sets device for this worker, note that CUDA devices are indexed from 0\n simulate(obj[parts[i]], seq, sys)\nend\n#Use multiple compute nodes\nusing Distributed\nusing ClusterManagers\n\n#Add workers based on the specified number of SLURM tasks\naddprocs(SlurmManager(parse(Int, ENV[\"SLURM_NTASKS\"])))\n\n#Define inputs on each worker process\n@everywhere begin\n using KomaMRI\n sys = Scanner()\n seq = PulseDesigner.EPI_example()\n obj = brain_phantom2D()\n parts = kfoldperm(length(obj), nworkers())\nend\n\n#Distribute simulation across workers\nraw = Distributed.@distributed (+) for i=1:nworkers()\n simulate(obj[parts[i]], seq, sys)\nend\nPull reqeust for adding these examples to the KomaMRI documentation: https://github.com/JuliaHealth/KomaMRI.jl/pull/468\n\n\nConclusions / Future Work\nThis project was a 350-hour large project, since there were many goals to accomplish. To summarize what changed since the beginning of the project:\n\nAdded support for AMDGPU.jl, Metal.jl, and oneAPI.jl GPU backends\nCI for automated testing and benchmarking accross each backend + public benchmarks page\nSignificantly faster CPU and GPU performance\nDemonstrated distributed support and examples added in documentation\n\nFuture work could look at ways to further optimize the simulation code, since despite the progress made, I believe there is more work to be done! The aforementioned logical indexing issue is still not resolved, and the kernel used inside the run_spin_excitation! function has not been profiled in depth. KomaMRI is also looking into adding support for higher-order ODE methods, which could require more GPU kernels being written.\n\n\nAcknowledgements\nI would like to thank my mentor, Carlos Castillo, for his help and support on this project. I would also like to thank Jakub Mitura, who attended some of our meetings to help with GPU optimization, Dilum Aluthge who helped set up our BuildKite pipeline, and Tim Besard, who answered many GPU-related questions that Carlos and I had.\n\n\n\n\n\n\n Back to topCitationBibTeX citation:@online{kierulf2024,\n author = {Kierulf, Ryan},\n title = {GSoC ’24: {Enhancements} to {KomaMRI.jl} {GPU} {Support}},\n date = {2024-08-30},\n url = {https://juliahealth.org/JuliaHealthBlog/posts/ryan-gsoc/Ryan_GSOC.html},\n langid = {en}\n}\nFor attribution, please cite this work as:\nKierulf, Ryan. 2024. β€œGSoC ’24: Enhancements to KomaMRI.jl GPU\nSupport.” August 30, 2024. https://juliahealth.org/JuliaHealthBlog/posts/ryan-gsoc/Ryan_GSOC.html." + "objectID": "pages/meeting_notes.html#meeting-outcomes-5", + "href": "pages/meeting_notes.html#meeting-outcomes-5", + "title": "Meeting Notes", + "section": "Meeting Outcomes", + "text": "Meeting Outcomes\n\nShort-Term Outcomes\n\nJacob shares info on waste water management + viral load information\n\n\n\nLong-Term Outcomes" }, { - "objectID": "posts/dummy/index.html", - "href": "posts/dummy/index.html", - "title": "Dummy Post", - "section": "", - "text": "Seciton 1\nSmall dummy blog post\n\n\nCode\n2 + 2\n\n\n4\n\n\n\n\nCode\nprintln(2 + 2)\n\n\n4\n\n\n\n\nSection 2\n\n\nSection 3\n\n\n\n\n\n\n Back to topCitationBibTeX citation:@online{2024,\n author = {, Foobar},\n title = {Dummy {Post}},\n date = {2024-06-22},\n url = {https://juliahealth.org/JuliaHealthBlog/posts/dummy/},\n langid = {en}\n}\nFor attribution, please cite this work as:\nFoobar. 2024. β€œDummy Post.” June 22, 2024. https://juliahealth.org/JuliaHealthBlog/posts/dummy/." - }, - { - "objectID": "posts/michela-gsoc/Michela_JSoC.html", - "href": "posts/michela-gsoc/Michela_JSoC.html", - "title": "GSoC ’24: IPUMS.jl Small Project", - "section": "", - "text": "Hello! πŸ‘‹\nHi! I am Michela, I have a Master’s degree in Physics of Complex Systems and I am currently working as a software engineer in Rome, where I am from. During my studies, I became interested in the use of modeling and AI methods to improve healthcare and how these tools can be used to better understand how cultural and social backgrounds influence the health of individuals. I am also interested in the computational modeling of the brain and the human body and its implications for a better understanding of certain pathological conditions.\nWith these motivations in mind, I heard about Google Summer of Code. Since I had studied Julia in some courses and given that the language is expanding rapidly, I decided to find a project within Julia. As a result, I found the project of Jacob Zelko (@TheCedarPrince) to start this experience.\n\nIf you want to learn more about me, you can connect with me here: LinkedIn, GitHub\n\n\n\nProject Description\nIPUMS is the β€œworld’s largest available single database of census microdata”, providing survey and census data from around the world. It includes several projects that provide a wide variety of datasets. The information and data collected by IPUMS are useful for comparative research, as well as for the analysis of individuals in their life contexts. These data can be used to create a more comprehensive dataset that will facilitate research on the social determinants of health for different types of diseases, social communities, and geographical areas.\n\n\nTo learn more about IPUMS, visit the website\n\n\n\nTasks and Goals\nThe primary objectives of this proposal are to:\n\nDevelop a native Julia package to interact with the APIs available around the datasets IPUMS provides.\nProvide useful utilities within this package for manipulating IPUMS datasets.\nCompose this package with the wider Julia ecosystem to enable novel research in health, economics, and more.\n\nTo achieve this, the work was distributed as follows:\n\nExpand some of the functionality developed in ipumsr IPUMS NHGIS\n\nCreate a link between OpenAPI documentation and the functions internally used in IPUMS.jl: updating already present functions, determining if updating is needed, and testing them\nDevelop functionality similar to the get_metadata_nghis function present in ipumsr\n\nUpdate IPUMS documentation\n\nSet up and deploy DocumenterVitepress.jl\n\nWrite a blog post on how IPUMS.jl can be composed within the ecosystem.\n\n\n\n\nHow the work was done\nThe first task was to migrate documents from Documenter to DocumenterVitepress.This issue aims to support the significant refactoring underway across JuliaHealth, aimed at improving the discoverability and cohesion of the JuliaHealth ecosystem, particularly about documentation. This issue is intended to create a more attractive entry point for new Julia users interested in health research within the Julia community. To accomplish this task, a dependency of DocumenterVitepress was added to the docs directory of the IPUMS.jl repository. Once this was done, the Documenter.jl make.jl file was migrated into a DocumenterVitepress.jl make.jl file. Working on the make.jl file, the pages structure were added to the web page explaining the IPUMS.jl package. With this in mind, those were added: 1. Home: to explain the main purpose of the package 2. Workflows: to explain the working process 3. How to: to give general information 4. Tutorials: to show how to use IPUMS.jl\n5. Examples: some examples of activities 6. Mission: to explain why the package is useful for the community 7. References: references used to write the pages.\nThis first task takes some time, especially setting up GitHub and cloning the repository locally. At this point, my experience with GitHub was really limited and I had to learn how to use the Git environment from scratch, for example how to do continuous integration (to commit code to a shared repository), documentation release and merge, and local testing. I found the support of my mentors and searching for material online was really helpful.\nThe second task was to update the documentation of IPUMS.jl by modifying the functionality within the model folder in the IPUMS.jl folder. The main aim of this task was to a description of the function and its attributes, an example of possible implementation and result, and finally to show how to use it. The documentation to be updated as of several types of functions: 1. Data extract 2. Data set 3. Data Table 4. Time series table 5. Error 6. Shapefile. Each of these macro-categories (from 1 to 4) contains a set of functions, each signaling the different expected output and specific purpose. Information about what each function does, and the meaning of each specific input variable, has been found on the IPUMS website and references have been made in the written documentation.\n\n\nHow to work with IPUMS\nAfter writing down the description of the function and the inputs, examples were formulated, starting from the IPUMS website: when you register at IPUMS, an API key is given. which is used, among other things, to run pre-written code on the website. This code contains examples of these functions, and these examples have been adapted by changing some input values and adapting them to work in the Julia framework. The latter task was done by simply rewriting some structures, such as dictionaries, maps, or lists, in the Julia language. Here is a small guide on how to set up working with the API: 1. Create an IPUMS account 2. Log in to your account 3. Copy the API key, which can be obtained from the website 4. Use the key to run the code that is already available on the IPUMS Developer Portal, where you will also find information about the variables and packages.\n\n\nFunctions testing\nA final task was to test the functions in the β€˜api_IPUMSAPI.jl’ file. In this file, the function to be tested and other functions are defined and the most important ones are extracted to be available in the available throughout the framework. Some of the functions to be tested were the following:\n\nmetadata_nhgis_data_tables_get\nmetadata_nhgis_datasets_dataset_data_tables_data_table_get\nmetadata_nhgis_datasets_dataset_get\nmetadata_nhgis_datasets_get\n\nBefore working on the Julia files, testing and understanding the original R function was done using R studio.\n\nEach function was then tested using the API key from the IPUMS registration as well as other input examples taken from the documentation or the IPUMS website. or from the IPUMS website. All functions were displayed successfully, giving the expected result, so it can be concluded that the translation from R to Julia is successful.\n\n\nCode\nusing IPUMS\nusing OpenAPI\n\napi_key = \"insert your key here\"\n\nversion = \"2\"\npage_number = 1\npage_size = 2500\n#media_type = \n\napi = IPUMSAPI(\"https://api.ipums.org\", Dict(\"Authorization\" => api_key));\n\nres1 = metadata_nhgis_data_tables_get(api, version)\n\nres2 = metadata_nhgis_datasets_dataset_get(api, \"2022_ACS1\", \"2\");\n\nres3 = metadata_nhgis_datasets_dataset_data_tables_data_table_get(api, \"2022_ACS1\",\"B01001\", \"2\");\n\nres4 = metadata_nhgis_datasets_get(api, \"2\");\n\n\nAn example of the output is:\n. . .\n\n{\n \"name\": \"NT1\",\n \"nhgisCode\": \"AAA\",\n \"description\": \"Total Population\",\n \"universe\": \"Persons\",\n \"sequence\": 1,\n \"datasetName\": \"1790_cPop\",\n \"nVariables\": [\n 1\n ]\n}\n\n. . .\n\n\nAccomplished Goals and Future Development\nThe project was a 90-hour small project and during this time the documentation was completed and the testing of the metadata function was done, as well as the migration from Documenter.jl to DocumenterVitepress.jl. During these months some things took longer than I expected because of some problems that occurred, so some things were missing in relation to the original plan. However, this time was useful for learning new things: - I saw how to work with a package under development, how to work with large datasets, and how to write documentation - I had the opportunity to better understand how to work with Git and GitHub - I learned some new things about R, which was a completely unknown language to me. - I deepened my knowledge of Julia, a language I had worked with during my time at university. - I had the chance to work on a large open-source project, to be part of a large community, and to learn how to communicate with it efficiently.\nA special thanks goes to my mentors, Jacob Zelko and Krishna Bhogaonker, for helping me through this process.\nFuture developments of this work could include deepening the work that my mentors and I have started, with the possibility of integrating this package with other machine learning packages in Julia and, from there, doing new analyses of the data in terms of social and geographical implications for health.\n\n\n\n\n\n\n Back to topCitationBibTeX citation:@online{rocchetti2024,\n author = {Rocchetti, Michela},\n title = {GSoC ’24: {IPUMS.jl} {Small} {Project}},\n date = {2024-08-26},\n url = {https://juliahealth.org/JuliaHealthBlog/posts/michela-gsoc/Michela_JSoC.html},\n langid = {en}\n}\nFor attribution, please cite this work as:\nRocchetti, Michela. 2024. β€œGSoC ’24: IPUMS.jl Small\nProject.” August 26, 2024. https://juliahealth.org/JuliaHealthBlog/posts/michela-gsoc/Michela_JSoC.html." + "objectID": "pages/meeting_notes.html#notes-4", + "href": "pages/meeting_notes.html#notes-4", + "title": "Meeting Notes", + "section": "Notes", + "text": "Notes\n\nNew member introductions\n\nTiem van der Deure\n\nUniversity of Copenhagen PhD\nVector-borne Disease Modeling\nEpidemiological modeling and climate effects on health\nRafael Schoueten\n\nScott Jones\n\nHeavily involved in healthcare IT\n\ndx/dt\n\nGoogle Summer of Code\n\nDidn’t know it existed\nGoogle Season of Docs is great too\n\nBest for long-term maintenance\nIn the Julia docs ecosystem is kinda a mess\n\n\nOHDSI + Julia\n\nHow difficult it has been to work with EHR from EPIC\n\nStill a bit manual but getting better\n\nTuring modeling β€œmaking them work”\n\nGetting them to run\n\nMaking it run fast enough\nMuch easier to use but not as fast as otherwise\n\nExtremely mathy very fast\n\n\nSewage water information for disease population estimations\n\nWeekly excerpt\nInfectious disease doctor\n\nWould be really neat to make some kind of app\nTo check wastewater\n\nPropensity of viruses in ER\n\n\nPhysician testing for rough understanding of what is happening in community\n\nYou don’t just need to look for one disease, but rather multiple co-factors\n\nMany healthcare systems put together monitoring systems\n\nNHS (in UK) dismantled their monitoring systems" }, { - "objectID": "posts/jay-gsoc/gsoc-2024-fellows.html", - "href": "posts/jay-gsoc/gsoc-2024-fellows.html", - "title": "GSoC ’24: Developing Tooling for Observational Health Research in Julia", - "section": "", - "text": "I am Jay Sanjay, and I am pursuing a Bachelor’s degree in Computational Sciences and Engineering at the Indian Institute of Technology (IIT) in Hyderabad, India. Coming from a mathematics and data analysis background, I was initially introduced to Julia at my university lectures. Later, I delved more into the language and the JuliaHealth community - an intersection of Julia, Health Research, Data Sciences, and Informatics. Here, I met some of the great folks in JuliaHealth and I decided to take it on as a full-fledged summer project. In this blog, I will briefly describe what my project is and what I did as a part of it.\n\nYou can find my GSoC project archive link\nYou can also find the related publication of my work on Zenodo\nIf you want to know more about me, you can connect with me on LinkedIn and follow me on GitHub" + "objectID": "pages/meeting_notes.html#agenda-3", + "href": "pages/meeting_notes.html#agenda-3", + "title": "Meeting Notes", + "section": "Agenda", + "text": "Agenda\n\nNew member introductions\nRunning tasks follow-ups:\n\nShort-term task follow-ups:\nLong-term task follow-ups:\n\nCreating a template repository\n\n\nUpcoming research opportunities and events\n\nNot too early to start thinking about GSoC\nJulia and OHDSI Symposium\n\nInfectious Disease load for various sewage water data\nOpen discussion" }, { - "objectID": "posts/jay-gsoc/gsoc-2024-fellows.html#what-is-observational-health-research", - "href": "posts/jay-gsoc/gsoc-2024-fellows.html#what-is-observational-health-research", - "title": "GSoC ’24: Developing Tooling for Observational Health Research in Julia", - "section": "What Is Observational Health Research?", - "text": "What Is Observational Health Research?\nObservational Health Research refers to studies that analyze real-world data (such as patient medical claims, electronic health records, etc.) to understand patient health. These studies often encompass a vast amount of data concerning patient care. An outstanding challenge here is that these datasets can become very complex and grow large enough to require advanced computing methods to process this information." + "objectID": "pages/meeting_notes.html#meeting-outcomes-6", + "href": "pages/meeting_notes.html#meeting-outcomes-6", + "title": "Meeting Notes", + "section": "Meeting Outcomes", + "text": "Meeting Outcomes\n\nShort-Term Outcomes\n\nJacob shares info on waste water management + viral load information" }, { - "objectID": "posts/jay-gsoc/gsoc-2024-fellows.html#what-are-patient-pathways", - "href": "posts/jay-gsoc/gsoc-2024-fellows.html#what-are-patient-pathways", - "title": "GSoC ’24: Developing Tooling for Observational Health Research in Julia", - "section": "What Are Patient Pathways?", - "text": "What Are Patient Pathways?\nPatient pathways refer to the journey that patients with specific medical conditions undergo in terms of their treatment. This concept goes beyond simple drug uptake statistics and looks at the sequence of treatments patients receive over time, including first-line treatments and subsequent therapies. Understanding patient pathways is essential for analyzing treatment patterns, adherence to clinical guidelines, and the disbursement of drugs. To analyze patient pathways, one would typically use real-world data from sources such as electronic health records, claims data, and registries. However, barriers such as data interoperability and resource requirements have hindered the full utilization of real-world data for this purpose.\nSo to address these challenges we (the JuliaHealth organization and I) want to develop a set of tools to extract and analyze these patient pathways. These sets of tools are based on the Observational Medical Outcomes Partnership (OMOP) Common Data Model, which standardizes healthcare data to promote interoperability." + "objectID": "pages/meeting_notes.html#notes-5", + "href": "pages/meeting_notes.html#notes-5", + "title": "Meeting Notes", + "section": "Notes", + "text": "Notes\n\nIntroductions\n\nTiem van der Deure\n\nUniversity of Copenhagen PhD\nVector-borne Disease Modeling\nEpidemiological modeling and climate effects on health\nRafael Schoueten\n\nScott Jones\n\nHeavily involved in healthcare IT\n\ndx/dt\n\nGoogle Summer of Code\n\nRecently discovered by the team\nGoogle Season of Docs\n\nBest for long-term maintenance\nSignificant challenge organizing in Julia docs ecosystem\n\n\nOHDSI + Julia\n\nWorking with EHR from EPIC is demanding\n\nLabour intensive albeit improving\n\nTuring modeling β€œmaking them work”\n\nGetting them to run\n\nMaking it run fast enough\nTrade off ease-of-use for computation speed\n\nRequires significant mathematical ability for speed gains\n\n\nSewage water information for disease population estimations\n\nWeekly excerpt\nInfectious disease doctor\n\nWould be really neat to make some kind of app to check wastewater\n\nPropensity of viruses in ER\n\n\nPhysician testing for rough understanding of what is happening in community\n\nAbility to look for multiple co-factors instead of just one disease\n\nMany healthcare systems put together monitoring systems\n\nNHS (in UK) dismantled their monitoring systems\n\n\nDatabases and JuliaHealth\n\nShow how to do the basics\nCommon database errors\n\nHow to address them\n\nConsider having more people working in this space?\nNot really a problem within ecosystem\nLook at drivers across all packages to see how things work in Julia ecosystem\n\nSee how we can address issues across ecosystem" }, { - "objectID": "posts/jay-gsoc/gsoc-2024-fellows.html#setting-up-the-package-in-juliahealth-channel", - "href": "posts/jay-gsoc/gsoc-2024-fellows.html#setting-up-the-package-in-juliahealth-channel", - "title": "GSoC ’24: Developing Tooling for Observational Health Research in Julia", - "section": "1. Setting Up the Package in JuliaHealth Channel", - "text": "1. Setting Up the Package in JuliaHealth Channel\nInitially, there was no package as such for generating pathways, so I had to build it from scratch. First, I created the repository with the name OMOPCDMPathways.jl. Once the repository was created, we needed to have a skeleton for a standard Julia repository. For this, we used the PkgTemplates.jl this provided a basic skeleton for the repository that included - folders for test suites, documentation, src code files, GitHub files, README and LICENSE file, TOML and citation files. All this we can further edit and modify as per our work. By default, PkgTemplate.jl uses Documenter.jl for the documentation part but as suggested and discussed with my mentor we decided to shift to DocumenterVitepress.jl for the documentation part. However, we still faced some deployment issues in the new documentation due to a few mistakes in the make.jl file, thanks to Anshul Singhvi for helping fix the Deployment issues with DocumenterVitepress. With this, we were ready with the documentation set up and fully functional. After we had shifted to DocumenterVitepress the main task now was to host the documentation, this was done using Github-Actions, detailed steps for hosting are provided at this page. Then we added the CodeCov to our package by triggering it via a dummy function and a corresponding test case for it. Also, the CI for the package was set up with it. And, now finally the repository was ready with test coverage, CI, and documentation fully functional repository ready. Here’s some snapshots of the documentation set-up:\n\n\nInitial documentation with Documenter.jl\n\n\n\nNew documentation using DocumenterVitepress.jl\n\nSo, as a part of it, I created this documentation which provides detailed steps for converting docs from Documenter to DocumenterVitepress." + "objectID": "pages/meeting_notes.html#agenda-4", + "href": "pages/meeting_notes.html#agenda-4", + "title": "Meeting Notes", + "section": "Agenda", + "text": "Agenda\n\nNew member introductions\nMisc Announcements\n\nCalciumScoring.jl – Dale Black\nSurvival Analyses – Arin Basu\nGoogle Summer of Code Fellowship wrapping up\nWe are on the Julia Community Calendar!\nSmall updates to the JuliaHealth website\n\nRunning tasks follow-ups:\n\nShort-term task follow-ups:\n\n@Jacob Set-up HackMD to take notes going forward\n\nCopy and paste meeting minutes over to JuliaHealth PR to update at end of meetings\n\n\n@Dilum finds out how to live stream JuliaHealth BoF\n\nLong-term task follow-ups:\n\nCreating a template repositoryΒ \n\nDebrief from JuliaCon\n\nInteroperability of Julia with health research ecosystems (R )\nDevelop and document tutorials showcasing compositional solutions to JuliaHealth ecosystem problems\nCoordinate with bigger Julia Blog to bridge between communities even better\nDatabases and JuliaHealth\n\nJon Starr and NumFOCUS’s OSSci Program\nOpen discussion on next steps for the JuliaHealth community" }, { - "objectID": "posts/jay-gsoc/gsoc-2024-fellows.html#loading-the-postgresql-database", - "href": "posts/jay-gsoc/gsoc-2024-fellows.html#loading-the-postgresql-database", - "title": "GSoC ’24: Developing Tooling for Observational Health Research in Julia", - "section": "2. Loading the PostgreSQL Database", - "text": "2. Loading the PostgreSQL Database\nThe main database we worked on/built analysis was the freely available OMOPCDM Database. The Database was formatted within a PostgreSQL database with installation instructions here are some instructions on how to set up Postgres in a Linux machine. However, I was provided with some more extra synthetic data from my mentor for further testing of the functionalities. Being a very large database we had to strategically download it further, my mentor helped me in setting up the Postgres on my local machine. Once, the database was set up proper testing was performed on it to check if things were as expected. With this, we were done with the database setup as well and could finally dive into the actual code logic for the Pathways synthesis." + "objectID": "pages/meeting_notes.html#meeting-outcomes-7", + "href": "pages/meeting_notes.html#meeting-outcomes-7", + "title": "Meeting Notes", + "section": "Meeting Outcomes", + "text": "Meeting Outcomes\n\nShort-Term Outcomes\n\n@Jacob follow-up with Jonathan about JuliaHealth + OSSci\n@Edmund let Jacob know about blog posts solving problems\n\n\n\nLong-Term Outcomes\n\nSupport OSSci about JuliaHealth" }, { - "objectID": "posts/jay-gsoc/gsoc-2024-fellows.html#testing-and-development-setup-on-my-local-computer", - "href": "posts/jay-gsoc/gsoc-2024-fellows.html#testing-and-development-setup-on-my-local-computer", - "title": "GSoC ’24: Developing Tooling for Observational Health Research in Julia", - "section": "3. Testing and Development setup on my local computer", - "text": "3. Testing and Development setup on my local computer\nTo get a proper environment for functionality creation and concurrent testing we required a proper testing setup so that we could test the new functions made at the same time. This was done using Revise.jl, which helps to keep Julia sessions running without frequent restarts when making changes to code. It allowed me to edit my code, update packages, or switch git branches during a session, with changes applied immediately in the next command. My mentor helped me set it up, added Revise.jl to the global Julia environment, also PackageCompatUI that provides a terminal text interface to the [compat] section of a Julia Project.toml file, and finally made a Julia script by the name β€œstartup.jl” out of it. This script was then added to /home/jay-sanjay/.julia/config/ path in my local computer.\nHere is the sample for the startup.jl file:\nusing PackageCompatUI\nusing PkgTemplates\nusing Revise\n\n###################################\n# HELPER FUNCTIONS\n###################################\nfunction template()\n Template(;\n user=\"jay-sanjay\",\n dir=\"~/FOSS\",\n authors=\"jaysanjay <jaysanjay@gmail.com> and contributors\",\n julia=v\"1.6\",\n plugins=[\n ProjectFile(; version=v\"0.0.1\"),\n Git(),\n Readme(),\n License(; name=\"MIT\"),\n GitHubActions(; extra_versions=[\"1.6\", \"1\", \"nightly\"]),\n TagBot(),\n Codecov(),\n Documenter{GitHubActions}(),\n Citation(; readme = true),\n RegisterAction(),\n BlueStyleBadge(),\n Formatter(;style = \"blue\")\n ],\n )\nend" + "objectID": "pages/meeting_notes.html#notes-6", + "href": "pages/meeting_notes.html#notes-6", + "title": "Meeting Notes", + "section": "Notes", + "text": "Notes\n\nIntroductions\n\nClark C. Evans\n\nMaster cobbler of YAML\nUsed to work at Prometheus Research\n\nSold to IQVIA\n\nWorked under MechanicalRabbit Umbrella\n\nDeveloped FunSQL.jl with Kirill\nDatabase characterization\n\nJoined Tufts University CTSA\n\nHelping with data warehousing\n\nObjects to query OHDSI databases and EPIC Clarity\n\nGetting Pluto working\n\n\nJonathan\n\nManager for OSSci for NumFOCUS\nGoal: Mapping open source science ecosystem\nWork with Distributed Computing\n\nBerkeley technology\nBlocks and chains!\n\nUsing Open Source and Science to drive research\n\nEdmund\n\nPhD Candidate at Texas Dallas\n\nMolecular and Cell Biology\nFunctional Genomics\n\nComing from JuliaCon\nExcited about Health stuff\n\n\nInteroperability of Julia with health research ecosystems (R)\n\nEasiest way to interoperate is to call them directly from the command line\nBuild your own executables\nMost reliable/easiest\nDatabase approach:\n\nBuild table in one language\nIngest in another\n\nCombining executables in one location – use Docker?\n\nCan run on several different machines\n\nBuilding R packages with Julia backends is possible\n\nDevelop and document tutorials showcasing compositional solutions to JuliaHealth ecosystem problems\n\nCompeting Julia with other tutorials?\nSwitching over to Julia from what?\nWhy are people still not switching?\n\nDemonstrating the use is one way\n\nObviously, one could write more posts\nBut there seems to be a lot of content already – what is missing?\nDoes seem like there is two different levels of documentation\n\nBeginner\nAdvanced\n\nWhere are the practical means of solving problems in Julia?\n\nDatabases and JuliaHealth\n\nShow how to do the basics\nCommon database errors\n\nHow to address them\n\nUnclear on how to solve it; more people working in this space?\nNot really a problem within ecosystem\nLook at drivers across all packages to see how things work in Julia ecosystem\n\nSee how we can address issues across ecosystem\n\n\nJonathan Starr and NumFOCUS’s OSSci Program\n\nGetting to deep diving within Julia ecosystem\nResearchers who want to find a package that they can use and develop\nMapping projects and people to a given tool\n\nCan look at map to see where packages are needed for a particular ecosystem\nCan click on and connect with researchers\nHighlighting of credit for researchers\n\nStarting with NumFOCUS projects\nBuilding out knowledge of all ongoing projects/software\n\nJulia is little represented right now\n\nHow to show to funders/orgs what projects to support\nHow to build support across or collaboration between groups\nTrying to stop abandonware from happening\nAttempting to build social infrastructure\nQ&A\n\nTufts doing something very similar – happy to collaborate\nHow can JuliaHealth get started and involved?\n\nJonathan: Send me reference page and we can get this started!\n\n\nLinks:\n\nAbout: https://numfocus.org/open-source-science-initiative-ossci\nHow To Join: https://opensource.science\nMap of Open Source Science (MOSS)" }, { - "objectID": "posts/jay-gsoc/gsoc-2024-fellows.html#selecting-treatments-of-interest", - "href": "posts/jay-gsoc/gsoc-2024-fellows.html#selecting-treatments-of-interest", - "title": "GSoC ’24: Developing Tooling for Observational Health Research in Julia", - "section": "4. Selecting Treatments of Interest", - "text": "4. Selecting Treatments of Interest\nSo, as a part of this, we used the previously mentioned research paper and discussion with the mentors we came up with logic for it. The first thing to do was to determine the moment in time from which selected treatments of interest should be included in the treatment pathway. The default is all treatments starting after the index date of the target cohort. For example, for a target cohort consisting of newly diagnosed patients, treatments after the moment of first diagnosis are included. However, it would also be desirable to include (some) treatments before the index date, for instance in case a specific disease diagnosis is only confirmed after initiating treatment. Therefore, periodPriorToIndex specifies the period (i.e.Β number of days) before the index date from which treatments should be included. We have created two dispatches for this function. After that proper testing and documentation are also added.\nA basic implementation for it is:\n\nConstruct a SQL query to select cohort_definition_id, subject_id, and cohort_start_date from a specified table, filtering by cohort_id.\nThe SQL query construction and execution was done using the FunSQL.jl library, in the below-shown manner:\n\nsql = From(tab) |>\n Where(Fun.in(Get.cohort_definition_id, cohort_id...)) |>\n Select(Get.cohort_definition_id, Get.subject_id, Get.cohort_start_date) |>\n q -> render(q, dialect=dialect)\n\nExecutes the constructed SQL query using a database connection, fetching the results into a data frame.\nIf the DataFrame is not empty, convert cohort_start_date to DateTime and subtract date_prior from each date, then return the modified DataFrame.\n\nThis was then be called this:\nperiod_prior_to_index(\n cohort_id = [1, 1, 1, 1, 1], \n conn; \n date_prior = Day(100), \n tab=cohort\n )" + "objectID": "pages/meeting_notes.html#agenda-5", + "href": "pages/meeting_notes.html#agenda-5", + "title": "Meeting Notes", + "section": "Agenda", + "text": "Agenda\n\nIntroductions and what people in the community are using Julia for in health research\nWhat is missing of painful in Julia that is needed to drive health research forward\nThoughts on how to address some of these problems\nOpen discussion and next steps for JuliaHealth\n\n\nShort-Term Outcomes\nNot Available\n\n\nLong-Term Outcomes\n\nACTION: Develop and document tutorials showcasing compositional solutions to JuliaHealth ecosystem problems.\nACTION: Establish cohesive and organized Julia Blog to guide users and highlight official blogs." }, { - "objectID": "posts/jay-gsoc/gsoc-2024-fellows.html#filters-applied", - "href": "posts/jay-gsoc/gsoc-2024-fellows.html#filters-applied", - "title": "GSoC ’24: Developing Tooling for Observational Health Research in Julia", - "section": "5. Filters Applied", - "text": "5. Filters Applied\nAfter this, we where needed to get the patient’s database filtered more finely so that there are minimal variations that can be ignored. The duration of the above extracted event eras may vary a lot and it can be preferable to limit to only treatments exceeding a minimum duration. Hence, minEraDuration specifies the minimum time an event era should last to be included in the analysis. All these implementations were more of Dataframe manipulation where I used DataFrames.jl package.\nAfter that proper testing and documentation are also added.\nA basic implementation for the minEraDuration is: It filters the treatment history DataFrame to retain only those rows where the duration between drug_exposure_end and drug_exposure_start is at least minEraDuration. This function can be used as follows:\n#| eval: false \n\ncalculate_era_duration(test_df, 920000)\n\n#= ... =#\n\n4Γ—3 DataFrame\n Row β”‚ person_id drug_exposure_start drug_exposure_end \n β”‚ Int64 Float64 Int64 \n─────┼───────────────────────────────────────────────────\n 1 β”‚ 1 -3.7273e8 -364953600\n 2 β”‚ 1 2.90304e7 31449600\n 3 β”‚ 1 -8.18208e7 -80006400\n 4 β”‚ 1 1.32918e9 1330387200\nAnother filter we worked on is the EraCollapse. So, let’s suppose a case where an individual receives the same treatment for a long period of time (e.g.Β need for chronic treatment). Then it’s highly likely that the person would require refills. Now as patients are not 100% adherent, there might be a gap between two subsequent event eras. Usually, these eras are still considered as one treatment episode, and the eraCollapseSize deals with the maximum gap within which two eras of the same event cohort would be collapsed into one era (i.e.Β seen as a continuous treatment instead of a stop and re-initiation of the same treatment). After that proper testing and documentation are also added.\nA basic implementation for the eraCollapseSize is: (a) Sorts the data frame by event_start_date and event_end_date. (b) Calculates the gap between each era and the previous era. (c) Filters out rows with gap_same > eraCollapseSize.\nThese functions can be used as follows:\n#| eval: false \n\n#= ... =#\n\nEraCollapse(treatment_history = test_df, eraCollapseSize = 400000000)\n4Γ—4 DataFrame\n Row β”‚ person_id drug_exposure_start drug_exposure_end gap_same \n β”‚ Int64 Float64 Int64 Float64 \n─────┼───────────────────────────────────────────────────────────────\n 1 β”‚ 1 -5.33347e8 -532483200 -1.86373e9\n 2 β”‚ 1 -3.7273e8 -364953600 1.59754e8\n 3 β”‚ 1 -8.18208e7 -80006400 2.83133e8\n 4 β”‚ 1 2.90304e7 31449600 1.09037e8" + "objectID": "pages/meeting_notes.html#meeting-notes-1", + "href": "pages/meeting_notes.html#meeting-notes-1", + "title": "Meeting Notes", + "section": "Meeting Notes", + "text": "Meeting Notes\n\nAttendee interests and background\n\nHere to learn\nFrom EHR development and background\nGenie folks here to support JuliaHealth endeavors\nGenomics research and prevention\nQuebec Heart and Lung Institute\nRepresenting PumasAI\nConsulting group\n\nDeveloping health research in Michigan area\nAggregating claims data\nTo learn what is going on in the community\n\nCreator of MetaAnalysis.jl\nInvolved with backend of healthcare IT\nWorking on JuliaHub\n\nLearning about packages that are out there\nHere to support JuliaHealth members\nNew Zealand longitudinal child health\n\nHave own secure system\nPost-COVID syndrome\n\nComputational biology\n\nSickle Cell\nApplying some ML\n\n\n\nProblems within the Julia ecosystem\n\nJulia needs more database connectivity to more easily do operations research\nDatabases are a pain point and composing with other aspects of the ecosystem\nInteroperability within Julia and other sorts of resources\nI end up doing the bare minimum in SQL\n\nDo we have RAM?\nCan we pull this into the Julia ecosystem?\nCrank up the RAM! But only so much scaling\nMinimal SQL writing\n\nSearchlight.jl: Julia ORM layer within\n\nIs Genie like a shiny?\n\nNo, more of a full-stack\nGoes beyond just visualization dashboards\n\n\nSequencing data\n\nEqually data\nEveryone uploads data in slightly different ways\nMake simple ways to pull that data\nR Conductor –> JuliaConductor?\n\nWould make genomic pipelines within Julia pipelines a lot easier\n\nWe need to understand the underlying structures\nOne of the big pain points\n\nOften to have roll your own\n\n\nEpiR –> EpiJ?\n\nPower calculators\n\nCo-founder of start-up\n\nFound unmet need for remote monitoring for neuotropenia\nNon-invasive screen for neutropenia\nDevice runs Julia\nPain points:\n\nTestability of hardware\nLOTS of CI – bit of a pain\nHow much repetition happens in CI\n\nPart of the problem for these problems:\n\nThere are still going to be folks who use the same organizations\nOvercoming inertia to do the same or similar things in Julia\nWrapping around Julia?\n\nBringing it into the R ecosystem\nLeading to big impacts for callable things from R by having smaller static binaries\nWrapping Julia packages in R\n\nN3C – National COVID Cohort Collaborative\n\nWent to many healthcare systems across the US to get COVID data\nShelled out to Palantir\nOpen source tools within the ecosystem\nJuliaHub has Boeing board member\n\nTrusted within security community\nCould help in this situation\n\n\n\n\n\nThoughts on how to address some of these problems\n\nUsing other packages outside of Julia\n\nIf you have some way to wrap around it\nGetting support\nPythonCall.jl or RCall.jl\n\nNot clear how to make this compositional\n\n\nThe paradox of compositionality\n\nBlog posts go a huge ways to solving problems\nTutorials showing how things can be combined together\nPromotional type material\nNice docs are nice\n\nThe Julia Blog itself\n\nMentions JuliaBloggers but doesn’t help with guiding users to read\nBlogs need to go on as official blogs\nJulia Forem – is it maintained?\n\nHook into the tags from blogs\nCross-posting where appropriate\n\n\nHow to learn Julia within the context of health\n\nCarpentries for learning resources" }, { - "objectID": "posts/jay-gsoc/gsoc-2024-fellows.html#treatment-history-of-the-patients", - "href": "posts/jay-gsoc/gsoc-2024-fellows.html#treatment-history-of-the-patients", - "title": "GSoC ’24: Developing Tooling for Observational Health Research in Julia", - "section": "6. Treatment History of the Patients", - "text": "6. Treatment History of the Patients\nThe create_treatment_history function constructs a detailed treatment history for patients in a target cohort by processing and filtering event cohort data from a given DataFrame. It begins by isolating the target cohort based on its cohort_id, adding a new column for the index_year derived from the cohort’s start date. Then, it selects relevant event cohorts based on a provided list of cohort IDs and merges them with the target cohort on the subject_id to associate events with individuals in the target group. The function applies different filtering criteria depending on whether the user is interested in treatments starting or ending within a specified period before the target cohort’s start date (defined by periodPriorToIndex). It keeps only the event cohorts that match the filtering condition, ensuring that only relevant treatments are considered. After filtering, the function calculates time gaps between consecutive cohort events for each patient, adding these gaps to the DataFrame. The final DataFrame provides a history of treatments, including the dates of events and the time intervals between them, offering a clear timeline of treatment for each patient. After that proper testing and documentation are also added." + "objectID": "pages/meeting_notes.html#meeting-summary", + "href": "pages/meeting_notes.html#meeting-summary", + "title": "Meeting Notes", + "section": "Meeting Summary", + "text": "Meeting Summary\nIn Attendance: Jacob Zelko, Fareeda Abdelazeez, Zachary Christensen\nLocation: Virtual\nSummary: Discussed new members, upcoming JuliaCon, JuliaHealth Birds of a Feather discussion on topics like neural decoding and OMOP tooling, managing logistics for Julia organizations, and JuliaHealth PR reviews.\nKeywords: #brain #imaging #neural #decoding #collaboration #community #engagement" }, { - "objectID": "posts/jay-gsoc/gsoc-2024-fellows.html#combinationwindow-functionality-to-combine-overlapping-treatments", - "href": "posts/jay-gsoc/gsoc-2024-fellows.html#combinationwindow-functionality-to-combine-overlapping-treatments", - "title": "GSoC ’24: Developing Tooling for Observational Health Research in Julia", - "section": "7. CombinationWindow Functionality To Combine Overlapping Treatments", - "text": "7. CombinationWindow Functionality To Combine Overlapping Treatments\nNow once we have the filtering of the treatments done, we need to combine the overlapping treatments based on some set of rules. The combinationWindow specifies the time that two event eras need to overlap to be considered a combination treatment. If there are more than two overlapping event eras, we sequentially combine treatments, starting from the first two overlapping event eras.\nThe combination_Window function processes a patient’s treatment history by identifying overlapping treatment events and combining them into continuous treatment periods based on certain rules. It first converts event_cohort_id into strings and sorts the treatment data by person_id, event_start_date, and event_end_date. The helper function selectRowsCombinationWindow calculates gaps between consecutive treatments, marking rows where treatments overlap or occur too closely. In the main loop, the function checks these overlaps and gaps against a specified combinationWindow. If treatments overlap (or nearly overlap), the function adjusts the treatment periods by either merging adjacent rows or splitting rows to create continuous treatment periods. The process continues until all overlapping treatments are combined into one, creating an updated and accurate treatment history. The function ensures the final output reflects realistic treatment windows by handling special cases where gaps between treatments are smaller than the treatment durations themselves.\nIt mainly covers the three cases mentioned in the R-research paper:\n\nSwitch Case:\nCondition: If the gap between the two treatment events is smaller than the combinationWindow, but the gap is not equal to the duration of either event. Action: The event_end_date of the previous treatment is set to the event_start_date of the current treatment. This effectively β€œshifts” the previous treatment’s end date to eliminate the gap, merging the treatments into one continuous period. Purpose: This ensures that treatment gaps that are too small (less than combinationWindow) are treated as part of the same treatment window.\n#| eval: false \n\n#= ... =#\n\nif -gap_previous < combinationWindow && !(-gap_previous in [duration_era, prev_duration_era])\n treatment_history[i-1, :event_end_date] = treatment_history[i, :event_start_date]\nHere is the pictorial representation for the same: \n\n\nFRFS (First Row, First Shortened):\nCondition: If the gap is larger than or equal to the combinationWindow, or the gap equals the duration of one of the two treatments, and the first treatment ends before or on the same date as the second treatment. Action: A new row is created where the second treatment’s event_end_date is set to the end date of the first treatment. This preserves the overlap but ensures that the earlier treatment period stays intact. Purpose: This prevents unnecessary truncation of the first treatment if it spans the entire overlap window.\n#| eval: false \n\n#= ... =#\n\nelseif -gap_previous >= combinationWindow || -gap_previous in [duration_era, prev_duration_era]\n if treatment_history[i-1, :event_end_date] <= treatment_history[i, :event_end_date]\n new_row = deepcopy(treatment_history[i, :])\n new_row.event_end_date = treatment_history[i-1, :event_end_date]\n append!(treatment_history, DataFrame(new_row'))\nHere is the pictorial representation for the same: \n\n\nLRFS (Last Row, First Shortened):\nCondition: If the gap is larger than or equal to the combinationWindow, or the gap equals the duration of one of the treatments, and the first treatment ends after the second treatment. Action: The current treatment’s event_end_date is adjusted to match the event_end_date of the previous treatment. Purpose: This handles cases where the second treatment’s window should be shortened to prevent overlap with the previous treatment, merging them into a single continuous window.\n#| eval: false \n\n#= ... =#\n\nelse\n treatment_history[i, :event_end_date] = treatment_history[i-1, :event_end_date]\nHere is the pictorial representation for the same: \n\nNote: However, There are a few things left to cover here, most of which are the documentation and writing the test suite for the same." + "objectID": "pages/meeting_notes.html#agenda-6", + "href": "pages/meeting_notes.html#agenda-6", + "title": "Meeting Notes", + "section": "Agenda", + "text": "Agenda\n\nNew member welcomes!\nPlanning JuliaHealth Birds of a Feather\n\nTopics?\nFacilitators?\nCreating actionable outcomes?\n\nOpen discussion on Julia Orgs, How Do You Manage Logistics?\nMisc topics\nJulia for Health Informatics Research & Bridging community organizations\n\n1. Open Discussion on [The Graphs Ecosystem](https://discourse.julialang.org/t/the-graphs-ecosystem/99463?u=thecedarprince)" }, { - "objectID": "posts/jay-gsoc/gsoc-2024-fellows.html#organizing-meetings-and-communication", - "href": "posts/jay-gsoc/gsoc-2024-fellows.html#organizing-meetings-and-communication", - "title": "GSoC ’24: Developing Tooling for Observational Health Research in Julia", - "section": "1. Organizing Meetings and Communication", - "text": "1. Organizing Meetings and Communication\nThroughout the project, I regularly met with my mentor, [Jacob Zelko], and co-mentor, [Mounika], via weekly Zoom calls to discuss progress and seek guidance. During these meetings, we reviewed my work, identified areas where I needed help, and set clear goals for the upcoming weeks. We used Trello to organize and track these goals, ensuring that nothing was overlooked. My mentors provided detailed insights into specific technical aspects and guided me through the logic behind various functions. Outside of our scheduled meetings, they were always available for quick queries via Slack, ensuring constant support." + "objectID": "pages/meeting_notes.html#meeting-outcomes-8", + "href": "pages/meeting_notes.html#meeting-outcomes-8", + "title": "Meeting Notes", + "section": "Meeting Outcomes", + "text": "Meeting Outcomes\n\nShort-Term Outcomes\n\n@Jacob Set-up HackMD to take notes going forward\n\nCopy and paste meeting minutes over to JuliaHealth PR to update at end of meetings\n\n\n\n\nLong-Term Outcomes\n\nACTION: Creating a template repository" }, { - "objectID": "posts/jay-gsoc/gsoc-2024-fellows.html#personal-documentation", - "href": "posts/jay-gsoc/gsoc-2024-fellows.html#personal-documentation", - "title": "GSoC ’24: Developing Tooling for Observational Health Research in Julia", - "section": "2. Personal Documentation", - "text": "2. Personal Documentation\nIn addition to the notes from our meetings, I maintained personal documentation where I recorded every step I took, including the challenges I faced and the mistakes I made. This helped me reflect on my progress and stay organized throughout the fellowship. Following my selection for GSoC 2024, I also published a blog post on Medium to share my journey and experiences with the Julia Language community." + "objectID": "pages/meeting_notes.html#meeting-notes-2", + "href": "pages/meeting_notes.html#meeting-notes-2", + "title": "Meeting Notes", + "section": "Meeting Notes", + "text": "Meeting Notes\n\nNew members:\n\nZachary Christensen\n\nNeuroimaging research\nMD/PhD\n\nTrying to finish this year!!!\n\nLots of background work like in JuliaData\nWorks on making Julia interface\n\n\nAnnouncement: JuliaCon about 1 month away!\n\nWe have our own track: biology and medicine\nMany people working on different things\n\nJuliaHealth Birds of a Feather Discussion\n\nPossible Topics:\n\nNeural decodingΒ \n\nInspired by MATLAB: http://www.readout.infoΒ \nSister organization: https://julianeuro.github.io/packages\n\nOMOP Tooling for Real World Data\nHow to start collaborations?\n\nMaybe grant collaborations?\nGetting access to datasets\n\nComing up with different research questions\n\n\nHow can we integrate across the community?\n\nWhat problem can we solve?\n\nBecome a community resource to point to packages\nDon’t need to keep recreating or developing new packages\n\nPackages could be applications built on top of a specific use case\nCombining old packages in new ways\n\n\n\n\n\nOpen discussion on Julia Orgs, How Do You Manage Logistics?\n\nHave multiple persons part of the organizations\nSharing meeting documentation\n\nShare Google Doc at the beginning or before a meeting in announcement\nPublish notes on website publicly\n\nPR to update the JuliaHealth website with new tab for meeting minutes\n\nACTION: Using HackMD to take notes going forward\nCopy and paste meeting minutes over to JuliaHealth PR to update at end of meetings\n\n\n\nConsistent APIs for JuliaHealth\n\nInitial first pass with HealthBase.jl: https://github.com/JuliaHealth/HealthBase.jlΒ \nAs free as possible from niche\nCould become quickly overwhelming or run risk of bikeshedding\nArrayInterface is a learning example in this context\nLight dependency package is great with a well-described APIΒ \nHow to move forward and get momentum\n\nWithout it turning into a mess\n\nCommon ontologies: http://obofoundry.orgΒ \n\nJuliaHealth PR Reviews\n\nPR Checklist:\n\nPurpose\nReduce cognitive load\n\nJuliaHealth package forks: https://github.com/JuliaCI/PkgTemplates.jlΒ \nACTION: Creating a template repository" }, { - "objectID": "posts/jay-gsoc/gsoc-2024-fellows.html#contributions-to-the-rest-of-the-juliahealth-repositories", - "href": "posts/jay-gsoc/gsoc-2024-fellows.html#contributions-to-the-rest-of-the-juliahealth-repositories", - "title": "GSoC ’24: Developing Tooling for Observational Health Research in Julia", - "section": "3. Contributions To the Rest of the JuliaHealth Repositories", - "text": "3. Contributions To the Rest of the JuliaHealth Repositories\nEarlier I have contributed a lot to the OMOPCDMCohortCreator.jl including adding new functionalities writing test suites, adding blogs including - Patient Pathways within JuliaHealth. Apart from that I also initiated 3 new releases of this package." + "objectID": "pages/meeting_notes.html#meeting-summary-1", + "href": "pages/meeting_notes.html#meeting-summary-1", + "title": "Meeting Notes", + "section": "Meeting Summary", + "text": "Meeting Summary\nIn Attendance: Jacob Zelko, Dilum Aluthge, Asher Wasserman, Fareeda Abdelazeez, Kyle Beggs\nLocation: Virtual\nSummary: First JuliaHealth community call to meet other Julians, learn how we can galvanize the Juliahealth Community, and open discussion on paths forward\nKeywords: #data #analysis #hemodynamics #omop #machine #learning" }, { - "objectID": "posts/divyansh-gsoc/gsoc-2024-fellows.html", - "href": "posts/divyansh-gsoc/gsoc-2024-fellows.html", - "title": "GSoC ’24: Adding functionalities to medical imaging visualizations", - "section": "", - "text": "I am Divyansh, an undergraduate student from Guru Gobind Singh Indraprastha university, majoring in Artificial Intelligence and Machine Learning. Stumbling upon projects under the Juliahealth sub-ecosystem of medical imaging packages, the intricacies of imaging modalities and file formats, reflected in their relevant project counterparts, captured my interest. Working with standards such as NIfTI (Neuroimaging Informatics Technology Initiative) and DICOM (Digital Imaging and Communications in Medicine) with MedImages.jl, I became interested in the visualization routines of such imaging datasets and their integration within the segmentation pipelines for modern medical-imaging analysis.\nIn this post, I’d like to summarize what I did this summer and everything I learned along the way, contributing to MedEye3d.jl medical imaging visualizer under GSOC-2024!\n\nIf you want to learn more about me, you can connect with me on LinkedIn and follow me on GitHub" + "objectID": "pages/meeting_notes.html#agenda-7", + "href": "pages/meeting_notes.html#agenda-7", + "title": "Meeting Notes", + "section": "Agenda", + "text": "Agenda\n\nIntroductions\nWhat people are using Julia for in health research\nSelected topics and state within the Julia ecosystem:\n\nObservational Health\nMedical Imaging\nMachine Learning and Health\nInteroperability Standards\nDrug Discovery\n\nStandard Interfaces" }, { - "objectID": "posts/divyansh-gsoc/gsoc-2024-fellows.html#what-is-medeye3d.jl", - "href": "posts/divyansh-gsoc/gsoc-2024-fellows.html#what-is-medeye3d.jl", - "title": "GSoC ’24: Adding functionalities to medical imaging visualizations", - "section": "What is MedEye3d.jl?", - "text": "What is MedEye3d.jl?\nMedEye3D.jl is a package under the Julia language ecosystem designed to facilitate the visualization and annotation of medical images. Tailored specifically for medical applications, it offers a range of functionalities to enhance the interpretation and analysis of medical images. MedEye3D aims to provide an essential tool for 3D medical imaging workflow within Julia. The underlying combination of Rocket.jl and ModernGL.jl ensures the high-performance robust visualizations that the package has to offer.\nMedEye3d.jl is open-source and comes with an intuitive user interface (To learn more about MedEye3d, you can read the paper introducing it here [1])." + "objectID": "pages/meeting_notes.html#meeting-outcomes-9", + "href": "pages/meeting_notes.html#meeting-outcomes-9", + "title": "Meeting Notes", + "section": "Meeting Outcomes", + "text": "Meeting Outcomes\n\nShort-Term Outcomes\n\n@Dilum finds out how to live stream JuliaHealth BoF\n\n\n\nLong-Term Outcomes" }, { - "objectID": "posts/divyansh-gsoc/gsoc-2024-fellows.html#what-features-does-this-project-encompass", - "href": "posts/divyansh-gsoc/gsoc-2024-fellows.html#what-features-does-this-project-encompass", - "title": "GSoC ’24: Adding functionalities to medical imaging visualizations", - "section": "What features does this project encompass?", - "text": "What features does this project encompass?\nThis project covers implementation of several tasks that will enable the establishment of additional important functionalities within the MedEye3D package, facilitating enhancements within the visualization’s windowing for MRI and PET data, support for super voxels (sv), improved load times, high-level functionality implementation and robust viewing for multiple images." + "objectID": "pages/meeting_notes.html#meeting-notes-3", + "href": "pages/meeting_notes.html#meeting-notes-3", + "title": "Meeting Notes", + "section": "Meeting Notes", + "text": "Meeting Notes\n\nIntroductions\n\nDilum Aluthge – MD/PhD Student Brown University (BCBI), PumasAI\n\nJulia Community Involvement\n\nPkg\nGeneral Registry\nContinuous Integration\n\nJuliaHealth and beyond\n\nOriginally created JuliaHealth to bring people together in health\nBioJulia folks are a great source of inspiration for packages!\n\nBirds of a Feather!!! COME VISIT! – Friday July 28th, 4PM EST in Boston, MA!\n\nAsher Wasserman – Astronomy PhD, Data Scientist in BioTech\n\nJulia Community Involvement\n\nDifferential Equations\nOne off deployments\n\n\nFareeda Abdelazeez – GSoC JuliaHealth (First GSoC Student!!!!!)\n\nJulia Community Involvement\n\nObservational Health tooling JuliaHealth!\n\n\nKyle Beggs – Software Engineer in small Optics company, Finishing PhD in MechE\n\nJulia Community Involvement\n\nPDEs\nHemodynamics research focus\nTake advantage of these tools for imaging, segmentation\n\n\n\nWhat people are using Julia for in health research\n\nAsher: Cancer patient data\n\nPDFs and other data formatsΒ \n\nCDA documents\n\nHow to structure this ad hoc type of data into common data model\nDeveloping processes to automatically make these documents useful\nHow do we clean the data to match actual reality\nHow do we make this data actionable/useful\nCould match towards goals of OHDSI/observational health\n\nAnalyses at population level?\nOutcome propensity scores?\nPatient phenotype development?\n\nRole of Julia:\n\nMainly as a scripting language\nSupplement to a lot of SQL scripting (FunSQL discovered)\nPython is generally being deployed because of software devs\n\nHow to not crash AWS, etc.\n\nJulia deployment for risk (?)\nSurvival Analysis in Julia; lifelines in Python otherwise\n\n\nKyle: Vascular Surgical Planning\n\nUnobvious on where to place graft, etc – educated guesses\nCreating a tool to simulate operations\nWhy Julia?\n\nExisting tools are open source but really GUI-driven\nIntegration across ecosystem would be even better for hemodynamics in Julia\nGive a base to simulate the mechanics involved with this\n\nJuliaFEM, etc.Β \n\n\nMesh list methods\n\nPoint clouds\nMain application is within hemodynamics\n\n\nFareeda: JuliaHealth GSoC Student\n\nWorking on OMOP Common Data Model\nStandard model for observational health patient data\nDevelop infrastructure of JuliaHealth to work with OMOP CDM data\n\nImprove DBConnector\nOMOPCDMCohortCreator.jl – add tooling\nOHDSIAPI.jl – creating interfaces for ATHENA/ATLAS\n\nPatient Level Prediction tooling\n\nUsing MLJ algorithms\nAttempting to solve a research question\n\nEvaluate success of package\n\n\nStretch goals:\n\nCohort Quality and underlying data is β€œgood”\nBuild support for OBDC connections\n\n\nOverlap with other organizations\n\nDoesn’t happen in a vacuum\nServing as a bridge between a bridge and a community between other groups\nWhat should be JuliaHealth?\n\nBringing together peopleΒ \n\n\n\nSelected topics and state within the Julia ecosystem:\n\nObservational Health\nMedical Imaging\nMachine Learning and Health\nInteroperability Standards\nDrug Discovery\n\nStandard Interfaces\n\nJune 30th, 2023\nAttending:\nAgenda:\n\nNew member welcomes!\nPlanning JuliaHealth Birds of a Feather\n\nTopics?\nFacilitators?\nCreating actionable outcomes?\n\nOpen discussion on Julia Orgs, How Do You Manage Logistics?\nMisc topics\n\nJulia for Health Informatics Research & Bridging community organizations\n\nOpen Discussion on The Graphs Ecosystem\n\n\n\nNotes:Β \n\nNew members:\n\nZachary Christensen\n\nNeuroimaging research\nMD/PhD\n\nTrying to finish this year!!!\n\nLots of background work like in JuliaData\nWorks on making Julia interface\n\n\nAnnouncement: JuliaCon about 1 month away!\n\nWe have our own track: biology and medicine\nMany people working on different things\n\nJuliaHealth Birds of a Feather Discussion\n\nPossible Topics:\n\nNeural decodingΒ \n\nInspired by MATLAB: http://www.readout.infoΒ \nSister organization: https://julianeuro.github.io/packages\n\nOMOP Tooling for Real World Data\nHow to start collaborations?\n\nMaybe grant collaborations?\nGetting access to datasets\n\nComing up with different research questions\n\n\nHow can we integrate across the community?\n\nWhat problem can we solve?\n\nBecome a community resource to point to packages\nDon’t need to keep recreating or developing new packages\n\nPackages could be applications built on top of a specific use case\nCombining old packages in new ways\n\n\n\n\n\nOpen discussion on Julia Orgs, How Do You Manage Logistics?\n\nHave multiple persons part of the organizations\nSharing meeting documentation\n\nShare Google Doc at the beginning or before a meeting in announcement\nPublish notes on website publicly\n\nPR to update the JuliaHealth website with new tab for meeting minutes\n\nACTION: Using HackMD to take notes going forward\nCopy and paste meeting minutes over to JuliaHealth PR to update at end of meetings\n\n\n\nConsistent APIs for JuliaHealth\n\nInitial first pass with HealthBase.jl: https://github.com/JuliaHealth/HealthBase.jlΒ \nAs free as possible from niche\nCould become quickly overwhelming or run risk of bikeshedding\nArrayInterface is a learning example in this context\nLight dependency package is great with a well-described APIΒ \nHow to move forward and get momentum\n\nWithout it turning into a mess\n\nCommon ontologies: http://obofoundry.orgΒ \n\nJuliaHealth PR Reviews\n\nPR Checklist:\n\nPurpose\nReduce cognitive load\n\nJuliaHealth package forks: https://github.com/JuliaCI/PkgTemplates.jlΒ \nACTION: Creating a template repository" }, { - "objectID": "posts/divyansh-gsoc/gsoc-2024-fellows.html#migration-of-package-from-rocket-to-julias-base.channel", - "href": "posts/divyansh-gsoc/gsoc-2024-fellows.html#migration-of-package-from-rocket-to-julias-base.channel", - "title": "GSoC ’24: Adding functionalities to medical imaging visualizations", - "section": "1. Migration of package from Rocket to Julia’s Base.Channel", - "text": "1. Migration of package from Rocket to Julia’s Base.Channel\nInitially, there was significant screen-tearing evident from the pixelated display of the rendered text and main image which, furthermore exhibited flickering upon scrolling through the slices in the relevant displayed image’s planar views i.e (Transversal, Coronal and Saggital). Troubleshooting along the way, we narrowed down the issue within the Rocket’s actor-subscription mechanism and decided to integrate Julia’s Base.Channel within MedEye3d.jl for handling the event and state management routine. Julia has asynchronous, threadsafe channels which facilitate in asynchronous programming with the help of a producer-consumer mechanism. An example usage of Base.Channel is as follows:\nfunction consumer(channel::Base.Channel)\n while(true)\n channelData::String = take!(channel)\n println(\"Channel got \" * channelData)\n end\nend\n\nnewChannel = Base.Channel(100)\n\n@async consumer(newChannel)\nput!(newChannel, \"apples\")\nJulia’s multiple dispatch made for the architectural setup of MedEye3d, facilitated fixing the issue of screen tearing. Below is how the on_next! function, invokes different reactive components based on the types of arguments it is dealing with.\n\nDump data in channel -> fetch data from the channel in an event loop -> invoke on_next!(state, channelData) -> invoke relevant functionality based on the type of arguments passed\n\n\nThe end result was a visualizer with a seamless display of a CT image without any pixelating artifacts." - }, - { - "objectID": "posts/divyansh-gsoc/gsoc-2024-fellows.html#implementation-of-high-level-functions-with-simplified-basic-usage", - "href": "posts/divyansh-gsoc/gsoc-2024-fellows.html#implementation-of-high-level-functions-with-simplified-basic-usage", - "title": "GSoC ’24: Adding functionalities to medical imaging visualizations", - "section": "2. Implementation of high level functions with simplified basic usage", - "text": "2. Implementation of high level functions with simplified basic usage\nImplementing a bare-bones image visualization required a lot of function calls and definitions, in order to execute the following phases:\n\nRendering an image-plane with OpenGL\nLoading data slices from the image\nCreating texture specifications for modalities\nProducing the final segmentation display\n\nIn order to simplify basic usage, high-level abstractions were put in place with the help of MedImages.jl (under ongoing development) library to load images in the form of MedImage objects to formulate a single display function for the user. Further simplifications were made to accommodate options for the user to manipulate the imaging data that is displayed currently in the visualizer i.e retrieval of voxel arrays and their modification. Taking this in mind, the following relevant functions were exposed:\nMedEye3d.SegmentationDisplay.displayImage()\nMedEye3d.DisplayDataManag.getDisplayedData()\nMedEye3d.DisplayDataManag.setDisplayedData()\nPutting all of the above functions to use together, we can launch the visualizer, retrieve the displayed voxel data and modify it to our liking. A sample script to achieve the former, is highlighted below:\nusing MedEye3d\nctNiftiImage = \"/home/hurtbadly/Downloads/ct_soft_study.nii.gz\"\nmedEyeStruct = MedEye3d.SegmentationDisplay.displayImage(ctNiftiImage)\ndisplayData = MedEye3d.DisplayDataManag.getDisplayedData(medEyeStruct, [Int32(1), Int32(2)]) #passing the active texture number\n\n# We need to check if the return type of the displayData is a single Array{Float32,3} or a vector{Array{Float32,3}}\n# Now in this case we are setting Gaussian noise over the manualModif Texture voxel layer, and the manualModif texture defaults to 2 for active number\n\ndisplayData[2][:, :, :] = randn(Float32, size(displayData[2]))\nMedEye3d.DisplayDataManag.setDisplayedData(medEyeStruct, displayData)\nThe result of this Gaussian noise within the annotation layer, made for an outcome like the following:" - }, - { - "objectID": "posts/divyansh-gsoc/gsoc-2024-fellows.html#improved-precompilation-with-decreased-outputs-to-reduce-start-time", - "href": "posts/divyansh-gsoc/gsoc-2024-fellows.html#improved-precompilation-with-decreased-outputs-to-reduce-start-time", - "title": "GSoC ’24: Adding functionalities to medical imaging visualizations", - "section": "3. Improved precompilation with decreased outputs to reduce start time", - "text": "3. Improved precompilation with decreased outputs to reduce start time\nPreviously, the package’s precompilation was failing in Julia v1.9 and v1.10 due to pattern matching errors arising after the usage of match macros from the Match.jl pkg in MedEye3d’s keymapping workflow between GLFW callbacks from mouse and keyboard. The relevant equivalent native conditional (if-else) statements, resolved the issue and facilitated in successful precompilation of the package. Further, only following minimal outputs were produced during precompilation:\n\nChanges highlighted within the following pull-request:\nhttps://github.com/JuliaHealth/MedEye3d.jl/pull/12" - }, - { - "objectID": "posts/divyansh-gsoc/gsoc-2024-fellows.html#automatic-windowing-for-most-common-mri-and-pet-modalities", - "href": "posts/divyansh-gsoc/gsoc-2024-fellows.html#automatic-windowing-for-most-common-mri-and-pet-modalities", - "title": "GSoC ’24: Adding functionalities to medical imaging visualizations", - "section": "4. Automatic windowing for most common MRI and PET modalities", - "text": "4. Automatic windowing for most common MRI and PET modalities\nWindowing is a crucial aspect of medical imaging, particularly in MRI (Magnetic Resonance Imaging) and PET (Positron Emission Tomography) modalities. It enables radiologists to enhance the contrast of images, highlighting specific features and improving the overall diagnostic accuracy. Windowing involves controlling the display range of pixel values to optimize the contrast between different tissues or structures. The display range is defined by two values: the minimum (min) and maximum (max) values that contribute to the final range of pixels that are displayed. By adjusting these values, radiologists can enhance or suppress specific features in the image, facilitating a more accurate diagnosis.\nThe setTextureWindow function utilizes a set of predefined keymap controls to simplify the windowing process. The F1-F7 keys are designated for controlling windowing in MRI and PET modalities. The keymap controls are as follows:\n\nF1: Display wide window for bone (CT) or increase minimum value for PET\nF2: Display window for soft tissues (CT) or increase minimum value for PET\nF3: Display wide window for lung viewing (CT) or increase minimum value for PET\nF4: Decrease minimum value for display\nF5: Increase minimum value for display\nF6: Decrease maximum value for display\nF7: Increase maximum value for display\n\nImplementation of setTextureWindow Function\nThe setTextureWindow function is designed to update the texture window settings based on the input keymap control. The function takes three arguments:\n\nactiveTextur: The current texture specification\nstateObject: The state data fields\nwindowControlStruct: The window control structure containing the letter code for the keymap control\n\nThe function performs the following steps:\n\nChecks the letter code of the keymap control and updates the minimum and maximum values of the texture specification accordingly.\nUpdates the uniforms for the texture specification using the controlMinMaxUniformVals function.\n\nfunction setTextureWindow(activeTextur::TextureSpec, stateObject::StateDataFields, windowControlStruct::WindowControlStruct)\n activeTexturName = activeTextur.name\n displayRange = activeTextur.minAndMaxValue[2] - activeTextur.minAndMaxValue[1]\n activeTexturStudyType = activeTextur.studyType\n if windowControlStruct.letterCode == \"F1\"\n if activeTexturStudyType == \"CT\"\n #Bone windowing in CT\n activeTextur.minAndMaxValue = Float32.([400, 1000])\n elseif activeTexturStudyType == \"PET\"\n activeTextur.minAndMaxValue[1] += 0.10 * displayRange #windowing for pet, in the case of PET simply increase the minimum by 20% , doing the same in f1,f2 and f3\n end\n elseif windowControlStruct.letterCode == \"F2\"\n if activeTexturStudyType == \"CT\"\n activeTextur.minAndMaxValue = Float32.([-40, 350])\n elseif activeTexturStudyType == \"PET\"\n activeTextur.minAndMaxValue[1] += 0.10 * displayRange\n end\n elseif windowControlStruct.letterCode == \"F3\"\n if activeTexturStudyType == \"CT\"\n activeTextur.minAndMaxValue = Float32.([-426, 1000])\n elseif activeTexturStudyType == \"PET\"\n activeTextur.minAndMaxValue[1] += 0.10 * displayRange\n end\n elseif windowControlStruct.letterCode == \"F4\"\n activeTextur.minAndMaxValue[1] -= 0.20 * displayRange\n elseif windowControlStruct.letterCode == \"F5\"\n activeTextur.minAndMaxValue[1] += 0.20 * displayRange\n elseif windowControlStruct.letterCode == \"F6\"\n activeTextur.minAndMaxValue[2] -= 0.20 * displayRange\n elseif windowControlStruct.letterCode == \"F7\"\n activeTextur.minAndMaxValue[2] += 0.20 * displayRange\n elseif windowControlStruct.letterCode == \"F8\"\n activeTextur.uniforms.maskContribution -= 0.10\n elseif windowControlStruct.letterCode == \"F9\"\n activeTextur.uniforms.maskContribution += 0.10\n end\n\n stateObject.mainForDisplayObjects.listOfTextSpecifications = map(texture -> texture.name == activeTexturName ? activeTextur : texture, stateObject.mainForDisplayObjects.listOfTextSpecifications)\n coontrolMinMaxUniformVals(activeTextur)\nend\n\nBone windowing in CT\n\n\n\nBone windowing in PET" + "objectID": "blog/index.html", + "href": "blog/index.html", + "title": "Welcome to the JuliaHealthBlog! πŸ‘‹", + "section": "", + "text": "Welcome to the JuliaHealthBlog! πŸ‘‹\n\n\n\n\n\n\n\n \n \n \n Order By\n Default\n \n Title\n \n \n Date - Oldest\n \n \n Date - Newest\n \n \n Author\n \n \n \n\n\n\n\n\n\n\n\n\n\nDummy Post\n\n\n\n\n\nPost description\n\n\n\n\n\nJun 22, 2024\n\n\nFoobar\n\n\n1 min\n\n\n\n\n\n\nNo matching items" }, { - "objectID": "posts/divyansh-gsoc/gsoc-2024-fellows.html#adding-support-for-multi-image-viewing-with-crosshair-marker-for-image-registration", - "href": "posts/divyansh-gsoc/gsoc-2024-fellows.html#adding-support-for-multi-image-viewing-with-crosshair-marker-for-image-registration", - "title": "GSoC ’24: Adding functionalities to medical imaging visualizations", - "section": "5. Adding support for multi-image viewing with crosshair marker for image registration", - "text": "5. Adding support for multi-image viewing with crosshair marker for image registration\nFollowing the mid-term evaluation, MedEye3d.jl underwent a significant enhancement, whereby a multi-image display capability was implemented through a series of refinements. Specifically, a novel approach was adopted, whereby separate OpenGL fragment shaders were introduced to concurrently render images on either side of the visualizer, namely the left and right views. Prior to integrating voxel data into the fragment shaders, an initial series of tests involved evaluating individual colors to validate the integrity of the double image display. A screenshot from one of these critical testing phases is presented below: \nThe shaders were further manipulated to automatically initialize for each of the images separately. Further, the reactive aspect of the visualizer in multi-image display mode was iterated upon and now, instead of a single state management struct, a vector of states was being passed around, facilitating the user to scroll each of the images separately just by simply hovering their mouse over either of the image, activating its relevant associated state struct.\nDown below, is the struct for state that handles all of the things currently related with an image:\n@with_kw mutable struct StateDataFields\n currentDisplayedSlice::Int = 1 # stores information what slice number we are currently displaying\n mainForDisplayObjects::forDisplayObjects = forDisplayObjects() # stores objects needed to display using OpenGL and GLFW\n onScrollData::FullScrollableDat = FullScrollableDat()\n textureToModifyVec::Vector{TextureSpec} = [] # texture that we want currently to modify - if list is empty it means that we do not intend to modify any texture\n isSliceChanged::Bool = false # set to true when slice is changed set to false when we start interacting with this slice - thanks to this we know that when we start drawing on one slice and change the slice the line would star a new on new slice\n textDispObj::ForWordsDispStruct = ForWordsDispStruct()# set of objects and constants needed for text diplay\n currentlyDispDat::SingleSliceDat = SingleSliceDat() # holds the data displayed or in case of scrollable data view for accessing it\n calcDimsStruct::CalcDimsStruct = CalcDimsStruct() #data for calculations of necessary constants needed to calculate window size , mouse position ...\n valueForMasToSet::valueForMasToSetStruct = valueForMasToSetStruct() # value that will be used to set pixels where we would interact with mouse\n lastRecordedMousePosition::CartesianIndex{3} = CartesianIndex(1, 1, 1) # last position of the mouse related to right click - usefull to know onto which slice to change when dimensions of scroll change\n forUndoVector::AbstractArray = [] # holds lambda functions that when invoked will undo last operations\n maxLengthOfForUndoVector::Int64 = 15 # number controls how many step at maximum we can get back\n fieldKeyboardStruct::KeyboardStruct = KeyboardStruct()\n displayMode::DisplayMode = SingleImage\n imagePosition::Int64 = 1\n switchIndex::Int = 1\n mainRectFields::GlShaderAndBufferFields = GlShaderAndBufferFields()\n crosshairFields::GlShaderAndBufferFields = GlShaderAndBufferFields()\n textFields::GlShaderAndBufferFields = GlShaderAndBufferFields()\n spacingsValue::Union{Vector{Tuple{Float64,Float64,Float64}},Tuple{Float64,Float64,Float64}} = [(1.0, 1.0, 1.0)]\n originValue::Union{Vector{Tuple{Float64,Float64,Float64}},Tuple{Float64,Float64,Float64}} = [(1.0, 1.0, 1.0)]\n supervoxelFields::GlShaderAndBufferFields = GlShaderAndBufferFields()\nend\nAfter the integrity of the fragment shaders was verified in multi-image, voxel data for the images was integrated and further modifications to the high-level functions were made and eventually the following script produced a rather appealing result.\nScript for loading the same NIFTI image twice in the visualizer for side-by-side display:\nusing MedEye3d\nctNiftiImage = \"/home/hurtbadly/Downloads/ct_soft_study.nii.gz\"\nMedEye3d.SegmentationDisplay.displayImage([[ctNiftiImage],[ctNifitImage]])\n\nResults in :\n\n\nCrosshair marker for image registration are displayed in the relevant passive image to hightlight the same anatomical regions based on the spatial meta-data of the images i.e spacing, origin and direction. In order to achive the crosshair rendering in the passive image, the following action items were devised:\n\nRetrieval of GLFW Mouse Callbacks for x and y position of the cursor in window coordinates (0 to window-width) from the active image\nConversion of these x and y window coordinates into their relevant active image x and y texture coordinates\nConversion of these texture coordinates into real space point with the help of spatial metadata\nConversion of the real space point into the texture coordinates of the passive image\nConversion of the passive image texture coordinates into their relevant OpenGL coordinate system values (-1 to 1)\nRendering of crosshair on OpenGL coordinate in passive image\n\nConversion between different coordinate systems and accounting for the image’s spatial metadata during calculating proved to be challenging at first, but with multiple revisions, a final solution was achieved with seemingly no noticeable amount of lag or delay. One such frame of [CT] images with crosshair display in multi-image is depicted below:\n\n\nAnother frame from the openGL rendering cycle, highlighting PET images with crosshair display in multi-image mode:" + "objectID": "blog/posts/dummy/index.html", + "href": "blog/posts/dummy/index.html", + "title": "Dummy Post", + "section": "", + "text": "Seciton 1\nSmall dummy blog post\n\n2 + 2\n\n4\n\n\n\nprintln(2 + 2)\n\n4\n\n\n\n\nSection 2\n\n\nSection 3" }, { - "objectID": "posts/divyansh-gsoc/gsoc-2024-fellows.html#adding-support-for-the-display-of-supervoxels-sv-with-borders-within-the-image-slices-to-better-understand-anatomical-regions-within-slices", - "href": "posts/divyansh-gsoc/gsoc-2024-fellows.html#adding-support-for-the-display-of-supervoxels-sv-with-borders-within-the-image-slices-to-better-understand-anatomical-regions-within-slices", - "title": "GSoC ’24: Adding functionalities to medical imaging visualizations", - "section": "6. Adding support for the display of SuperVoxels sv with borders within the image slices to better understand anatomical regions within slices", - "text": "6. Adding support for the display of SuperVoxels sv with borders within the image slices to better understand anatomical regions within slices\nIn enhancing MedEye3d’s functionality, supporting super voxels (sv) with boundaries becomes paramount. The sv rendering, effectively capturing gradients, serves as the cornerstone for detecting these boundaries within both MRI and PET volumes. Supervoxels, described either through indicator masks or meshes, encapsulate regions of interest with distinct image characteristics. By integrating boundary detection for super-voxels, MedEye3d can offer enhanced segmentation capabilities, enabling more precise delineation and analysis of anatomical structures and pathological regions within medical imaging data.\nSupervoxels are basically a collection of voxels that share similar image properties. For example: in MRI scans of the brain cortex, super voxels could represent clusters of voxels corresponding to specific anatomical regions or functional areas. The main objective of this task was to add support for the display of super voxel-based segmentation of images, followed by some janitorial tasks:\n\nDisplay of the borders of super-voxels (sv), extracted using the machine learning algorithms.\nChecking image gradient agreement with super-voxel borders.\n\nThis initial workflow involved, the initialization of relevant buffers in OpenGL for dynamic rendering of lines over the image display, namely vertex array buffers (vao), vertex buffers (vbo) and edge buffers (ebo). Further, these buffers are updated on a scroll event, where the information from the currently displayed slice is passed to the event handler, which invokes a function that updates the vertex buffer (vbo) with new vertices pertaining to the relevant slice number and planar view, precalculated from an HDF5 file during initialization of the visualizer. For instance, if the user is scrolling in the 3rd axis (transversal plane) and is currently on slice 40, the supervoxel display will pertain to edges specifically calculated for that specific slice in that plane.\nEventually, with ever so increasing number of attempts and a few hurdles along the way, one of which particularly stood out since it marked our first step towards a good direction:\n\nChallenges in rendering\n\n\nAt last, an appealing result hit our sight.\n\nFinal result\n\n\nNote: The image borders are intentional to emphasize the size of the visualizer which is currently defaulted to a certain width and height.\n\n\n\nNote: However, There are a few things left to cover here, most of which revolve around MedImages.jl and documentation for the same. List of PRs that facilitated the completion of the tasks highlighted above:\n\n\nhttps://github.com/JuliaHealth/MedEye3d.jl/pull/21\nhttps://github.com/JuliaHealth/MedEye3d.jl/pull/20\nhttps://github.com/JuliaHealth/MedEye3d.jl/pull/16\nhttps://github.com/JuliaHealth/MedEye3d.jl/pull/14\nhttps://github.com/JuliaHealth/MedEye3d.jl/pull/13\nhttps://github.com/JuliaHealth/MedEye3d.jl/pull/12" + "objectID": "pages/related_organizations.html", + "href": "pages/related_organizations.html", + "title": "Related Organizations", + "section": "", + "text": "This is a (not necessarily comprehensive) list of organizations that focus primarily on developing and maintaining open-source Julia packages related to the life sciences and health sciences.\nIf you would like to add an organization to this list, please feel free to make a pull request." }, { - "objectID": "posts/divyansh-gsoc/gsoc-2024-fellows.html#mentoring-and-guidance", - "href": "posts/divyansh-gsoc/gsoc-2024-fellows.html#mentoring-and-guidance", - "title": "GSoC ’24: Adding functionalities to medical imaging visualizations", - "section": "1. Mentoring and Guidance", - "text": "1. Mentoring and Guidance\nI regularly organized meetings with my mentor to seek guidance on project direction and troubleshooting issues in the visualizer. This ensured that I stayed on track, received timely feedback, and addressed any challenges that arose." + "objectID": "pages/related_organizations.html#julia-community-organizations", + "href": "pages/related_organizations.html#julia-community-organizations", + "title": "Related Organizations", + "section": "Julia community organizations", + "text": "Julia community organizations\n\nBioJulia – Biology, bioinformatics, and computational biology (website | Gitter)\nEcoJulia - Ecology (website)\nJuliaHealth – Medicine, health care, public health, and biomedical research (website)\nJuliaEpi – Epidemiology\nJuliaNeuro - Neuroscience (website)\nJuliaNeuroscience - Neuroscience\nMagneticResonanceImaging - Magnetic resonance imaging" }, { - "objectID": "posts/divyansh-gsoc/gsoc-2024-fellows.html#package-documentation-and-community-contribution", - "href": "posts/divyansh-gsoc/gsoc-2024-fellows.html#package-documentation-and-community-contribution", - "title": "GSoC ’24: Adding functionalities to medical imaging visualizations", - "section": "2. Package Documentation and Community Contribution", - "text": "2. Package Documentation and Community Contribution\nI contributed to other medical imaging sub-ecosystem packages in JuliaHealth, including MedImages.jl and MedEval3D.jl. Specifically, I set up documentation for these packages using DocuementerVitepress.jl. This not only enhanced the functionality of these packages but also helped maintain a coherent and organized package ecosystem." + "objectID": "pages/related_organizations.html#labs-and-research-groups", + "href": "pages/related_organizations.html#labs-and-research-groups", + "title": "Related Organizations", + "section": "Labs and research groups", + "text": "Labs and research groups\n\nBCBI – Center for Biomedical Informatics at Brown University (website)\nHoly Lab - Holy Lab at Washington University in St.Β Louis (website)\nInPhyT - Interdisciplinary Physics Team" }, { - "objectID": "posts/divyansh-gsoc/gsoc-2024-fellows.html#multirepo-management-and-collaboration", - "href": "posts/divyansh-gsoc/gsoc-2024-fellows.html#multirepo-management-and-collaboration", - "title": "GSoC ’24: Adding functionalities to medical imaging visualizations", - "section": "3. Multirepo Management and Collaboration", - "text": "3. Multirepo Management and Collaboration\nIn addition to my work on the MedEye3d visualizer, I made significant contributions to other JuliaHealth repositories, including MedImages.jl and worked over an Insight Toolkit wrapper library ITKIOWrapper.jl for support in image I/O down the road in MedImages.jl. I also maintained relevant documentation and ensured continuous collaboration and synchronization across these packages." + "objectID": "pages/related_organizations.html#companies", + "href": "pages/related_organizations.html#companies", + "title": "Related Organizations", + "section": "Companies", + "text": "Companies\n\nBeacon Biosignals - Intelligent brain monitoring technologies (website)\nPumasAI - Pharmaceutical modeling and simulation (website)" }, { - "objectID": "index.html", - "href": "index.html", - "title": "", + "objectID": "pages/connect_with_us.html", + "href": "pages/connect_with_us.html", + "title": "Connect With Us", "section": "", - "text": "Code\n\n\n\n\n\nWelcome to the JuliaHealthBlog! πŸ‘‹\n\n\n\n\n\n\n\n\n\n \n \n \n Order By\n Default\n \n Title\n \n \n Date - Oldest\n \n \n Date - Newest\n \n \n Author\n \n \n \n\n\n\n\n\n\n\n\n\n\nGSoC ’24: Adding dataset-wide functions and integrations of augmentations\n\n\n\n\n\nMedPipe3D - Medical segmentation pipeline with dataset-wide functions and augmentations.\n\n\n\n\n\nNov 3, 2024\n\n\nJan Zubik\n\n\n32 min\n\n\n\n\n\n\n\n\n\n\n\n\nGSoC ’24: Adding functionalities to medical imaging visualizations\n\n\n\n\n\nA summary of my project for Google Summer of Code - 2024\n\n\n\n\n\nNov 1, 2024\n\n\nDivyansh Goyal\n\n\n17 min\n\n\n\n\n\n\n\n\n\n\n\n\nGSoC Co-Mentoring Experience\n\n\n\n\n\nMy experience as a GSoC co-mentor within JuliaHealth\n\n\n\n\n\nSep 12, 2024\n\n\nMounika Thakkallapally\n\n\n5 min\n\n\n\n\n\n\n\n\n\n\n\n\nGSoC ’24: Developing Tooling for Observational Health Research in Julia\n\n\n\n\n\nA summary of my project for Google Summer of Code - 2024\n\n\n\n\n\nSep 7, 2024\n\n\nJay Sanjay Landge\n\n\n19 min\n\n\n\n\n\n\n\n\n\n\n\n\nGSoC ’24: Enhancements to KomaMRI.jl GPU Support\n\n\n\n\n\nA summary of my project for Google Summer of Code\n\n\n\n\n\nAug 30, 2024\n\n\nRyan Kierulf\n\n\n15 min\n\n\n\n\n\n\n\n\n\n\n\n\nGSoC ’24: IPUMS.jl Small Project\n\n\n\n\n\nA summary of my project for Google Summer of Code\n\n\n\n\n\nAug 26, 2024\n\n\nMichela Rocchetti\n\n\n8 min\n\n\n\n\n\n\n\n\n\n\n\n\nDummy Post\n\n\n\n\n\nPost description\n\n\n\n\n\nJun 22, 2024\n\n\nFoobar\n\n\n1 min\n\n\n\n\n\n\nNo matching items\n\n Back to top" + "text": "Visit us on GitHub: https://github.com/JuliaHealth\nPost in the Biology, Health, and Medicine category on Discourse.\nJoin us in the #biology-health-and-medicine stream on Zulip.\nChat with us in the #health-and-medicine channel on Slack. 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0.125);--bs-btn-disabled-color: #fff;--bs-btn-disabled-bg: #3fb618;--bs-btn-disabled-border-color: #3fb618}.btn-info{--bs-btn-color: #fff;--bs-btn-bg: #9954bb;--bs-btn-border-color: #9954bb;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #82479f;--bs-btn-hover-border-color: #7a4396;--bs-btn-focus-shadow-rgb: 168, 110, 197;--bs-btn-active-color: #fff;--bs-btn-active-bg: #7a4396;--bs-btn-active-border-color: #733f8c;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #fff;--bs-btn-disabled-bg: #9954bb;--bs-btn-disabled-border-color: #9954bb}.btn-warning{--bs-btn-color: #fff;--bs-btn-bg: #ff7518;--bs-btn-border-color: #ff7518;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #d96314;--bs-btn-hover-border-color: #cc5e13;--bs-btn-focus-shadow-rgb: 255, 138, 59;--bs-btn-active-color: #fff;--bs-btn-active-bg: #cc5e13;--bs-btn-active-border-color: #bf5812;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #fff;--bs-btn-disabled-bg: 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none}.btn-outline-primary{--bs-btn-color: #2780e3;--bs-btn-border-color: #2780e3;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #2780e3;--bs-btn-hover-border-color: #2780e3;--bs-btn-focus-shadow-rgb: 39, 128, 227;--bs-btn-active-color: #fff;--bs-btn-active-bg: #2780e3;--bs-btn-active-border-color: #2780e3;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #2780e3;--bs-btn-disabled-bg: transparent;--bs-btn-disabled-border-color: #2780e3;--bs-btn-bg: transparent;--bs-gradient: none}.btn-outline-secondary{--bs-btn-color: #343a40;--bs-btn-border-color: #343a40;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #343a40;--bs-btn-hover-border-color: #343a40;--bs-btn-focus-shadow-rgb: 52, 58, 64;--bs-btn-active-color: #fff;--bs-btn-active-bg: #343a40;--bs-btn-active-border-color: #343a40;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #343a40;--bs-btn-disabled-bg: transparent;--bs-btn-disabled-border-color: #343a40;--bs-btn-bg: 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#ff0039;--bs-btn-disabled-bg: transparent;--bs-btn-disabled-border-color: #ff0039;--bs-btn-bg: transparent;--bs-gradient: none}.btn-outline-light{--bs-btn-color: #f8f9fa;--bs-btn-border-color: #f8f9fa;--bs-btn-hover-color: #000;--bs-btn-hover-bg: #f8f9fa;--bs-btn-hover-border-color: #f8f9fa;--bs-btn-focus-shadow-rgb: 248, 249, 250;--bs-btn-active-color: #000;--bs-btn-active-bg: #f8f9fa;--bs-btn-active-border-color: #f8f9fa;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #f8f9fa;--bs-btn-disabled-bg: transparent;--bs-btn-disabled-border-color: #f8f9fa;--bs-btn-bg: transparent;--bs-gradient: none}.btn-outline-dark{--bs-btn-color: #343a40;--bs-btn-border-color: #343a40;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #343a40;--bs-btn-hover-border-color: #343a40;--bs-btn-focus-shadow-rgb: 52, 58, 64;--bs-btn-active-color: #fff;--bs-btn-active-bg: #343a40;--bs-btn-active-border-color: #343a40;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 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reduce){.accordion-button{transition:none}}.accordion-button:not(.collapsed){color:var(--bs-accordion-active-color);background-color:var(--bs-accordion-active-bg);box-shadow:inset 0 calc(-1*var(--bs-accordion-border-width)) 0 var(--bs-accordion-border-color)}.accordion-button:not(.collapsed)::after{background-image:var(--bs-accordion-btn-active-icon);transform:var(--bs-accordion-btn-icon-transform)}.accordion-button::after{flex-shrink:0;-webkit-flex-shrink:0;width:var(--bs-accordion-btn-icon-width);height:var(--bs-accordion-btn-icon-width);margin-left:auto;content:"";background-image:var(--bs-accordion-btn-icon);background-repeat:no-repeat;background-size:var(--bs-accordion-btn-icon-width);transition:var(--bs-accordion-btn-icon-transition)}@media(prefers-reduced-motion: reduce){.accordion-button::after{transition:none}}.accordion-button:hover{z-index:2}.accordion-button:focus{z-index:3;border-color:var(--bs-accordion-btn-focus-border-color);outline:0;box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.accordion-header{margin-bottom:0}.accordion-item{color:var(--bs-accordion-color);background-color:var(--bs-accordion-bg);border:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.accordion-item:not(:first-of-type){border-top:0}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}[data-bs-theme=dark] .accordion-button::after{--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%237db3ee'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%237db3ee'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e")}.breadcrumb{--bs-breadcrumb-padding-x: 0;--bs-breadcrumb-padding-y: 0;--bs-breadcrumb-margin-bottom: 1rem;--bs-breadcrumb-bg: ;--bs-breadcrumb-border-radius: ;--bs-breadcrumb-divider-color: rgba(52, 58, 64, 0.75);--bs-breadcrumb-item-padding-x: 0.5rem;--bs-breadcrumb-item-active-color: rgba(52, 58, 64, 0.75);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding:var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, ">") /* rtl: var(--bs-breadcrumb-divider, ">") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: 0.75rem;--bs-pagination-padding-y: 0.375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: #2761e3;--bs-pagination-bg: #fff;--bs-pagination-border-width: 1px;--bs-pagination-border-color: #dee2e6;--bs-pagination-border-radius: 0.25rem;--bs-pagination-hover-color: #1f4eb6;--bs-pagination-hover-bg: #f8f9fa;--bs-pagination-hover-border-color: #dee2e6;--bs-pagination-focus-color: #1f4eb6;--bs-pagination-focus-bg: #e9ecef;--bs-pagination-focus-box-shadow: 0 0 0 0.25rem rgba(39, 128, 227, 0.25);--bs-pagination-active-color: #fff;--bs-pagination-active-bg: #2780e3;--bs-pagination-active-border-color: #2780e3;--bs-pagination-disabled-color: rgba(52, 58, 64, 0.75);--bs-pagination-disabled-bg: #e9ecef;--bs-pagination-disabled-border-color: #dee2e6;display:flex;display:-webkit-flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out,box-shadow .15s ease-in-out}@media(prefers-reduced-motion: reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.page-link.active,.active>.page-link{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.page-link.disabled,.disabled>.page-link{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(1px*-1)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: 0.75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: 0.5rem}.pagination-sm{--bs-pagination-padding-x: 0.5rem;--bs-pagination-padding-y: 0.25rem;--bs-pagination-font-size:0.875rem;--bs-pagination-border-radius: 0.2em}.badge{--bs-badge-padding-x: 0.65em;--bs-badge-padding-y: 0.35em;--bs-badge-font-size:0.75em;--bs-badge-font-weight: 700;--bs-badge-color: #fff;--bs-badge-border-radius: 0.25rem;display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: 0 solid var(--bs-alert-border-color);--bs-alert-border-radius: 0.25rem;--bs-alert-link-color: inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-default{--bs-alert-color: var(--bs-default-text-emphasis);--bs-alert-bg: var(--bs-default-bg-subtle);--bs-alert-border-color: var(--bs-default-border-subtle);--bs-alert-link-color: var(--bs-default-text-emphasis)}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:.5rem}}.progress,.progress-stacked{--bs-progress-height: 0.5rem;--bs-progress-font-size:0.75rem;--bs-progress-bg: #e9ecef;--bs-progress-border-radius: 0.25rem;--bs-progress-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.075);--bs-progress-bar-color: #fff;--bs-progress-bar-bg: #2780e3;--bs-progress-bar-transition: width 0.6s ease;display:flex;display:-webkit-flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg)}.progress-bar{display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;justify-content:center;-webkit-justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media(prefers-reduced-motion: reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media(prefers-reduced-motion: reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color: #343a40;--bs-list-group-bg: #fff;--bs-list-group-border-color: #dee2e6;--bs-list-group-border-width: 1px;--bs-list-group-border-radius: 0.25rem;--bs-list-group-item-padding-x: 1rem;--bs-list-group-item-padding-y: 0.5rem;--bs-list-group-action-color: rgba(52, 58, 64, 0.75);--bs-list-group-action-hover-color: #000;--bs-list-group-action-hover-bg: #f8f9fa;--bs-list-group-action-active-color: #343a40;--bs-list-group-action-active-bg: #e9ecef;--bs-list-group-disabled-color: rgba(52, 58, 64, 0.75);--bs-list-group-disabled-bg: #fff;--bs-list-group-active-color: #fff;--bs-list-group-active-bg: #2780e3;--bs-list-group-active-border-color: #2780e3;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;padding-left:0;margin-bottom:0}.list-group-numbered{list-style-type:none;counter-reset:section}.list-group-numbered>.list-group-item::before{content:counters(section, ".") ". ";counter-increment:section}.list-group-item-action{width:100%;color:var(--bs-list-group-action-color);text-align:inherit}.list-group-item-action:hover,.list-group-item-action:focus{z-index:1;color:var(--bs-list-group-action-hover-color);text-decoration:none;background-color:var(--bs-list-group-action-hover-bg)}.list-group-item-action:active{color:var(--bs-list-group-action-active-color);background-color:var(--bs-list-group-action-active-bg)}.list-group-item{position:relative;display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);color:var(--bs-list-group-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid var(--bs-list-group-border-color)}.list-group-item.disabled,.list-group-item:disabled{color:var(--bs-list-group-disabled-color);pointer-events:none;background-color:var(--bs-list-group-disabled-bg)}.list-group-item.active{z-index:2;color:var(--bs-list-group-active-color);background-color:var(--bs-list-group-active-bg);border-color:var(--bs-list-group-active-border-color)}.list-group-item+.list-group-item{border-top-width:0}.list-group-item+.list-group-item.active{margin-top:calc(-1*var(--bs-list-group-border-width));border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal>.list-group-item.active{margin-top:0}.list-group-horizontal>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}@media(min-width: 576px){.list-group-horizontal-sm{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 768px){.list-group-horizontal-md{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-md>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 992px){.list-group-horizontal-lg{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1200px){.list-group-horizontal-xl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1400px){.list-group-horizontal-xxl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xxl>.list-group-item.active{margin-top:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}.list-group-flush>.list-group-item{border-width:0 0 var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-default{--bs-list-group-color: var(--bs-default-text-emphasis);--bs-list-group-bg: var(--bs-default-bg-subtle);--bs-list-group-border-color: var(--bs-default-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-default-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-default-border-subtle);--bs-list-group-active-color: var(--bs-default-bg-subtle);--bs-list-group-active-bg: var(--bs-default-text-emphasis);--bs-list-group-active-border-color: var(--bs-default-text-emphasis)}.list-group-item-primary{--bs-list-group-color: var(--bs-primary-text-emphasis);--bs-list-group-bg: var(--bs-primary-bg-subtle);--bs-list-group-border-color: var(--bs-primary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-primary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-primary-border-subtle);--bs-list-group-active-color: var(--bs-primary-bg-subtle);--bs-list-group-active-bg: var(--bs-primary-text-emphasis);--bs-list-group-active-border-color: var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color: var(--bs-secondary-text-emphasis);--bs-list-group-bg: var(--bs-secondary-bg-subtle);--bs-list-group-border-color: var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-secondary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-secondary-border-subtle);--bs-list-group-active-color: var(--bs-secondary-bg-subtle);--bs-list-group-active-bg: var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-info-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-info-border-subtle);--bs-list-group-active-color: var(--bs-info-bg-subtle);--bs-list-group-active-bg: var(--bs-info-text-emphasis);--bs-list-group-active-border-color: var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color: var(--bs-warning-text-emphasis);--bs-list-group-bg: var(--bs-warning-bg-subtle);--bs-list-group-border-color: var(--bs-warning-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-warning-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-warning-border-subtle);--bs-list-group-active-color: var(--bs-warning-bg-subtle);--bs-list-group-active-bg: var(--bs-warning-text-emphasis);--bs-list-group-active-border-color: var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-dark-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-dark-border-subtle);--bs-list-group-active-color: var(--bs-dark-bg-subtle);--bs-list-group-active-bg: var(--bs-dark-text-emphasis);--bs-list-group-active-border-color: var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color: #000;--bs-btn-close-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23000'%3e%3cpath d='M.293.293a1 1 0 0 1 1.414 0L8 6.586 14.293.293a1 1 0 1 1 1.414 1.414L9.414 8l6.293 6.293a1 1 0 0 1-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 0 1-1.414-1.414L6.586 8 .293 1.707a1 1 0 0 1 0-1.414z'/%3e%3c/svg%3e");--bs-btn-close-opacity: 0.5;--bs-btn-close-hover-opacity: 0.75;--bs-btn-close-focus-shadow: 0 0 0 0.25rem rgba(39, 128, 227, 0.25);--bs-btn-close-focus-opacity: 1;--bs-btn-close-disabled-opacity: 0.25;--bs-btn-close-white-filter: invert(1) grayscale(100%) brightness(200%);box-sizing:content-box;width:1em;height:1em;padding:.25em .25em;color:var(--bs-btn-close-color);background:rgba(0,0,0,0) var(--bs-btn-close-bg) center/1em auto no-repeat;border:0;opacity:var(--bs-btn-close-opacity)}.btn-close:hover{color:var(--bs-btn-close-color);text-decoration:none;opacity:var(--bs-btn-close-hover-opacity)}.btn-close:focus{outline:0;box-shadow:var(--bs-btn-close-focus-shadow);opacity:var(--bs-btn-close-focus-opacity)}.btn-close:disabled,.btn-close.disabled{pointer-events:none;user-select:none;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;-o-user-select:none;opacity:var(--bs-btn-close-disabled-opacity)}.btn-close-white{filter:var(--bs-btn-close-white-filter)}[data-bs-theme=dark] .btn-close{filter:var(--bs-btn-close-white-filter)}.toast{--bs-toast-zindex: 1090;--bs-toast-padding-x: 0.75rem;--bs-toast-padding-y: 0.5rem;--bs-toast-spacing: 1.5rem;--bs-toast-max-width: 350px;--bs-toast-font-size:0.875rem;--bs-toast-color: ;--bs-toast-bg: rgba(255, 255, 255, 0.85);--bs-toast-border-width: 1px;--bs-toast-border-color: rgba(0, 0, 0, 0.175);--bs-toast-border-radius: 0.25rem;--bs-toast-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15);--bs-toast-header-color: rgba(52, 58, 64, 0.75);--bs-toast-header-bg: rgba(255, 255, 255, 0.85);--bs-toast-header-border-color: rgba(0, 0, 0, 0.175);width:var(--bs-toast-max-width);max-width:100%;font-size:var(--bs-toast-font-size);color:var(--bs-toast-color);pointer-events:auto;background-color:var(--bs-toast-bg);background-clip:padding-box;border:var(--bs-toast-border-width) solid var(--bs-toast-border-color);box-shadow:var(--bs-toast-box-shadow)}.toast.showing{opacity:0}.toast:not(.show){display:none}.toast-container{--bs-toast-zindex: 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0.175);--bs-modal-border-width: 1px;--bs-modal-border-radius: 0.5rem;--bs-modal-box-shadow: 0 0.125rem 0.25rem rgba(0, 0, 0, 0.075);--bs-modal-inner-border-radius: calc(0.5rem - 1px);--bs-modal-header-padding-x: 1rem;--bs-modal-header-padding-y: 1rem;--bs-modal-header-padding: 1rem 1rem;--bs-modal-header-border-color: #dee2e6;--bs-modal-header-border-width: 1px;--bs-modal-title-line-height: 1.5;--bs-modal-footer-gap: 0.5rem;--bs-modal-footer-bg: ;--bs-modal-footer-border-color: #dee2e6;--bs-modal-footer-border-width: 1px;position:fixed;top:0;left:0;z-index:var(--bs-modal-zindex);display:none;width:100%;height:100%;overflow-x:hidden;overflow-y:auto;outline:0}.modal-dialog{position:relative;width:auto;margin:var(--bs-modal-margin);pointer-events:none}.modal.fade .modal-dialog{transition:transform .3s ease-out;transform:translate(0, -50px)}@media(prefers-reduced-motion: reduce){.modal.fade .modal-dialog{transition:none}}.modal.show .modal-dialog{transform:none}.modal.modal-static 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0.5;position:fixed;top:0;left:0;z-index:var(--bs-backdrop-zindex);width:100vw;height:100vh;background-color:var(--bs-backdrop-bg)}.modal-backdrop.fade{opacity:0}.modal-backdrop.show{opacity:var(--bs-backdrop-opacity)}.modal-header{display:flex;display:-webkit-flex;flex-shrink:0;-webkit-flex-shrink:0;align-items:center;-webkit-align-items:center;justify-content:space-between;-webkit-justify-content:space-between;padding:var(--bs-modal-header-padding);border-bottom:var(--bs-modal-header-border-width) solid var(--bs-modal-header-border-color)}.modal-header .btn-close{padding:calc(var(--bs-modal-header-padding-y)*.5) calc(var(--bs-modal-header-padding-x)*.5);margin:calc(-0.5*var(--bs-modal-header-padding-y)) calc(-0.5*var(--bs-modal-header-padding-x)) calc(-0.5*var(--bs-modal-header-padding-y)) auto}.modal-title{margin-bottom:0;line-height:var(--bs-modal-title-line-height)}.modal-body{position:relative;flex:1 1 auto;-webkit-flex:1 1 auto;padding:var(--bs-modal-padding)}.modal-footer{display:flex;display:-webkit-flex;flex-shrink:0;-webkit-flex-shrink:0;flex-wrap:wrap;-webkit-flex-wrap:wrap;align-items:center;-webkit-align-items:center;justify-content:flex-end;-webkit-justify-content:flex-end;padding:calc(var(--bs-modal-padding) - var(--bs-modal-footer-gap)*.5);background-color:var(--bs-modal-footer-bg);border-top:var(--bs-modal-footer-border-width) solid var(--bs-modal-footer-border-color)}.modal-footer>*{margin:calc(var(--bs-modal-footer-gap)*.5)}@media(min-width: 576px){.modal{--bs-modal-margin: 1.75rem;--bs-modal-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15)}.modal-dialog{max-width:var(--bs-modal-width);margin-right:auto;margin-left:auto}.modal-sm{--bs-modal-width: 300px}}@media(min-width: 992px){.modal-lg,.modal-xl{--bs-modal-width: 800px}}@media(min-width: 1200px){.modal-xl{--bs-modal-width: 1140px}}.modal-fullscreen{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen .modal-content{height:100%;border:0}.modal-fullscreen .modal-body{overflow-y:auto}@media(max-width: 575.98px){.modal-fullscreen-sm-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-sm-down .modal-content{height:100%;border:0}.modal-fullscreen-sm-down .modal-body{overflow-y:auto}}@media(max-width: 767.98px){.modal-fullscreen-md-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-md-down .modal-content{height:100%;border:0}.modal-fullscreen-md-down .modal-body{overflow-y:auto}}@media(max-width: 991.98px){.modal-fullscreen-lg-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-lg-down .modal-content{height:100%;border:0}.modal-fullscreen-lg-down .modal-body{overflow-y:auto}}@media(max-width: 1199.98px){.modal-fullscreen-xl-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-xl-down .modal-content{height:100%;border:0}.modal-fullscreen-xl-down .modal-body{overflow-y:auto}}@media(max-width: 1399.98px){.modal-fullscreen-xxl-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-xxl-down .modal-content{height:100%;border:0}.modal-fullscreen-xxl-down .modal-body{overflow-y:auto}}.tooltip{--bs-tooltip-zindex: 1080;--bs-tooltip-max-width: 200px;--bs-tooltip-padding-x: 0.5rem;--bs-tooltip-padding-y: 0.25rem;--bs-tooltip-margin: ;--bs-tooltip-font-size:0.875rem;--bs-tooltip-color: #fff;--bs-tooltip-bg: #000;--bs-tooltip-border-radius: 0.25rem;--bs-tooltip-opacity: 0.9;--bs-tooltip-arrow-width: 0.8rem;--bs-tooltip-arrow-height: 0.4rem;z-index:var(--bs-tooltip-zindex);display:block;margin:var(--bs-tooltip-margin);font-family:"Source Sans Pro",-apple-system,BlinkMacSystemFont,"Segoe UI",Roboto,"Helvetica Neue",Arial,sans-serif,"Apple Color Emoji","Segoe UI Emoji","Segoe UI Symbol";font-style:normal;font-weight:400;line-height:1.5;text-align:left;text-align:start;text-decoration:none;text-shadow:none;text-transform:none;letter-spacing:normal;word-break:normal;white-space:normal;word-spacing:normal;line-break:auto;font-size:var(--bs-tooltip-font-size);word-wrap:break-word;opacity:0}.tooltip.show{opacity:var(--bs-tooltip-opacity)}.tooltip .tooltip-arrow{display:block;width:var(--bs-tooltip-arrow-width);height:var(--bs-tooltip-arrow-height)}.tooltip .tooltip-arrow::before{position:absolute;content:"";border-color:rgba(0,0,0,0);border-style:solid}.bs-tooltip-top .tooltip-arrow,.bs-tooltip-auto[data-popper-placement^=top] .tooltip-arrow{bottom:calc(-1*var(--bs-tooltip-arrow-height))}.bs-tooltip-top .tooltip-arrow::before,.bs-tooltip-auto[data-popper-placement^=top] .tooltip-arrow::before{top:-1px;border-width:var(--bs-tooltip-arrow-height) calc(var(--bs-tooltip-arrow-width)*.5) 0;border-top-color:var(--bs-tooltip-bg)}.bs-tooltip-end 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var(--bs-popover-border-width))}.bs-popover-top>.popover-arrow::before,.bs-popover-auto[data-popper-placement^=top]>.popover-arrow::before,.bs-popover-top>.popover-arrow::after,.bs-popover-auto[data-popper-placement^=top]>.popover-arrow::after{border-width:var(--bs-popover-arrow-height) calc(var(--bs-popover-arrow-width)*.5) 0}.bs-popover-top>.popover-arrow::before,.bs-popover-auto[data-popper-placement^=top]>.popover-arrow::before{bottom:0;border-top-color:var(--bs-popover-arrow-border)}.bs-popover-top>.popover-arrow::after,.bs-popover-auto[data-popper-placement^=top]>.popover-arrow::after{bottom:var(--bs-popover-border-width);border-top-color:var(--bs-popover-bg)}.bs-popover-end>.popover-arrow,.bs-popover-auto[data-popper-placement^=right]>.popover-arrow{left:calc(-1*(var(--bs-popover-arrow-height)) - 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#fff;--quarto-scss-export-nav-underline-link-active-color: #000;--quarto-scss-export-navbar-padding-x: ;--quarto-scss-export-navbar-light-contrast: #000;--quarto-scss-export-navbar-dark-contrast: #000;--quarto-scss-export-navbar-light-icon-color: rgba(0, 0, 0, 0.75);--quarto-scss-export-navbar-dark-icon-color: rgba(0, 0, 0, 0.75);--quarto-scss-export-dropdown-color: #343a40;--quarto-scss-export-dropdown-bg: #fff;--quarto-scss-export-dropdown-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-dropdown-divider-bg: rgba(0, 0, 0, 0.175);--quarto-scss-export-dropdown-link-color: #343a40;--quarto-scss-export-dropdown-link-hover-color: #343a40;--quarto-scss-export-dropdown-link-hover-bg: #f8f9fa;--quarto-scss-export-dropdown-link-active-bg: #2780e3;--quarto-scss-export-dropdown-link-active-color: #fff;--quarto-scss-export-dropdown-link-disabled-color: rgba(52, 58, 64, 0.5);--quarto-scss-export-dropdown-header-color: #6c757d;--quarto-scss-export-dropdown-dark-color: #dee2e6;--quarto-scss-export-dropdown-dark-bg: #343a40;--quarto-scss-export-dropdown-dark-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-dropdown-dark-divider-bg: rgba(0, 0, 0, 0.175);--quarto-scss-export-dropdown-dark-box-shadow: ;--quarto-scss-export-dropdown-dark-link-color: #dee2e6;--quarto-scss-export-dropdown-dark-link-hover-color: #fff;--quarto-scss-export-dropdown-dark-link-hover-bg: rgba(255, 255, 255, 0.15);--quarto-scss-export-dropdown-dark-link-active-color: #fff;--quarto-scss-export-dropdown-dark-link-active-bg: #2780e3;--quarto-scss-export-dropdown-dark-link-disabled-color: #adb5bd;--quarto-scss-export-dropdown-dark-header-color: #adb5bd;--quarto-scss-export-pagination-color: #2761e3;--quarto-scss-export-pagination-bg: #fff;--quarto-scss-export-pagination-border-color: #dee2e6;--quarto-scss-export-pagination-focus-color: #1f4eb6;--quarto-scss-export-pagination-focus-bg: #e9ecef;--quarto-scss-export-pagination-hover-color: #1f4eb6;--quarto-scss-export-pagination-hover-bg: #f8f9fa;--quarto-scss-export-pagination-hover-border-color: #dee2e6;--quarto-scss-export-pagination-active-color: #fff;--quarto-scss-export-pagination-active-bg: #2780e3;--quarto-scss-export-pagination-active-border-color: #2780e3;--quarto-scss-export-pagination-disabled-color: rgba(52, 58, 64, 0.75);--quarto-scss-export-pagination-disabled-bg: #e9ecef;--quarto-scss-export-pagination-disabled-border-color: #dee2e6;--quarto-scss-export-card-title-color: ;--quarto-scss-export-card-subtitle-color: ;--quarto-scss-export-card-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-card-box-shadow: ;--quarto-scss-export-card-cap-color: ;--quarto-scss-export-card-height: ;--quarto-scss-export-card-color: ;--quarto-scss-export-card-bg: #fff;--quarto-scss-export-accordion-color: #343a40;--quarto-scss-export-accordion-bg: #fff;--quarto-scss-export-accordion-border-color: #dee2e6;--quarto-scss-export-accordion-button-color: #343a40;--quarto-scss-export-accordion-button-bg: #fff;--quarto-scss-export-accordion-button-active-bg: #d4e6f9;--quarto-scss-export-accordion-button-active-color: #10335b;--quarto-scss-export-accordion-button-focus-border-color: #93c0f1;--quarto-scss-export-accordion-icon-color: #343a40;--quarto-scss-export-accordion-icon-active-color: #10335b;--quarto-scss-export-tooltip-color: #fff;--quarto-scss-export-tooltip-bg: #000;--quarto-scss-export-tooltip-margin: ;--quarto-scss-export-tooltip-arrow-color: ;--quarto-scss-export-form-feedback-tooltip-line-height: ;--quarto-scss-export-popover-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-popover-header-bg: #e9ecef;--quarto-scss-export-popover-body-color: #343a40;--quarto-scss-export-popover-arrow-color: #fff;--quarto-scss-export-popover-arrow-outer-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-toast-color: ;--quarto-scss-export-toast-background-color: rgba(255, 255, 255, 0.85);--quarto-scss-export-toast-border-color: rgba(0, 0, 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#2780e3;--quarto-scss-export-list-group-active-color: #fff;--quarto-scss-export-list-group-active-border-color: #2780e3;--quarto-scss-export-list-group-disabled-color: rgba(52, 58, 64, 0.75);--quarto-scss-export-list-group-disabled-bg: #fff;--quarto-scss-export-list-group-action-color: rgba(52, 58, 64, 0.75);--quarto-scss-export-list-group-action-hover-color: #000;--quarto-scss-export-list-group-action-active-color: #343a40;--quarto-scss-export-list-group-action-active-bg: #e9ecef;--quarto-scss-export-thumbnail-bg: #fff;--quarto-scss-export-thumbnail-border-color: #dee2e6;--quarto-scss-export-figure-caption-color: rgba(52, 58, 64, 0.75);--quarto-scss-export-breadcrumb-font-size: ;--quarto-scss-export-breadcrumb-bg: ;--quarto-scss-export-breadcrumb-divider-color: rgba(52, 58, 64, 0.75);--quarto-scss-export-breadcrumb-active-color: rgba(52, 58, 64, 0.75);--quarto-scss-export-breadcrumb-border-radius: ;--quarto-scss-export-carousel-control-color: 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0, 0, 0), 0.1);--quarto-scss-export-sidebar-color: #545555;--quarto-scss-export-sidebar-hover-color: rgba(31, 78, 182, 0.8);--quarto-scss-export-sidebar-disabled-color: rgba(84, 85, 85, 0.75);--quarto-scss-export-valuebox-bg-primary: #5397e9;--quarto-scss-export-valuebox-bg-secondary: #343a40;--quarto-scss-export-valuebox-bg-success: #3aa716;--quarto-scss-export-valuebox-bg-info: rgba(153, 84, 187, 0.7019607843);--quarto-scss-export-valuebox-bg-warning: #fa6400;--quarto-scss-export-valuebox-bg-danger: rgba(255, 0, 57, 0.7019607843);--quarto-scss-export-valuebox-bg-light: #f8f9fa;--quarto-scss-export-valuebox-bg-dark: #343a40;--quarto-scss-export-mermaid-bg-color: #fff;--quarto-scss-export-mermaid-edge-color: #343a40;--quarto-scss-export-mermaid-node-fg-color: #343a40;--quarto-scss-export-mermaid-fg-color: #343a40;--quarto-scss-export-mermaid-fg-color--lighter: #4b545c;--quarto-scss-export-mermaid-fg-color--lightest: #626d78;--quarto-scss-export-mermaid-label-bg-color: #fff;--quarto-scss-export-mermaid-label-fg-color: #2780e3;--quarto-scss-export-mermaid-node-bg-color: rgba(39, 128, 227, 0.1);--quarto-scss-export-code-block-border-left-color: #dee2e6;--quarto-scss-export-callout-color-note: #2780e3;--quarto-scss-export-callout-color-tip: #3fb618;--quarto-scss-export-callout-color-important: #ff0039;--quarto-scss-export-callout-color-caution: #f0ad4e;--quarto-scss-export-callout-color-warning: #ff7518} \ No newline at end of file diff --git a/docs/site_libs/bootstrap/bootstrap-b91f5432c59177e88330fbc179e69942.min.css b/docs/site_libs/bootstrap/bootstrap-b91f5432c59177e88330fbc179e69942.min.css new file mode 100644 index 0000000..a953113 --- /dev/null +++ b/docs/site_libs/bootstrap/bootstrap-b91f5432c59177e88330fbc179e69942.min.css @@ -0,0 +1,12 @@ +/*! + * Bootstrap v5.3.1 (https://getbootstrap.com/) + * Copyright 2011-2023 The Bootstrap Authors + * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE) + */@import"https://fonts.googleapis.com/css2?family=Source+Sans+Pro:wght@300;400;700&display=swap";:root,[data-bs-theme=light]{--bs-blue: #2780e3;--bs-indigo: #6610f2;--bs-purple: #613d7c;--bs-pink: #e83e8c;--bs-red: #ff0039;--bs-orange: #f0ad4e;--bs-yellow: #ff7518;--bs-green: #3fb618;--bs-teal: #20c997;--bs-cyan: #9954bb;--bs-black: #000;--bs-white: #fff;--bs-gray: #6c757d;--bs-gray-dark: #343a40;--bs-gray-100: #f8f9fa;--bs-gray-200: #e9ecef;--bs-gray-300: #dee2e6;--bs-gray-400: #ced4da;--bs-gray-500: #adb5bd;--bs-gray-600: #6c757d;--bs-gray-700: #495057;--bs-gray-800: #343a40;--bs-gray-900: #212529;--bs-default: #343a40;--bs-primary: #2780e3;--bs-secondary: #343a40;--bs-success: #3fb618;--bs-info: #9954bb;--bs-warning: #ff7518;--bs-danger: #ff0039;--bs-light: #f8f9fa;--bs-dark: #343a40;--bs-default-rgb: 52, 58, 64;--bs-primary-rgb: 39, 128, 227;--bs-secondary-rgb: 52, 58, 64;--bs-success-rgb: 63, 182, 24;--bs-info-rgb: 153, 84, 187;--bs-warning-rgb: 255, 117, 24;--bs-danger-rgb: 255, 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.nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-sm .navbar-nav-scroll{overflow:visible}.navbar-expand-sm .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-sm .navbar-toggler{display:none}.navbar-expand-sm .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-sm .offcanvas .offcanvas-header{display:none}.navbar-expand-sm .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}@media(min-width: 768px){.navbar-expand-md{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-md .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-md .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-md .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-md .navbar-nav-scroll{overflow:visible}.navbar-expand-md .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-md .navbar-toggler{display:none}.navbar-expand-md .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-md .offcanvas .offcanvas-header{display:none}.navbar-expand-md .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}@media(min-width: 992px){.navbar-expand-lg{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-lg .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-lg .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-lg .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-lg .navbar-nav-scroll{overflow:visible}.navbar-expand-lg .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-lg .navbar-toggler{display:none}.navbar-expand-lg .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-lg .offcanvas .offcanvas-header{display:none}.navbar-expand-lg .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}@media(min-width: 1200px){.navbar-expand-xl{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-xl .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-xl .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-xl .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-xl .navbar-nav-scroll{overflow:visible}.navbar-expand-xl .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-xl .navbar-toggler{display:none}.navbar-expand-xl .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-xl .offcanvas .offcanvas-header{display:none}.navbar-expand-xl .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}@media(min-width: 1400px){.navbar-expand-xxl{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-xxl .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-xxl .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-xxl .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-xxl .navbar-nav-scroll{overflow:visible}.navbar-expand-xxl .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-xxl .navbar-toggler{display:none}.navbar-expand-xxl .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-xxl .offcanvas .offcanvas-header{display:none}.navbar-expand-xxl .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}.navbar-expand{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand .navbar-nav .dropdown-menu{position:absolute}.navbar-expand .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand .navbar-nav-scroll{overflow:visible}.navbar-expand .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand .navbar-toggler{display:none}.navbar-expand .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand .offcanvas .offcanvas-header{display:none}.navbar-expand .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}.navbar-dark,.navbar[data-bs-theme=dark]{--bs-navbar-color: #545555;--bs-navbar-hover-color: rgba(31, 78, 182, 0.8);--bs-navbar-disabled-color: rgba(84, 85, 85, 0.75);--bs-navbar-active-color: #1f4eb6;--bs-navbar-brand-color: #545555;--bs-navbar-brand-hover-color: #1f4eb6;--bs-navbar-toggler-border-color: rgba(84, 85, 85, 0);--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='%23545555' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e")}[data-bs-theme=dark] .navbar-toggler-icon{--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='%23545555' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e")}.card{--bs-card-spacer-y: 1rem;--bs-card-spacer-x: 1rem;--bs-card-title-spacer-y: 0.5rem;--bs-card-title-color: ;--bs-card-subtitle-color: ;--bs-card-border-width: 1px;--bs-card-border-color: rgba(0, 0, 0, 0.175);--bs-card-border-radius: 0.25rem;--bs-card-box-shadow: ;--bs-card-inner-border-radius: calc(0.25rem - 1px);--bs-card-cap-padding-y: 0.5rem;--bs-card-cap-padding-x: 1rem;--bs-card-cap-bg: rgba(52, 58, 64, 0.25);--bs-card-cap-color: ;--bs-card-height: ;--bs-card-color: ;--bs-card-bg: #fff;--bs-card-img-overlay-padding: 1rem;--bs-card-group-margin: 0.75rem;position:relative;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;min-width:0;height:var(--bs-card-height);color:var(--bs-body-color);word-wrap:break-word;background-color:var(--bs-card-bg);background-clip:border-box;border:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card>hr{margin-right:0;margin-left:0}.card>.list-group{border-top:inherit;border-bottom:inherit}.card>.list-group:first-child{border-top-width:0}.card>.list-group:last-child{border-bottom-width:0}.card>.card-header+.list-group,.card>.list-group+.card-footer{border-top:0}.card-body{flex:1 1 auto;-webkit-flex:1 1 auto;padding:var(--bs-card-spacer-y) var(--bs-card-spacer-x);color:var(--bs-card-color)}.card-title{margin-bottom:var(--bs-card-title-spacer-y);color:var(--bs-card-title-color)}.card-subtitle{margin-top:calc(-0.5*var(--bs-card-title-spacer-y));margin-bottom:0;color:var(--bs-card-subtitle-color)}.card-text:last-child{margin-bottom:0}.card-link+.card-link{margin-left:var(--bs-card-spacer-x)}.card-header{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);margin-bottom:0;color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-bottom:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-footer{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-top:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-header-tabs{margin-right:calc(-0.5*var(--bs-card-cap-padding-x));margin-bottom:calc(-1*var(--bs-card-cap-padding-y));margin-left:calc(-0.5*var(--bs-card-cap-padding-x));border-bottom:0}.card-header-tabs .nav-link.active{background-color:var(--bs-card-bg);border-bottom-color:var(--bs-card-bg)}.card-header-pills{margin-right:calc(-0.5*var(--bs-card-cap-padding-x));margin-left:calc(-0.5*var(--bs-card-cap-padding-x))}.card-img-overlay{position:absolute;top:0;right:0;bottom:0;left:0;padding:var(--bs-card-img-overlay-padding)}.card-img,.card-img-top,.card-img-bottom{width:100%}.card-group>.card{margin-bottom:var(--bs-card-group-margin)}@media(min-width: 576px){.card-group{display:flex;display:-webkit-flex;flex-flow:row wrap;-webkit-flex-flow:row wrap}.card-group>.card{flex:1 0 0%;-webkit-flex:1 0 0%;margin-bottom:0}.card-group>.card+.card{margin-left:0;border-left:0}}.accordion{--bs-accordion-color: #343a40;--bs-accordion-bg: #fff;--bs-accordion-transition: color 0.15s ease-in-out, background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out, border-radius 0.15s ease;--bs-accordion-border-color: #dee2e6;--bs-accordion-border-width: 1px;--bs-accordion-border-radius: 0.25rem;--bs-accordion-inner-border-radius: calc(0.25rem - 1px);--bs-accordion-btn-padding-x: 1.25rem;--bs-accordion-btn-padding-y: 1rem;--bs-accordion-btn-color: #343a40;--bs-accordion-btn-bg: #fff;--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23343a40'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-icon-width: 1.25rem;--bs-accordion-btn-icon-transform: rotate(-180deg);--bs-accordion-btn-icon-transition: transform 0.2s ease-in-out;--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%2310335b'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-focus-border-color: #93c0f1;--bs-accordion-btn-focus-box-shadow: 0 0 0 0.25rem rgba(39, 128, 227, 0.25);--bs-accordion-body-padding-x: 1.25rem;--bs-accordion-body-padding-y: 1rem;--bs-accordion-active-color: #10335b;--bs-accordion-active-bg: #d4e6f9}.accordion-button{position:relative;display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;width:100%;padding:var(--bs-accordion-btn-padding-y) var(--bs-accordion-btn-padding-x);font-size:1rem;color:var(--bs-accordion-btn-color);text-align:left;background-color:var(--bs-accordion-btn-bg);border:0;overflow-anchor:none;transition:var(--bs-accordion-transition)}@media(prefers-reduced-motion: reduce){.accordion-button{transition:none}}.accordion-button:not(.collapsed){color:var(--bs-accordion-active-color);background-color:var(--bs-accordion-active-bg);box-shadow:inset 0 calc(-1*var(--bs-accordion-border-width)) 0 var(--bs-accordion-border-color)}.accordion-button:not(.collapsed)::after{background-image:var(--bs-accordion-btn-active-icon);transform:var(--bs-accordion-btn-icon-transform)}.accordion-button::after{flex-shrink:0;-webkit-flex-shrink:0;width:var(--bs-accordion-btn-icon-width);height:var(--bs-accordion-btn-icon-width);margin-left:auto;content:"";background-image:var(--bs-accordion-btn-icon);background-repeat:no-repeat;background-size:var(--bs-accordion-btn-icon-width);transition:var(--bs-accordion-btn-icon-transition)}@media(prefers-reduced-motion: reduce){.accordion-button::after{transition:none}}.accordion-button:hover{z-index:2}.accordion-button:focus{z-index:3;border-color:var(--bs-accordion-btn-focus-border-color);outline:0;box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.accordion-header{margin-bottom:0}.accordion-item{color:var(--bs-accordion-color);background-color:var(--bs-accordion-bg);border:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.accordion-item:not(:first-of-type){border-top:0}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}[data-bs-theme=dark] .accordion-button::after{--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%237db3ee'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%237db3ee'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e")}.breadcrumb{--bs-breadcrumb-padding-x: 0;--bs-breadcrumb-padding-y: 0;--bs-breadcrumb-margin-bottom: 1rem;--bs-breadcrumb-bg: ;--bs-breadcrumb-border-radius: ;--bs-breadcrumb-divider-color: rgba(52, 58, 64, 0.75);--bs-breadcrumb-item-padding-x: 0.5rem;--bs-breadcrumb-item-active-color: rgba(52, 58, 64, 0.75);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding:var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, ">") /* rtl: var(--bs-breadcrumb-divider, ">") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: 0.75rem;--bs-pagination-padding-y: 0.375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: #2761e3;--bs-pagination-bg: #fff;--bs-pagination-border-width: 1px;--bs-pagination-border-color: #dee2e6;--bs-pagination-border-radius: 0.25rem;--bs-pagination-hover-color: #1f4eb6;--bs-pagination-hover-bg: #f8f9fa;--bs-pagination-hover-border-color: #dee2e6;--bs-pagination-focus-color: #1f4eb6;--bs-pagination-focus-bg: #e9ecef;--bs-pagination-focus-box-shadow: 0 0 0 0.25rem rgba(39, 128, 227, 0.25);--bs-pagination-active-color: #fff;--bs-pagination-active-bg: #2780e3;--bs-pagination-active-border-color: #2780e3;--bs-pagination-disabled-color: rgba(52, 58, 64, 0.75);--bs-pagination-disabled-bg: #e9ecef;--bs-pagination-disabled-border-color: #dee2e6;display:flex;display:-webkit-flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out,box-shadow .15s ease-in-out}@media(prefers-reduced-motion: reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.page-link.active,.active>.page-link{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.page-link.disabled,.disabled>.page-link{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(1px*-1)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: 0.75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: 0.5rem}.pagination-sm{--bs-pagination-padding-x: 0.5rem;--bs-pagination-padding-y: 0.25rem;--bs-pagination-font-size:0.875rem;--bs-pagination-border-radius: 0.2em}.badge{--bs-badge-padding-x: 0.65em;--bs-badge-padding-y: 0.35em;--bs-badge-font-size:0.75em;--bs-badge-font-weight: 700;--bs-badge-color: #fff;--bs-badge-border-radius: 0.25rem;display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: 0 solid var(--bs-alert-border-color);--bs-alert-border-radius: 0.25rem;--bs-alert-link-color: inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-default{--bs-alert-color: var(--bs-default-text-emphasis);--bs-alert-bg: var(--bs-default-bg-subtle);--bs-alert-border-color: var(--bs-default-border-subtle);--bs-alert-link-color: var(--bs-default-text-emphasis)}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:.5rem}}.progress,.progress-stacked{--bs-progress-height: 0.5rem;--bs-progress-font-size:0.75rem;--bs-progress-bg: #e9ecef;--bs-progress-border-radius: 0.25rem;--bs-progress-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.075);--bs-progress-bar-color: #fff;--bs-progress-bar-bg: #2780e3;--bs-progress-bar-transition: width 0.6s ease;display:flex;display:-webkit-flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg)}.progress-bar{display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;justify-content:center;-webkit-justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media(prefers-reduced-motion: reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media(prefers-reduced-motion: reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color: #343a40;--bs-list-group-bg: #fff;--bs-list-group-border-color: #dee2e6;--bs-list-group-border-width: 1px;--bs-list-group-border-radius: 0.25rem;--bs-list-group-item-padding-x: 1rem;--bs-list-group-item-padding-y: 0.5rem;--bs-list-group-action-color: rgba(52, 58, 64, 0.75);--bs-list-group-action-hover-color: #000;--bs-list-group-action-hover-bg: #f8f9fa;--bs-list-group-action-active-color: #343a40;--bs-list-group-action-active-bg: #e9ecef;--bs-list-group-disabled-color: rgba(52, 58, 64, 0.75);--bs-list-group-disabled-bg: #fff;--bs-list-group-active-color: #fff;--bs-list-group-active-bg: #2780e3;--bs-list-group-active-border-color: #2780e3;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;padding-left:0;margin-bottom:0}.list-group-numbered{list-style-type:none;counter-reset:section}.list-group-numbered>.list-group-item::before{content:counters(section, ".") ". ";counter-increment:section}.list-group-item-action{width:100%;color:var(--bs-list-group-action-color);text-align:inherit}.list-group-item-action:hover,.list-group-item-action:focus{z-index:1;color:var(--bs-list-group-action-hover-color);text-decoration:none;background-color:var(--bs-list-group-action-hover-bg)}.list-group-item-action:active{color:var(--bs-list-group-action-active-color);background-color:var(--bs-list-group-action-active-bg)}.list-group-item{position:relative;display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);color:var(--bs-list-group-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid var(--bs-list-group-border-color)}.list-group-item.disabled,.list-group-item:disabled{color:var(--bs-list-group-disabled-color);pointer-events:none;background-color:var(--bs-list-group-disabled-bg)}.list-group-item.active{z-index:2;color:var(--bs-list-group-active-color);background-color:var(--bs-list-group-active-bg);border-color:var(--bs-list-group-active-border-color)}.list-group-item+.list-group-item{border-top-width:0}.list-group-item+.list-group-item.active{margin-top:calc(-1*var(--bs-list-group-border-width));border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal>.list-group-item.active{margin-top:0}.list-group-horizontal>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}@media(min-width: 576px){.list-group-horizontal-sm{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 768px){.list-group-horizontal-md{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-md>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 992px){.list-group-horizontal-lg{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1200px){.list-group-horizontal-xl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1400px){.list-group-horizontal-xxl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xxl>.list-group-item.active{margin-top:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}.list-group-flush>.list-group-item{border-width:0 0 var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-default{--bs-list-group-color: var(--bs-default-text-emphasis);--bs-list-group-bg: var(--bs-default-bg-subtle);--bs-list-group-border-color: var(--bs-default-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-default-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-default-border-subtle);--bs-list-group-active-color: var(--bs-default-bg-subtle);--bs-list-group-active-bg: var(--bs-default-text-emphasis);--bs-list-group-active-border-color: var(--bs-default-text-emphasis)}.list-group-item-primary{--bs-list-group-color: var(--bs-primary-text-emphasis);--bs-list-group-bg: var(--bs-primary-bg-subtle);--bs-list-group-border-color: var(--bs-primary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-primary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-primary-border-subtle);--bs-list-group-active-color: var(--bs-primary-bg-subtle);--bs-list-group-active-bg: var(--bs-primary-text-emphasis);--bs-list-group-active-border-color: var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color: var(--bs-secondary-text-emphasis);--bs-list-group-bg: var(--bs-secondary-bg-subtle);--bs-list-group-border-color: var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-secondary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-secondary-border-subtle);--bs-list-group-active-color: var(--bs-secondary-bg-subtle);--bs-list-group-active-bg: var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-info-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-info-border-subtle);--bs-list-group-active-color: var(--bs-info-bg-subtle);--bs-list-group-active-bg: var(--bs-info-text-emphasis);--bs-list-group-active-border-color: var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color: var(--bs-warning-text-emphasis);--bs-list-group-bg: var(--bs-warning-bg-subtle);--bs-list-group-border-color: var(--bs-warning-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-warning-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-warning-border-subtle);--bs-list-group-active-color: var(--bs-warning-bg-subtle);--bs-list-group-active-bg: var(--bs-warning-text-emphasis);--bs-list-group-active-border-color: var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-dark-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-dark-border-subtle);--bs-list-group-active-color: var(--bs-dark-bg-subtle);--bs-list-group-active-bg: var(--bs-dark-text-emphasis);--bs-list-group-active-border-color: var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color: #000;--bs-btn-close-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23000'%3e%3cpath d='M.293.293a1 1 0 0 1 1.414 0L8 6.586 14.293.293a1 1 0 1 1 1.414 1.414L9.414 8l6.293 6.293a1 1 0 0 1-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 0 1-1.414-1.414L6.586 8 .293 1.707a1 1 0 0 1 0-1.414z'/%3e%3c/svg%3e");--bs-btn-close-opacity: 0.5;--bs-btn-close-hover-opacity: 0.75;--bs-btn-close-focus-shadow: 0 0 0 0.25rem rgba(39, 128, 227, 0.25);--bs-btn-close-focus-opacity: 1;--bs-btn-close-disabled-opacity: 0.25;--bs-btn-close-white-filter: invert(1) grayscale(100%) brightness(200%);box-sizing:content-box;width:1em;height:1em;padding:.25em .25em;color:var(--bs-btn-close-color);background:rgba(0,0,0,0) var(--bs-btn-close-bg) center/1em auto no-repeat;border:0;opacity:var(--bs-btn-close-opacity)}.btn-close:hover{color:var(--bs-btn-close-color);text-decoration:none;opacity:var(--bs-btn-close-hover-opacity)}.btn-close:focus{outline:0;box-shadow:var(--bs-btn-close-focus-shadow);opacity:var(--bs-btn-close-focus-opacity)}.btn-close:disabled,.btn-close.disabled{pointer-events:none;user-select:none;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;-o-user-select:none;opacity:var(--bs-btn-close-disabled-opacity)}.btn-close-white{filter:var(--bs-btn-close-white-filter)}[data-bs-theme=dark] .btn-close{filter:var(--bs-btn-close-white-filter)}.toast{--bs-toast-zindex: 1090;--bs-toast-padding-x: 0.75rem;--bs-toast-padding-y: 0.5rem;--bs-toast-spacing: 1.5rem;--bs-toast-max-width: 350px;--bs-toast-font-size:0.875rem;--bs-toast-color: ;--bs-toast-bg: rgba(255, 255, 255, 0.85);--bs-toast-border-width: 1px;--bs-toast-border-color: rgba(0, 0, 0, 0.175);--bs-toast-border-radius: 0.25rem;--bs-toast-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15);--bs-toast-header-color: rgba(52, 58, 64, 0.75);--bs-toast-header-bg: rgba(255, 255, 255, 0.85);--bs-toast-header-border-color: rgba(0, 0, 0, 0.175);width:var(--bs-toast-max-width);max-width:100%;font-size:var(--bs-toast-font-size);color:var(--bs-toast-color);pointer-events:auto;background-color:var(--bs-toast-bg);background-clip:padding-box;border:var(--bs-toast-border-width) solid var(--bs-toast-border-color);box-shadow:var(--bs-toast-box-shadow)}.toast.showing{opacity:0}.toast:not(.show){display:none}.toast-container{--bs-toast-zindex: 1090;position:absolute;z-index:var(--bs-toast-zindex);width:max-content;width:-webkit-max-content;width:-moz-max-content;width:-ms-max-content;width:-o-max-content;max-width:100%;pointer-events:none}.toast-container>:not(:last-child){margin-bottom:var(--bs-toast-spacing)}.toast-header{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;padding:var(--bs-toast-padding-y) var(--bs-toast-padding-x);color:var(--bs-toast-header-color);background-color:var(--bs-toast-header-bg);background-clip:padding-box;border-bottom:var(--bs-toast-border-width) solid var(--bs-toast-header-border-color)}.toast-header .btn-close{margin-right:calc(-0.5*var(--bs-toast-padding-x));margin-left:var(--bs-toast-padding-x)}.toast-body{padding:var(--bs-toast-padding-x);word-wrap:break-word}.modal{--bs-modal-zindex: 1055;--bs-modal-width: 500px;--bs-modal-padding: 1rem;--bs-modal-margin: 0.5rem;--bs-modal-color: ;--bs-modal-bg: #fff;--bs-modal-border-color: rgba(0, 0, 0, 0.175);--bs-modal-border-width: 1px;--bs-modal-border-radius: 0.5rem;--bs-modal-box-shadow: 0 0.125rem 0.25rem rgba(0, 0, 0, 0.075);--bs-modal-inner-border-radius: calc(0.5rem - 1px);--bs-modal-header-padding-x: 1rem;--bs-modal-header-padding-y: 1rem;--bs-modal-header-padding: 1rem 1rem;--bs-modal-header-border-color: #dee2e6;--bs-modal-header-border-width: 1px;--bs-modal-title-line-height: 1.5;--bs-modal-footer-gap: 0.5rem;--bs-modal-footer-bg: ;--bs-modal-footer-border-color: #dee2e6;--bs-modal-footer-border-width: 1px;position:fixed;top:0;left:0;z-index:var(--bs-modal-zindex);display:none;width:100%;height:100%;overflow-x:hidden;overflow-y:auto;outline:0}.modal-dialog{position:relative;width:auto;margin:var(--bs-modal-margin);pointer-events:none}.modal.fade .modal-dialog{transition:transform .3s ease-out;transform:translate(0, -50px)}@media(prefers-reduced-motion: reduce){.modal.fade .modal-dialog{transition:none}}.modal.show .modal-dialog{transform:none}.modal.modal-static .modal-dialog{transform:scale(1.02)}.modal-dialog-scrollable{height:calc(100% - var(--bs-modal-margin)*2)}.modal-dialog-scrollable .modal-content{max-height:100%;overflow:hidden}.modal-dialog-scrollable .modal-body{overflow-y:auto}.modal-dialog-centered{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;min-height:calc(100% - var(--bs-modal-margin)*2)}.modal-content{position:relative;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;width:100%;color:var(--bs-modal-color);pointer-events:auto;background-color:var(--bs-modal-bg);background-clip:padding-box;border:var(--bs-modal-border-width) solid var(--bs-modal-border-color);outline:0}.modal-backdrop{--bs-backdrop-zindex: 1050;--bs-backdrop-bg: #000;--bs-backdrop-opacity: 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!important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand .navbar-toggler{display:none}.navbar-expand .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand .offcanvas .offcanvas-header{display:none}.navbar-expand .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}.navbar-dark,.navbar[data-bs-theme=dark]{--bs-navbar-color: #dee2e6;--bs-navbar-hover-color: rgba(255, 255, 255, 0.8);--bs-navbar-disabled-color: rgba(222, 226, 230, 0.75);--bs-navbar-active-color: #fff;--bs-navbar-brand-color: #dee2e6;--bs-navbar-brand-hover-color: #fff;--bs-navbar-toggler-border-color: rgba(222, 226, 230, 0);--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='%23dee2e6' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e")}[data-bs-theme=dark] .navbar-toggler-icon{--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='%23dee2e6' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e")}.card{--bs-card-spacer-y: 1rem;--bs-card-spacer-x: 1rem;--bs-card-title-spacer-y: 0.5rem;--bs-card-title-color: ;--bs-card-subtitle-color: ;--bs-card-border-width: 1px;--bs-card-border-color: rgba(0, 0, 0, 0.175);--bs-card-border-radius: 0.25rem;--bs-card-box-shadow: ;--bs-card-inner-border-radius: calc(0.25rem - 1px);--bs-card-cap-padding-y: 0.5rem;--bs-card-cap-padding-x: 1rem;--bs-card-cap-bg: rgba(52, 58, 64, 0.25);--bs-card-cap-color: ;--bs-card-height: ;--bs-card-color: ;--bs-card-bg: #2d2d2d;--bs-card-img-overlay-padding: 1rem;--bs-card-group-margin: 0.75rem;position:relative;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;min-width:0;height:var(--bs-card-height);color:var(--bs-body-color);word-wrap:break-word;background-color:var(--bs-card-bg);background-clip:border-box;border:var(--bs-card-border-width) solid var(--bs-card-border-color);border-radius:var(--bs-card-border-radius)}.card>hr{margin-right:0;margin-left:0}.card>.list-group{border-top:inherit;border-bottom:inherit}.card>.list-group:first-child{border-top-width:0;border-top-left-radius:var(--bs-card-inner-border-radius);border-top-right-radius:var(--bs-card-inner-border-radius)}.card>.list-group:last-child{border-bottom-width:0;border-bottom-right-radius:var(--bs-card-inner-border-radius);border-bottom-left-radius:var(--bs-card-inner-border-radius)}.card>.card-header+.list-group,.card>.list-group+.card-footer{border-top:0}.card-body{flex:1 1 auto;-webkit-flex:1 1 auto;padding:var(--bs-card-spacer-y) var(--bs-card-spacer-x);color:var(--bs-card-color)}.card-title{margin-bottom:var(--bs-card-title-spacer-y);color:var(--bs-card-title-color)}.card-subtitle{margin-top:calc(-0.5*var(--bs-card-title-spacer-y));margin-bottom:0;color:var(--bs-card-subtitle-color)}.card-text:last-child{margin-bottom:0}.card-link+.card-link{margin-left:var(--bs-card-spacer-x)}.card-header{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);margin-bottom:0;color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-bottom:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-header:first-child{border-radius:var(--bs-card-inner-border-radius) var(--bs-card-inner-border-radius) 0 0}.card-footer{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-top:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-footer:last-child{border-radius:0 0 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.nav-link.active{background-color:var(--bs-card-bg);border-bottom-color:var(--bs-card-bg)}.card-header-pills{margin-right:calc(-0.5*var(--bs-card-cap-padding-x));margin-left:calc(-0.5*var(--bs-card-cap-padding-x))}.card-img-overlay{position:absolute;top:0;right:0;bottom:0;left:0;padding:var(--bs-card-img-overlay-padding);border-radius:var(--bs-card-inner-border-radius)}.card-img,.card-img-top,.card-img-bottom{width:100%}.card-img,.card-img-top{border-top-left-radius:var(--bs-card-inner-border-radius);border-top-right-radius:var(--bs-card-inner-border-radius)}.card-img,.card-img-bottom{border-bottom-right-radius:var(--bs-card-inner-border-radius);border-bottom-left-radius:var(--bs-card-inner-border-radius)}.card-group>.card{margin-bottom:var(--bs-card-group-margin)}@media(min-width: 576px){.card-group{display:flex;display:-webkit-flex;flex-flow:row wrap;-webkit-flex-flow:row wrap}.card-group>.card{flex:1 0 0%;-webkit-flex:1 0 0%;margin-bottom:0}.card-group>.card+.card{margin-left:0;border-left:0}.card-group>.card:not(:last-child){border-top-right-radius:0;border-bottom-right-radius:0}.card-group>.card:not(:last-child) .card-img-top,.card-group>.card:not(:last-child) .card-header{border-top-right-radius:0}.card-group>.card:not(:last-child) .card-img-bottom,.card-group>.card:not(:last-child) .card-footer{border-bottom-right-radius:0}.card-group>.card:not(:first-child){border-top-left-radius:0;border-bottom-left-radius:0}.card-group>.card:not(:first-child) .card-img-top,.card-group>.card:not(:first-child) .card-header{border-top-left-radius:0}.card-group>.card:not(:first-child) .card-img-bottom,.card-group>.card:not(:first-child) .card-footer{border-bottom-left-radius:0}}.accordion{--bs-accordion-color: #fff;--bs-accordion-bg: #222;--bs-accordion-transition: color 0.15s ease-in-out, background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out, border-radius 0.15s ease;--bs-accordion-border-color: #dee2e6;--bs-accordion-border-width: 1px;--bs-accordion-border-radius: 0.25rem;--bs-accordion-inner-border-radius: calc(0.25rem - 1px);--bs-accordion-btn-padding-x: 1.25rem;--bs-accordion-btn-padding-y: 1rem;--bs-accordion-btn-color: #fff;--bs-accordion-btn-bg: #222;--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23fff'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-icon-width: 1.25rem;--bs-accordion-btn-icon-transform: rotate(-180deg);--bs-accordion-btn-icon-transition: transform 0.2s ease-in-out;--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23162433'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-focus-border-color: #9badbf;--bs-accordion-btn-focus-box-shadow: 0 0 0 0.25rem rgba(55, 90, 127, 0.25);--bs-accordion-body-padding-x: 1.25rem;--bs-accordion-body-padding-y: 1rem;--bs-accordion-active-color: #162433;--bs-accordion-active-bg: #d7dee5}.accordion-button{position:relative;display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;width:100%;padding:var(--bs-accordion-btn-padding-y) var(--bs-accordion-btn-padding-x);font-size:1rem;color:var(--bs-accordion-btn-color);text-align:left;background-color:var(--bs-accordion-btn-bg);border:0;border-radius:0;overflow-anchor:none;transition:var(--bs-accordion-transition)}@media(prefers-reduced-motion: reduce){.accordion-button{transition:none}}.accordion-button:not(.collapsed){color:var(--bs-accordion-active-color);background-color:var(--bs-accordion-active-bg);box-shadow:inset 0 calc(-1*var(--bs-accordion-border-width)) 0 var(--bs-accordion-border-color)}.accordion-button:not(.collapsed)::after{background-image:var(--bs-accordion-btn-active-icon);transform:var(--bs-accordion-btn-icon-transform)}.accordion-button::after{flex-shrink:0;-webkit-flex-shrink:0;width:var(--bs-accordion-btn-icon-width);height:var(--bs-accordion-btn-icon-width);margin-left:auto;content:"";background-image:var(--bs-accordion-btn-icon);background-repeat:no-repeat;background-size:var(--bs-accordion-btn-icon-width);transition:var(--bs-accordion-btn-icon-transition)}@media(prefers-reduced-motion: reduce){.accordion-button::after{transition:none}}.accordion-button:hover{z-index:2}.accordion-button:focus{z-index:3;border-color:var(--bs-accordion-btn-focus-border-color);outline:0;box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.accordion-header{margin-bottom:0}.accordion-item{color:var(--bs-accordion-color);background-color:var(--bs-accordion-bg);border:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.accordion-item:first-of-type{border-top-left-radius:var(--bs-accordion-border-radius);border-top-right-radius:var(--bs-accordion-border-radius)}.accordion-item:first-of-type .accordion-button{border-top-left-radius:var(--bs-accordion-inner-border-radius);border-top-right-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:not(:first-of-type){border-top:0}.accordion-item:last-of-type{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-item:last-of-type .accordion-button.collapsed{border-bottom-right-radius:var(--bs-accordion-inner-border-radius);border-bottom-left-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:last-of-type .accordion-collapse{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0;border-radius:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}.accordion-flush .accordion-item .accordion-button,.accordion-flush .accordion-item .accordion-button.collapsed{border-radius:0}[data-bs-theme=dark] .accordion-button::after{--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23879cb2'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23879cb2'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e")}.breadcrumb{--bs-breadcrumb-padding-x: 0.75rem;--bs-breadcrumb-padding-y: 0.375rem;--bs-breadcrumb-margin-bottom: 1rem;--bs-breadcrumb-bg: #434343;--bs-breadcrumb-border-radius: 0.25rem;--bs-breadcrumb-divider-color: rgba(255, 255, 255, 0.75);--bs-breadcrumb-item-padding-x: 0.5rem;--bs-breadcrumb-item-active-color: rgba(255, 255, 255, 0.75);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding:var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg);border-radius:var(--bs-breadcrumb-border-radius)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, ">") /* rtl: var(--bs-breadcrumb-divider, ">") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: 0.75rem;--bs-pagination-padding-y: 0.375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: #fff;--bs-pagination-bg: #00bc8c;--bs-pagination-border-width: 0;--bs-pagination-border-color: transparent;--bs-pagination-border-radius: 0.25rem;--bs-pagination-hover-color: #fff;--bs-pagination-hover-bg: #00efb2;--bs-pagination-hover-border-color: transparent;--bs-pagination-focus-color: #009670;--bs-pagination-focus-bg: #ebebeb;--bs-pagination-focus-box-shadow: 0 0 0 0.25rem rgba(55, 90, 127, 0.25);--bs-pagination-active-color: #fff;--bs-pagination-active-bg: #00efb2;--bs-pagination-active-border-color: transparent;--bs-pagination-disabled-color: #fff;--bs-pagination-disabled-bg: #007053;--bs-pagination-disabled-border-color: transparent;display:flex;display:-webkit-flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out,box-shadow .15s ease-in-out}@media(prefers-reduced-motion: reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.page-link.active,.active>.page-link{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.page-link.disabled,.disabled>.page-link{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(0*-1)}.page-item:first-child .page-link{border-top-left-radius:var(--bs-pagination-border-radius);border-bottom-left-radius:var(--bs-pagination-border-radius)}.page-item:last-child .page-link{border-top-right-radius:var(--bs-pagination-border-radius);border-bottom-right-radius:var(--bs-pagination-border-radius)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: 0.75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: 0.5rem}.pagination-sm{--bs-pagination-padding-x: 0.5rem;--bs-pagination-padding-y: 0.25rem;--bs-pagination-font-size:0.875rem;--bs-pagination-border-radius: 0.2em}.badge{--bs-badge-padding-x: 0.65em;--bs-badge-padding-y: 0.35em;--bs-badge-font-size:0.75em;--bs-badge-font-weight: 700;--bs-badge-color: #fff;--bs-badge-border-radius: 0.25rem;display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline;border-radius:var(--bs-badge-border-radius)}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: 1px solid var(--bs-alert-border-color);--bs-alert-border-radius: 0.25rem;--bs-alert-link-color: inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border);border-radius:var(--bs-alert-border-radius)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-default{--bs-alert-color: var(--bs-default-text-emphasis);--bs-alert-bg: var(--bs-default-bg-subtle);--bs-alert-border-color: var(--bs-default-border-subtle);--bs-alert-link-color: var(--bs-default-text-emphasis)}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:1rem}}.progress,.progress-stacked{--bs-progress-height: 1rem;--bs-progress-font-size:0.75rem;--bs-progress-bg: #434343;--bs-progress-border-radius: 0.25rem;--bs-progress-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.075);--bs-progress-bar-color: #fff;--bs-progress-bar-bg: #375a7f;--bs-progress-bar-transition: width 0.6s ease;display:flex;display:-webkit-flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg);border-radius:var(--bs-progress-border-radius)}.progress-bar{display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;justify-content:center;-webkit-justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media(prefers-reduced-motion: reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media(prefers-reduced-motion: reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color: #fff;--bs-list-group-bg: #2d2d2d;--bs-list-group-border-color: #434343;--bs-list-group-border-width: 1px;--bs-list-group-border-radius: 0.25rem;--bs-list-group-item-padding-x: 1rem;--bs-list-group-item-padding-y: 0.5rem;--bs-list-group-action-color: rgba(255, 255, 255, 0.75);--bs-list-group-action-hover-color: #fff;--bs-list-group-action-hover-bg: #434343;--bs-list-group-action-active-color: #fff;--bs-list-group-action-active-bg: #222;--bs-list-group-disabled-color: rgba(255, 255, 255, 0.75);--bs-list-group-disabled-bg: #2d2d2d;--bs-list-group-active-color: #fff;--bs-list-group-active-bg: #375a7f;--bs-list-group-active-border-color: 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#6c757d;--quarto-scss-export-gray-800: #343a40;--quarto-scss-export-card-cap-bg: rgba(52, 58, 64, 0.25);--quarto-scss-export-border-color: #dee2e6;--quarto-scss-export-text-muted: #6c757d;--quarto-scss-export-white: #fff;--quarto-scss-export-gray-100: #f8f9fa;--quarto-scss-export-gray-200: #ebebeb;--quarto-scss-export-gray-400: #ced4da;--quarto-scss-export-gray-700: #444;--quarto-scss-export-gray-900: #222;--quarto-scss-export-black: #000;--quarto-scss-export-blue: #375a7f;--quarto-scss-export-indigo: #6610f2;--quarto-scss-export-purple: #6f42c1;--quarto-scss-export-pink: #e83e8c;--quarto-scss-export-red: #e74c3c;--quarto-scss-export-orange: #fd7e14;--quarto-scss-export-yellow: #f39c12;--quarto-scss-export-green: #00bc8c;--quarto-scss-export-teal: #20c997;--quarto-scss-export-cyan: #3498db;--quarto-scss-export-body-bg: #222;--quarto-scss-export-body-color: #fff;--quarto-scss-export-primary: #375a7f;--quarto-scss-export-success: #00bc8c;--quarto-scss-export-info: #3498db;--quarto-scss-export-warning: #f39c12;--quarto-scss-export-danger: #e74c3c;--quarto-scss-export-link-color: #00bc8c;--quarto-scss-export-input-bg: #fff;--quarto-scss-export-input-border-color: #adb5bd;--quarto-scss-export-form-check-input-bg: #fff;--quarto-scss-export-form-file-button-hover-bg: #363636;--quarto-scss-export-dropdown-bg: #222;--quarto-scss-export-dropdown-link-color: #fff;--quarto-scss-export-dropdown-link-hover-color: #fff;--quarto-scss-export-dropdown-link-hover-bg: #375a7f;--quarto-scss-export-nav-tabs-link-active-color: #fff;--quarto-scss-export-pagination-color: #fff;--quarto-scss-export-pagination-bg: #00bc8c;--quarto-scss-export-pagination-border-color: transparent;--quarto-scss-export-pagination-hover-color: #fff;--quarto-scss-export-pagination-hover-bg: #00efb2;--quarto-scss-export-pagination-hover-border-color: transparent;--quarto-scss-export-pagination-active-bg: #00efb2;--quarto-scss-export-pagination-active-border-color: transparent;--quarto-scss-export-pagination-disabled-color: #fff;--quarto-scss-export-pagination-disabled-bg: #007053;--quarto-scss-export-pagination-disabled-border-color: transparent;--quarto-scss-export-list-group-color: #fff;--quarto-scss-export-list-group-action-hover-color: #fff;--quarto-scss-export-list-group-action-active-bg: #222;--quarto-scss-export-btn-close-color: #fff;--quarto-scss-export-title-banner-color: ;--quarto-scss-export-title-banner-bg: ;--quarto-scss-export-btn-code-copy-color: #f8f8f2;--quarto-scss-export-btn-code-copy-color-active: #ffa07a;--quarto-scss-export-sidebar-bg: #222;--quarto-scss-export-link-color-bg: transparent;--quarto-scss-export-code-color: #7d12ba;--quarto-scss-export-code-bg: #f8f9fa;--quarto-scss-export-toc-color: #00bc8c;--quarto-scss-export-toc-active-border: #00bc8c;--quarto-scss-export-toc-inactive-border: #ebebeb;--quarto-scss-export-navbar-default: #375a7f;--quarto-scss-export-navbar-hl-override: false;--quarto-scss-export-navbar-bg: #375a7f;--quarto-scss-export-btn-bg: #434343;--quarto-scss-export-btn-fg: #d9d9d9;--quarto-scss-export-body-contrast-bg: #222;--quarto-scss-export-body-contrast-color: #fff;--quarto-scss-export-navbar-fg: #dee2e6;--quarto-scss-export-navbar-hl: #fff;--quarto-scss-export-navbar-brand: #dee2e6;--quarto-scss-export-navbar-brand-hl: #fff;--quarto-scss-export-navbar-toggler-border-color: rgba(222, 226, 230, 0);--quarto-scss-export-navbar-hover-color: rgba(255, 255, 255, 0.8);--quarto-scss-export-navbar-disabled-color: rgba(222, 226, 230, 0.75);--quarto-scss-export-sidebar-fg: #adadad;--quarto-scss-export-title-block-color: #fff;--quarto-scss-export-title-block-contast-color: #222;--quarto-scss-export-footer-bg: #222;--quarto-scss-export-footer-fg: #8a8a8a;--quarto-scss-export-code-annotation-higlight-color: rgba(170, 170, 170, 0.2666666667);--quarto-scss-export-code-annotation-higlight-bg: rgba(170, 170, 170, 0.1333333333);--quarto-scss-export-table-group-separator-color: #919191;--quarto-scss-export-table-group-separator-color-lighter: #4e4e4e;--quarto-scss-export-link-decoration: underline;--quarto-scss-export-sidebar-glass-bg: rgba(102, 102, 102, 0.4);--quarto-scss-export-color-contrast-dark: #000;--quarto-scss-export-color-contrast-light: #fff;--quarto-scss-export-blue-100: #d7dee5;--quarto-scss-export-blue-200: #afbdcc;--quarto-scss-export-blue-300: #879cb2;--quarto-scss-export-blue-400: #5f7b99;--quarto-scss-export-blue-500: #375a7f;--quarto-scss-export-blue-600: #2c4866;--quarto-scss-export-blue-700: #21364c;--quarto-scss-export-blue-800: #162433;--quarto-scss-export-blue-900: #0b1219;--quarto-scss-export-indigo-100: #e0cffc;--quarto-scss-export-indigo-200: #c29ffa;--quarto-scss-export-indigo-300: #a370f7;--quarto-scss-export-indigo-400: #8540f5;--quarto-scss-export-indigo-500: #6610f2;--quarto-scss-export-indigo-600: #520dc2;--quarto-scss-export-indigo-700: #3d0a91;--quarto-scss-export-indigo-800: #290661;--quarto-scss-export-indigo-900: #140330;--quarto-scss-export-purple-100: #e2d9f3;--quarto-scss-export-purple-200: #c5b3e6;--quarto-scss-export-purple-300: #a98eda;--quarto-scss-export-purple-400: #8c68cd;--quarto-scss-export-purple-500: #6f42c1;--quarto-scss-export-purple-600: #59359a;--quarto-scss-export-purple-700: #432874;--quarto-scss-export-purple-800: #2c1a4d;--quarto-scss-export-purple-900: #160d27;--quarto-scss-export-pink-100: #fad8e8;--quarto-scss-export-pink-200: #f6b2d1;--quarto-scss-export-pink-300: #f18bba;--quarto-scss-export-pink-400: #ed65a3;--quarto-scss-export-pink-500: #e83e8c;--quarto-scss-export-pink-600: #ba3270;--quarto-scss-export-pink-700: #8b2554;--quarto-scss-export-pink-800: #5d1938;--quarto-scss-export-pink-900: #2e0c1c;--quarto-scss-export-red-100: #fadbd8;--quarto-scss-export-red-200: #f5b7b1;--quarto-scss-export-red-300: #f1948a;--quarto-scss-export-red-400: #ec7063;--quarto-scss-export-red-500: #e74c3c;--quarto-scss-export-red-600: #b93d30;--quarto-scss-export-red-700: #8b2e24;--quarto-scss-export-red-800: #5c1e18;--quarto-scss-export-red-900: #2e0f0c;--quarto-scss-export-orange-100: #ffe5d0;--quarto-scss-export-orange-200: #fecba1;--quarto-scss-export-orange-300: #feb272;--quarto-scss-export-orange-400: #fd9843;--quarto-scss-export-orange-500: #fd7e14;--quarto-scss-export-orange-600: #ca6510;--quarto-scss-export-orange-700: #984c0c;--quarto-scss-export-orange-800: #653208;--quarto-scss-export-orange-900: #331904;--quarto-scss-export-yellow-100: #fdebd0;--quarto-scss-export-yellow-200: #fad7a0;--quarto-scss-export-yellow-300: #f8c471;--quarto-scss-export-yellow-400: #f5b041;--quarto-scss-export-yellow-500: #f39c12;--quarto-scss-export-yellow-600: #c27d0e;--quarto-scss-export-yellow-700: #925e0b;--quarto-scss-export-yellow-800: #613e07;--quarto-scss-export-yellow-900: #311f04;--quarto-scss-export-green-100: #ccf2e8;--quarto-scss-export-green-200: #99e4d1;--quarto-scss-export-green-300: #66d7ba;--quarto-scss-export-green-400: #33c9a3;--quarto-scss-export-green-500: #00bc8c;--quarto-scss-export-green-600: #009670;--quarto-scss-export-green-700: #007154;--quarto-scss-export-green-800: #004b38;--quarto-scss-export-green-900: #00261c;--quarto-scss-export-teal-100: #d2f4ea;--quarto-scss-export-teal-200: #a6e9d5;--quarto-scss-export-teal-300: #79dfc1;--quarto-scss-export-teal-400: #4dd4ac;--quarto-scss-export-teal-500: #20c997;--quarto-scss-export-teal-600: #1aa179;--quarto-scss-export-teal-700: #13795b;--quarto-scss-export-teal-800: #0d503c;--quarto-scss-export-teal-900: #06281e;--quarto-scss-export-cyan-100: #d6eaf8;--quarto-scss-export-cyan-200: #aed6f1;--quarto-scss-export-cyan-300: #85c1e9;--quarto-scss-export-cyan-400: #5dade2;--quarto-scss-export-cyan-500: #3498db;--quarto-scss-export-cyan-600: #2a7aaf;--quarto-scss-export-cyan-700: #1f5b83;--quarto-scss-export-cyan-800: #153d58;--quarto-scss-export-cyan-900: #0a1e2c;--quarto-scss-export-default: #434343;--quarto-scss-export-primary-text-emphasis: #162433;--quarto-scss-export-secondary-text-emphasis: #1b1b1b;--quarto-scss-export-success-text-emphasis: #004b38;--quarto-scss-export-info-text-emphasis: #153d58;--quarto-scss-export-warning-text-emphasis: #613e07;--quarto-scss-export-danger-text-emphasis: #5c1e18;--quarto-scss-export-light-text-emphasis: #444;--quarto-scss-export-dark-text-emphasis: #444;--quarto-scss-export-primary-bg-subtle: #d7dee5;--quarto-scss-export-secondary-bg-subtle: #d9d9d9;--quarto-scss-export-success-bg-subtle: #ccf2e8;--quarto-scss-export-info-bg-subtle: #d6eaf8;--quarto-scss-export-warning-bg-subtle: #fdebd0;--quarto-scss-export-danger-bg-subtle: #fadbd8;--quarto-scss-export-light-bg-subtle: #fcfcfd;--quarto-scss-export-dark-bg-subtle: #ced4da;--quarto-scss-export-primary-border-subtle: #afbdcc;--quarto-scss-export-secondary-border-subtle: #b4b4b4;--quarto-scss-export-success-border-subtle: #99e4d1;--quarto-scss-export-info-border-subtle: #aed6f1;--quarto-scss-export-warning-border-subtle: #fad7a0;--quarto-scss-export-danger-border-subtle: #f5b7b1;--quarto-scss-export-light-border-subtle: #ebebeb;--quarto-scss-export-dark-border-subtle: #adb5bd;--quarto-scss-export-body-text-align: ;--quarto-scss-export-body-secondary-color: rgba(255, 255, 255, 0.75);--quarto-scss-export-body-secondary-bg: #ebebeb;--quarto-scss-export-body-tertiary-color: rgba(255, 255, 255, 0.5);--quarto-scss-export-body-tertiary-bg: #f8f9fa;--quarto-scss-export-body-emphasis-color: #000;--quarto-scss-export-link-hover-color: #009670;--quarto-scss-export-link-hover-decoration: ;--quarto-scss-export-border-color-translucent: rgba(0, 0, 0, 0.175);--quarto-scss-export-component-active-bg: #375a7f;--quarto-scss-export-component-active-color: #fff;--quarto-scss-export-focus-ring-color: rgba(55, 90, 127, 0.25);--quarto-scss-export-headings-font-family: ;--quarto-scss-export-headings-font-style: ;--quarto-scss-export-display-font-family: ;--quarto-scss-export-display-font-style: ;--quarto-scss-export-blockquote-footer-color: #6c757d;--quarto-scss-export-blockquote-border-color: #ebebeb;--quarto-scss-export-hr-bg-color: ;--quarto-scss-export-hr-height: ;--quarto-scss-export-hr-border-color: ;--quarto-scss-export-legend-font-weight: ;--quarto-scss-export-mark-bg: #fdebd0;--quarto-scss-export-table-color: #fff;--quarto-scss-export-table-bg: #222;--quarto-scss-export-table-accent-bg: transparent;--quarto-scss-export-table-th-font-weight: ;--quarto-scss-export-table-striped-color: #fff;--quarto-scss-export-table-striped-bg: rgba(0, 0, 0, 0.05);--quarto-scss-export-table-active-color: #fff;--quarto-scss-export-table-active-bg: rgba(0, 0, 0, 0.1);--quarto-scss-export-table-hover-color: #fff;--quarto-scss-export-table-hover-bg: rgba(0, 0, 0, 0.075);--quarto-scss-export-table-caption-color: rgba(255, 255, 255, 0.75);--quarto-scss-export-input-btn-font-family: ;--quarto-scss-export-input-btn-focus-color: rgba(55, 90, 127, 0.25);--quarto-scss-export-btn-color: #fff;--quarto-scss-export-btn-font-family: ;--quarto-scss-export-btn-white-space: ;--quarto-scss-export-btn-link-color: #00bc8c;--quarto-scss-export-btn-link-hover-color: #009670;--quarto-scss-export-btn-link-disabled-color: #6c757d;--quarto-scss-export-form-text-font-style: ;--quarto-scss-export-form-text-font-weight: ;--quarto-scss-export-form-text-color: rgba(255, 255, 255, 0.75);--quarto-scss-export-form-label-font-size: ;--quarto-scss-export-form-label-font-style: ;--quarto-scss-export-form-label-font-weight: ;--quarto-scss-export-form-label-color: ;--quarto-scss-export-input-font-family: ;--quarto-scss-export-input-disabled-color: ;--quarto-scss-export-input-disabled-bg: #ebebeb;--quarto-scss-export-input-disabled-border-color: ;--quarto-scss-export-input-focus-bg: #fff;--quarto-scss-export-input-focus-border-color: #9badbf;--quarto-scss-export-input-plaintext-color: #fff;--quarto-scss-export-form-check-label-color: ;--quarto-scss-export-form-check-transition: ;--quarto-scss-export-form-check-input-focus-border: #9badbf;--quarto-scss-export-form-check-input-checked-color: #fff;--quarto-scss-export-form-check-input-checked-bg-color: #375a7f;--quarto-scss-export-form-check-input-checked-border-color: #375a7f;--quarto-scss-export-form-check-input-indeterminate-color: #fff;--quarto-scss-export-form-check-input-indeterminate-bg-color: #375a7f;--quarto-scss-export-form-check-input-indeterminate-border-color: #375a7f;--quarto-scss-export-form-switch-color: rgba(0, 0, 0, 0.25);--quarto-scss-export-form-switch-focus-color: #9badbf;--quarto-scss-export-form-switch-checked-color: #fff;--quarto-scss-export-input-group-addon-border-color: #adb5bd;--quarto-scss-export-form-select-font-family: ;--quarto-scss-export-form-select-bg: #fff;--quarto-scss-export-form-select-disabled-bg: #ebebeb;--quarto-scss-export-form-select-disabled-border-color: ;--quarto-scss-export-form-select-indicator-color: #343a40;--quarto-scss-export-form-select-border-color: #adb5bd;--quarto-scss-export-form-select-focus-border-color: #9badbf;--quarto-scss-export-form-range-track-bg: #f8f9fa;--quarto-scss-export-form-range-thumb-bg: #375a7f;--quarto-scss-export-form-range-thumb-active-bg: #c3ced9;--quarto-scss-export-form-range-thumb-disabled-bg: rgba(255, 255, 255, 0.75);--quarto-scss-export-form-floating-label-disabled-color: #6c757d;--quarto-scss-export-form-feedback-font-style: ;--quarto-scss-export-form-feedback-valid-color: #00bc8c;--quarto-scss-export-form-feedback-invalid-color: #e74c3c;--quarto-scss-export-form-feedback-icon-valid-color: #00bc8c;--quarto-scss-export-form-feedback-icon-invalid-color: #e74c3c;--quarto-scss-export-form-valid-color: #00bc8c;--quarto-scss-export-form-valid-border-color: #00bc8c;--quarto-scss-export-form-invalid-color: #e74c3c;--quarto-scss-export-form-invalid-border-color: #e74c3c;--quarto-scss-export-nav-link-font-size: ;--quarto-scss-export-nav-link-font-weight: ;--quarto-scss-export-nav-link-color: #00bc8c;--quarto-scss-export-nav-link-hover-color: #009670;--quarto-scss-export-nav-tabs-link-active-bg: #222;--quarto-scss-export-nav-pills-link-active-bg: #375a7f;--quarto-scss-export-nav-pills-link-active-color: #fff;--quarto-scss-export-nav-underline-link-active-color: #000;--quarto-scss-export-navbar-padding-x: ;--quarto-scss-export-navbar-light-contrast: #fff;--quarto-scss-export-navbar-dark-contrast: #fff;--quarto-scss-export-navbar-light-icon-color: rgba(255, 255, 255, 0.75);--quarto-scss-export-navbar-dark-icon-color: rgba(255, 255, 255, 0.75);--quarto-scss-export-dropdown-color: #fff;--quarto-scss-export-dropdown-link-active-bg: #375a7f;--quarto-scss-export-dropdown-link-active-color: #fff;--quarto-scss-export-dropdown-link-disabled-color: rgba(255, 255, 255, 0.5);--quarto-scss-export-dropdown-header-color: #6c757d;--quarto-scss-export-dropdown-dark-color: #dee2e6;--quarto-scss-export-dropdown-dark-bg: #343a40;--quarto-scss-export-dropdown-dark-box-shadow: 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.offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-xxl .offcanvas .offcanvas-header{display:none}.navbar-expand-xxl .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}.navbar-expand{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand .navbar-nav .dropdown-menu{position:absolute}.navbar-expand .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand .navbar-nav-scroll{overflow:visible}.navbar-expand .navbar-collapse{display:flex !important;display:-webkit-flex 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30'%3e%3cpath stroke='%23dee2e6' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e")}[data-bs-theme=dark] .navbar-toggler-icon{--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='%23dee2e6' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e")}.card{--bs-card-spacer-y: 1rem;--bs-card-spacer-x: 1rem;--bs-card-title-spacer-y: 0.5rem;--bs-card-title-color: ;--bs-card-subtitle-color: ;--bs-card-border-width: 1px;--bs-card-border-color: rgba(0, 0, 0, 0.175);--bs-card-border-radius: 0.25rem;--bs-card-box-shadow: ;--bs-card-inner-border-radius: calc(0.25rem - 1px);--bs-card-cap-padding-y: 0.5rem;--bs-card-cap-padding-x: 1rem;--bs-card-cap-bg: rgba(52, 58, 64, 0.25);--bs-card-cap-color: ;--bs-card-height: ;--bs-card-color: ;--bs-card-bg: #2d2d2d;--bs-card-img-overlay-padding: 1rem;--bs-card-group-margin: 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var(--bs-card-spacer-x);color:var(--bs-card-color)}.card-title{margin-bottom:var(--bs-card-title-spacer-y);color:var(--bs-card-title-color)}.card-subtitle{margin-top:calc(-0.5*var(--bs-card-title-spacer-y));margin-bottom:0;color:var(--bs-card-subtitle-color)}.card-text:last-child{margin-bottom:0}.card-link+.card-link{margin-left:var(--bs-card-spacer-x)}.card-header{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);margin-bottom:0;color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-bottom:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-header:first-child{border-radius:var(--bs-card-inner-border-radius) var(--bs-card-inner-border-radius) 0 0}.card-footer{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-top:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-footer:last-child{border-radius:0 0 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.nav-link.active{background-color:var(--bs-card-bg);border-bottom-color:var(--bs-card-bg)}.card-header-pills{margin-right:calc(-0.5*var(--bs-card-cap-padding-x));margin-left:calc(-0.5*var(--bs-card-cap-padding-x))}.card-img-overlay{position:absolute;top:0;right:0;bottom:0;left:0;padding:var(--bs-card-img-overlay-padding);border-radius:var(--bs-card-inner-border-radius)}.card-img,.card-img-top,.card-img-bottom{width:100%}.card-img,.card-img-top{border-top-left-radius:var(--bs-card-inner-border-radius);border-top-right-radius:var(--bs-card-inner-border-radius)}.card-img,.card-img-bottom{border-bottom-right-radius:var(--bs-card-inner-border-radius);border-bottom-left-radius:var(--bs-card-inner-border-radius)}.card-group>.card{margin-bottom:var(--bs-card-group-margin)}@media(min-width: 576px){.card-group{display:flex;display:-webkit-flex;flex-flow:row wrap;-webkit-flex-flow:row wrap}.card-group>.card{flex:1 0 0%;-webkit-flex:1 0 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0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-focus-border-color: #9badbf;--bs-accordion-btn-focus-box-shadow: 0 0 0 0.25rem rgba(55, 90, 127, 0.25);--bs-accordion-body-padding-x: 1.25rem;--bs-accordion-body-padding-y: 1rem;--bs-accordion-active-color: #162433;--bs-accordion-active-bg: #d7dee5}.accordion-button{position:relative;display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;width:100%;padding:var(--bs-accordion-btn-padding-y) var(--bs-accordion-btn-padding-x);font-size:1rem;color:var(--bs-accordion-btn-color);text-align:left;background-color:var(--bs-accordion-btn-bg);border:0;border-radius:0;overflow-anchor:none;transition:var(--bs-accordion-transition)}@media(prefers-reduced-motion: reduce){.accordion-button{transition:none}}.accordion-button:not(.collapsed){color:var(--bs-accordion-active-color);background-color:var(--bs-accordion-active-bg);box-shadow:inset 0 calc(-1*var(--bs-accordion-border-width)) 0 var(--bs-accordion-border-color)}.accordion-button:not(.collapsed)::after{background-image:var(--bs-accordion-btn-active-icon);transform:var(--bs-accordion-btn-icon-transform)}.accordion-button::after{flex-shrink:0;-webkit-flex-shrink:0;width:var(--bs-accordion-btn-icon-width);height:var(--bs-accordion-btn-icon-width);margin-left:auto;content:"";background-image:var(--bs-accordion-btn-icon);background-repeat:no-repeat;background-size:var(--bs-accordion-btn-icon-width);transition:var(--bs-accordion-btn-icon-transition)}@media(prefers-reduced-motion: reduce){.accordion-button::after{transition:none}}.accordion-button:hover{z-index:2}.accordion-button:focus{z-index:3;border-color:var(--bs-accordion-btn-focus-border-color);outline:0;box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.accordion-header{margin-bottom:0}.accordion-item{color:var(--bs-accordion-color);background-color:var(--bs-accordion-bg);border:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.accordion-item:first-of-type{border-top-left-radius:var(--bs-accordion-border-radius);border-top-right-radius:var(--bs-accordion-border-radius)}.accordion-item:first-of-type .accordion-button{border-top-left-radius:var(--bs-accordion-inner-border-radius);border-top-right-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:not(:first-of-type){border-top:0}.accordion-item:last-of-type{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-item:last-of-type .accordion-button.collapsed{border-bottom-right-radius:var(--bs-accordion-inner-border-radius);border-bottom-left-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:last-of-type .accordion-collapse{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0;border-radius:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}.accordion-flush .accordion-item .accordion-button,.accordion-flush .accordion-item .accordion-button.collapsed{border-radius:0}[data-bs-theme=dark] .accordion-button::after{--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23879cb2'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23879cb2'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e")}.breadcrumb{--bs-breadcrumb-padding-x: 0.75rem;--bs-breadcrumb-padding-y: 0.375rem;--bs-breadcrumb-margin-bottom: 1rem;--bs-breadcrumb-bg: #434343;--bs-breadcrumb-border-radius: 0.25rem;--bs-breadcrumb-divider-color: rgba(255, 255, 255, 0.75);--bs-breadcrumb-item-padding-x: 0.5rem;--bs-breadcrumb-item-active-color: rgba(255, 255, 255, 0.75);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding:var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg);border-radius:var(--bs-breadcrumb-border-radius)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, ">") /* rtl: var(--bs-breadcrumb-divider, ">") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: 0.75rem;--bs-pagination-padding-y: 0.375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: #fff;--bs-pagination-bg: #00bc8c;--bs-pagination-border-width: 0;--bs-pagination-border-color: transparent;--bs-pagination-border-radius: 0.25rem;--bs-pagination-hover-color: #fff;--bs-pagination-hover-bg: #00efb2;--bs-pagination-hover-border-color: transparent;--bs-pagination-focus-color: #009670;--bs-pagination-focus-bg: #ebebeb;--bs-pagination-focus-box-shadow: 0 0 0 0.25rem rgba(55, 90, 127, 0.25);--bs-pagination-active-color: #fff;--bs-pagination-active-bg: #00efb2;--bs-pagination-active-border-color: transparent;--bs-pagination-disabled-color: #fff;--bs-pagination-disabled-bg: #007053;--bs-pagination-disabled-border-color: transparent;display:flex;display:-webkit-flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out,box-shadow .15s ease-in-out}@media(prefers-reduced-motion: reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.page-link.active,.active>.page-link{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.page-link.disabled,.disabled>.page-link{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(0*-1)}.page-item:first-child .page-link{border-top-left-radius:var(--bs-pagination-border-radius);border-bottom-left-radius:var(--bs-pagination-border-radius)}.page-item:last-child .page-link{border-top-right-radius:var(--bs-pagination-border-radius);border-bottom-right-radius:var(--bs-pagination-border-radius)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: 0.75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: 0.5rem}.pagination-sm{--bs-pagination-padding-x: 0.5rem;--bs-pagination-padding-y: 0.25rem;--bs-pagination-font-size:0.875rem;--bs-pagination-border-radius: 0.2em}.badge{--bs-badge-padding-x: 0.65em;--bs-badge-padding-y: 0.35em;--bs-badge-font-size:0.75em;--bs-badge-font-weight: 700;--bs-badge-color: #fff;--bs-badge-border-radius: 0.25rem;display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline;border-radius:var(--bs-badge-border-radius)}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: 1px solid var(--bs-alert-border-color);--bs-alert-border-radius: 0.25rem;--bs-alert-link-color: inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border);border-radius:var(--bs-alert-border-radius)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-default{--bs-alert-color: var(--bs-default-text-emphasis);--bs-alert-bg: var(--bs-default-bg-subtle);--bs-alert-border-color: var(--bs-default-border-subtle);--bs-alert-link-color: var(--bs-default-text-emphasis)}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:1rem}}.progress,.progress-stacked{--bs-progress-height: 1rem;--bs-progress-font-size:0.75rem;--bs-progress-bg: #434343;--bs-progress-border-radius: 0.25rem;--bs-progress-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.075);--bs-progress-bar-color: #fff;--bs-progress-bar-bg: #375a7f;--bs-progress-bar-transition: width 0.6s ease;display:flex;display:-webkit-flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg);border-radius:var(--bs-progress-border-radius)}.progress-bar{display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;justify-content:center;-webkit-justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media(prefers-reduced-motion: reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media(prefers-reduced-motion: reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color: #fff;--bs-list-group-bg: #2d2d2d;--bs-list-group-border-color: #434343;--bs-list-group-border-width: 1px;--bs-list-group-border-radius: 0.25rem;--bs-list-group-item-padding-x: 1rem;--bs-list-group-item-padding-y: 0.5rem;--bs-list-group-action-color: rgba(255, 255, 255, 0.75);--bs-list-group-action-hover-color: #fff;--bs-list-group-action-hover-bg: #434343;--bs-list-group-action-active-color: #fff;--bs-list-group-action-active-bg: #222;--bs-list-group-disabled-color: rgba(255, 255, 255, 0.75);--bs-list-group-disabled-bg: #2d2d2d;--bs-list-group-active-color: #fff;--bs-list-group-active-bg: #375a7f;--bs-list-group-active-border-color: #375a7f;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;padding-left:0;margin-bottom:0;border-radius:var(--bs-list-group-border-radius)}.list-group-numbered{list-style-type:none;counter-reset:section}.list-group-numbered>.list-group-item::before{content:counters(section, ".") ". ";counter-increment:section}.list-group-item-action{width:100%;color:var(--bs-list-group-action-color);text-align:inherit}.list-group-item-action:hover,.list-group-item-action:focus{z-index:1;color:var(--bs-list-group-action-hover-color);text-decoration:none;background-color:var(--bs-list-group-action-hover-bg)}.list-group-item-action:active{color:var(--bs-list-group-action-active-color);background-color:var(--bs-list-group-action-active-bg)}.list-group-item{position:relative;display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);color:var(--bs-list-group-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid 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576px){.list-group-horizontal-sm{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-sm>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-sm>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 768px){.list-group-horizontal-md{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-md>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-md>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-md>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 992px){.list-group-horizontal-lg{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-lg>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-lg>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1200px){.list-group-horizontal-xl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1400px){.list-group-horizontal-xxl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xxl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xxl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xxl>.list-group-item.active{margin-top:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}.list-group-flush{border-radius:0}.list-group-flush>.list-group-item{border-width:0 0 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var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-primary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-primary-border-subtle);--bs-list-group-active-color: var(--bs-primary-bg-subtle);--bs-list-group-active-bg: var(--bs-primary-text-emphasis);--bs-list-group-active-border-color: var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color: var(--bs-secondary-text-emphasis);--bs-list-group-bg: var(--bs-secondary-bg-subtle);--bs-list-group-border-color: var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-secondary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-secondary-border-subtle);--bs-list-group-active-color: var(--bs-secondary-bg-subtle);--bs-list-group-active-bg: var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-info-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-info-border-subtle);--bs-list-group-active-color: var(--bs-info-bg-subtle);--bs-list-group-active-bg: var(--bs-info-text-emphasis);--bs-list-group-active-border-color: var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color: var(--bs-warning-text-emphasis);--bs-list-group-bg: var(--bs-warning-bg-subtle);--bs-list-group-border-color: var(--bs-warning-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-warning-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-warning-border-subtle);--bs-list-group-active-color: var(--bs-warning-bg-subtle);--bs-list-group-active-bg: var(--bs-warning-text-emphasis);--bs-list-group-active-border-color: var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-dark-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-dark-border-subtle);--bs-list-group-active-color: var(--bs-dark-bg-subtle);--bs-list-group-active-bg: var(--bs-dark-text-emphasis);--bs-list-group-active-border-color: var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color: #fff;--bs-btn-close-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' 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#6c757d;--quarto-scss-export-gray-800: #343a40;--quarto-scss-export-card-cap-bg: rgba(52, 58, 64, 0.25);--quarto-scss-export-border-color: #dee2e6;--quarto-scss-export-text-muted: #6c757d;--quarto-scss-export-white: #fff;--quarto-scss-export-gray-100: #f8f9fa;--quarto-scss-export-gray-200: #ebebeb;--quarto-scss-export-gray-400: #ced4da;--quarto-scss-export-gray-700: #444;--quarto-scss-export-gray-900: #222;--quarto-scss-export-black: #000;--quarto-scss-export-blue: #375a7f;--quarto-scss-export-indigo: #6610f2;--quarto-scss-export-purple: #6f42c1;--quarto-scss-export-pink: #e83e8c;--quarto-scss-export-red: #e74c3c;--quarto-scss-export-orange: #fd7e14;--quarto-scss-export-yellow: #f39c12;--quarto-scss-export-green: #00bc8c;--quarto-scss-export-teal: #20c997;--quarto-scss-export-cyan: #3498db;--quarto-scss-export-body-bg: #222;--quarto-scss-export-body-color: #fff;--quarto-scss-export-primary: #375a7f;--quarto-scss-export-success: #00bc8c;--quarto-scss-export-info: #3498db;--quarto-scss-export-warning: #f39c12;--quarto-scss-export-danger: #e74c3c;--quarto-scss-export-link-color: #00bc8c;--quarto-scss-export-input-bg: #fff;--quarto-scss-export-input-border-color: #adb5bd;--quarto-scss-export-form-check-input-bg: #fff;--quarto-scss-export-form-file-button-hover-bg: #363636;--quarto-scss-export-dropdown-bg: #222;--quarto-scss-export-dropdown-link-color: #fff;--quarto-scss-export-dropdown-link-hover-color: #fff;--quarto-scss-export-dropdown-link-hover-bg: #375a7f;--quarto-scss-export-nav-tabs-link-active-color: #fff;--quarto-scss-export-pagination-color: #fff;--quarto-scss-export-pagination-bg: #00bc8c;--quarto-scss-export-pagination-border-color: transparent;--quarto-scss-export-pagination-hover-color: #fff;--quarto-scss-export-pagination-hover-bg: #00efb2;--quarto-scss-export-pagination-hover-border-color: transparent;--quarto-scss-export-pagination-active-bg: #00efb2;--quarto-scss-export-pagination-active-border-color: transparent;--quarto-scss-export-pagination-disabled-color: #fff;--quarto-scss-export-pagination-disabled-bg: #007053;--quarto-scss-export-pagination-disabled-border-color: transparent;--quarto-scss-export-list-group-color: #fff;--quarto-scss-export-list-group-action-hover-color: #fff;--quarto-scss-export-list-group-action-active-bg: #222;--quarto-scss-export-btn-close-color: #fff;--quarto-scss-export-title-banner-color: ;--quarto-scss-export-title-banner-bg: ;--quarto-scss-export-btn-code-copy-color: #f8f8f2;--quarto-scss-export-btn-code-copy-color-active: #ffa07a;--quarto-scss-export-link-color-bg: transparent;--quarto-scss-export-code-color: #7d12ba;--quarto-scss-export-code-bg: #f8f9fa;--quarto-scss-export-toc-color: #00bc8c;--quarto-scss-export-toc-active-border: #00bc8c;--quarto-scss-export-toc-inactive-border: #ebebeb;--quarto-scss-export-navbar-default: #375a7f;--quarto-scss-export-navbar-hl-override: false;--quarto-scss-export-navbar-bg: #375a7f;--quarto-scss-export-btn-bg: #434343;--quarto-scss-export-btn-fg: #d9d9d9;--quarto-scss-export-body-contrast-bg: #222;--quarto-scss-export-body-contrast-color: #fff;--quarto-scss-export-navbar-fg: #dee2e6;--quarto-scss-export-navbar-hl: #fff;--quarto-scss-export-navbar-brand: #dee2e6;--quarto-scss-export-navbar-brand-hl: #fff;--quarto-scss-export-navbar-toggler-border-color: rgba(222, 226, 230, 0);--quarto-scss-export-navbar-hover-color: rgba(255, 255, 255, 0.8);--quarto-scss-export-navbar-disabled-color: rgba(222, 226, 230, 0.75);--quarto-scss-export-sidebar-fg: #fefefe;--quarto-scss-export-title-block-color: #fff;--quarto-scss-export-title-block-contast-color: #222;--quarto-scss-export-footer-bg: #222;--quarto-scss-export-footer-fg: #8a8a8a;--quarto-scss-export-code-annotation-higlight-color: rgba(170, 170, 170, 0.2666666667);--quarto-scss-export-code-annotation-higlight-bg: rgba(170, 170, 170, 0.1333333333);--quarto-scss-export-table-group-separator-color: #919191;--quarto-scss-export-table-group-separator-color-lighter: #4e4e4e;--quarto-scss-export-link-decoration: underline;--quarto-scss-export-sidebar-glass-bg: rgba(102, 102, 102, 0.4);--quarto-scss-export-color-contrast-dark: #000;--quarto-scss-export-color-contrast-light: #fff;--quarto-scss-export-blue-100: #d7dee5;--quarto-scss-export-blue-200: #afbdcc;--quarto-scss-export-blue-300: #879cb2;--quarto-scss-export-blue-400: #5f7b99;--quarto-scss-export-blue-500: #375a7f;--quarto-scss-export-blue-600: #2c4866;--quarto-scss-export-blue-700: #21364c;--quarto-scss-export-blue-800: #162433;--quarto-scss-export-blue-900: #0b1219;--quarto-scss-export-indigo-100: #e0cffc;--quarto-scss-export-indigo-200: #c29ffa;--quarto-scss-export-indigo-300: #a370f7;--quarto-scss-export-indigo-400: #8540f5;--quarto-scss-export-indigo-500: #6610f2;--quarto-scss-export-indigo-600: #520dc2;--quarto-scss-export-indigo-700: #3d0a91;--quarto-scss-export-indigo-800: #290661;--quarto-scss-export-indigo-900: #140330;--quarto-scss-export-purple-100: #e2d9f3;--quarto-scss-export-purple-200: #c5b3e6;--quarto-scss-export-purple-300: #a98eda;--quarto-scss-export-purple-400: #8c68cd;--quarto-scss-export-purple-500: #6f42c1;--quarto-scss-export-purple-600: #59359a;--quarto-scss-export-purple-700: #432874;--quarto-scss-export-purple-800: #2c1a4d;--quarto-scss-export-purple-900: #160d27;--quarto-scss-export-pink-100: #fad8e8;--quarto-scss-export-pink-200: #f6b2d1;--quarto-scss-export-pink-300: #f18bba;--quarto-scss-export-pink-400: #ed65a3;--quarto-scss-export-pink-500: #e83e8c;--quarto-scss-export-pink-600: #ba3270;--quarto-scss-export-pink-700: #8b2554;--quarto-scss-export-pink-800: #5d1938;--quarto-scss-export-pink-900: #2e0c1c;--quarto-scss-export-red-100: #fadbd8;--quarto-scss-export-red-200: #f5b7b1;--quarto-scss-export-red-300: #f1948a;--quarto-scss-export-red-400: #ec7063;--quarto-scss-export-red-500: #e74c3c;--quarto-scss-export-red-600: #b93d30;--quarto-scss-export-red-700: #8b2e24;--quarto-scss-export-red-800: #5c1e18;--quarto-scss-export-red-900: #2e0f0c;--quarto-scss-export-orange-100: #ffe5d0;--quarto-scss-export-orange-200: #fecba1;--quarto-scss-export-orange-300: #feb272;--quarto-scss-export-orange-400: #fd9843;--quarto-scss-export-orange-500: #fd7e14;--quarto-scss-export-orange-600: #ca6510;--quarto-scss-export-orange-700: #984c0c;--quarto-scss-export-orange-800: #653208;--quarto-scss-export-orange-900: #331904;--quarto-scss-export-yellow-100: #fdebd0;--quarto-scss-export-yellow-200: #fad7a0;--quarto-scss-export-yellow-300: #f8c471;--quarto-scss-export-yellow-400: #f5b041;--quarto-scss-export-yellow-500: #f39c12;--quarto-scss-export-yellow-600: #c27d0e;--quarto-scss-export-yellow-700: #925e0b;--quarto-scss-export-yellow-800: #613e07;--quarto-scss-export-yellow-900: #311f04;--quarto-scss-export-green-100: #ccf2e8;--quarto-scss-export-green-200: #99e4d1;--quarto-scss-export-green-300: #66d7ba;--quarto-scss-export-green-400: #33c9a3;--quarto-scss-export-green-500: #00bc8c;--quarto-scss-export-green-600: #009670;--quarto-scss-export-green-700: #007154;--quarto-scss-export-green-800: #004b38;--quarto-scss-export-green-900: #00261c;--quarto-scss-export-teal-100: #d2f4ea;--quarto-scss-export-teal-200: #a6e9d5;--quarto-scss-export-teal-300: #79dfc1;--quarto-scss-export-teal-400: #4dd4ac;--quarto-scss-export-teal-500: #20c997;--quarto-scss-export-teal-600: #1aa179;--quarto-scss-export-teal-700: #13795b;--quarto-scss-export-teal-800: #0d503c;--quarto-scss-export-teal-900: #06281e;--quarto-scss-export-cyan-100: #d6eaf8;--quarto-scss-export-cyan-200: #aed6f1;--quarto-scss-export-cyan-300: #85c1e9;--quarto-scss-export-cyan-400: #5dade2;--quarto-scss-export-cyan-500: #3498db;--quarto-scss-export-cyan-600: #2a7aaf;--quarto-scss-export-cyan-700: #1f5b83;--quarto-scss-export-cyan-800: #153d58;--quarto-scss-export-cyan-900: #0a1e2c;--quarto-scss-export-default: #434343;--quarto-scss-export-primary-text-emphasis: #162433;--quarto-scss-export-secondary-text-emphasis: #1b1b1b;--quarto-scss-export-success-text-emphasis: #004b38;--quarto-scss-export-info-text-emphasis: #153d58;--quarto-scss-export-warning-text-emphasis: #613e07;--quarto-scss-export-danger-text-emphasis: #5c1e18;--quarto-scss-export-light-text-emphasis: #444;--quarto-scss-export-dark-text-emphasis: #444;--quarto-scss-export-primary-bg-subtle: #d7dee5;--quarto-scss-export-secondary-bg-subtle: #d9d9d9;--quarto-scss-export-success-bg-subtle: #ccf2e8;--quarto-scss-export-info-bg-subtle: #d6eaf8;--quarto-scss-export-warning-bg-subtle: #fdebd0;--quarto-scss-export-danger-bg-subtle: #fadbd8;--quarto-scss-export-light-bg-subtle: #fcfcfd;--quarto-scss-export-dark-bg-subtle: #ced4da;--quarto-scss-export-primary-border-subtle: #afbdcc;--quarto-scss-export-secondary-border-subtle: #b4b4b4;--quarto-scss-export-success-border-subtle: #99e4d1;--quarto-scss-export-info-border-subtle: #aed6f1;--quarto-scss-export-warning-border-subtle: #fad7a0;--quarto-scss-export-danger-border-subtle: #f5b7b1;--quarto-scss-export-light-border-subtle: #ebebeb;--quarto-scss-export-dark-border-subtle: #adb5bd;--quarto-scss-export-body-text-align: ;--quarto-scss-export-body-secondary-color: rgba(255, 255, 255, 0.75);--quarto-scss-export-body-secondary-bg: #ebebeb;--quarto-scss-export-body-tertiary-color: rgba(255, 255, 255, 0.5);--quarto-scss-export-body-tertiary-bg: #f8f9fa;--quarto-scss-export-body-emphasis-color: #000;--quarto-scss-export-link-hover-color: #009670;--quarto-scss-export-link-hover-decoration: ;--quarto-scss-export-border-color-translucent: rgba(0, 0, 0, 0.175);--quarto-scss-export-component-active-bg: #375a7f;--quarto-scss-export-component-active-color: #fff;--quarto-scss-export-focus-ring-color: rgba(55, 90, 127, 0.25);--quarto-scss-export-headings-font-family: ;--quarto-scss-export-headings-font-style: ;--quarto-scss-export-display-font-family: ;--quarto-scss-export-display-font-style: ;--quarto-scss-export-blockquote-footer-color: #6c757d;--quarto-scss-export-blockquote-border-color: #ebebeb;--quarto-scss-export-hr-bg-color: ;--quarto-scss-export-hr-height: ;--quarto-scss-export-hr-border-color: ;--quarto-scss-export-legend-font-weight: ;--quarto-scss-export-mark-bg: #fdebd0;--quarto-scss-export-table-color: #fff;--quarto-scss-export-table-bg: #222;--quarto-scss-export-table-accent-bg: transparent;--quarto-scss-export-table-th-font-weight: ;--quarto-scss-export-table-striped-color: #fff;--quarto-scss-export-table-striped-bg: rgba(0, 0, 0, 0.05);--quarto-scss-export-table-active-color: #fff;--quarto-scss-export-table-active-bg: rgba(0, 0, 0, 0.1);--quarto-scss-export-table-hover-color: #fff;--quarto-scss-export-table-hover-bg: rgba(0, 0, 0, 0.075);--quarto-scss-export-table-caption-color: rgba(255, 255, 255, 0.75);--quarto-scss-export-input-btn-font-family: ;--quarto-scss-export-input-btn-focus-color: rgba(55, 90, 127, 0.25);--quarto-scss-export-btn-color: #fff;--quarto-scss-export-btn-font-family: ;--quarto-scss-export-btn-white-space: ;--quarto-scss-export-btn-link-color: #00bc8c;--quarto-scss-export-btn-link-hover-color: #009670;--quarto-scss-export-btn-link-disabled-color: #6c757d;--quarto-scss-export-form-text-font-style: ;--quarto-scss-export-form-text-font-weight: ;--quarto-scss-export-form-text-color: rgba(255, 255, 255, 0.75);--quarto-scss-export-form-label-font-size: ;--quarto-scss-export-form-label-font-style: ;--quarto-scss-export-form-label-font-weight: ;--quarto-scss-export-form-label-color: ;--quarto-scss-export-input-font-family: ;--quarto-scss-export-input-disabled-color: ;--quarto-scss-export-input-disabled-bg: #ebebeb;--quarto-scss-export-input-disabled-border-color: ;--quarto-scss-export-input-focus-bg: #fff;--quarto-scss-export-input-focus-border-color: #9badbf;--quarto-scss-export-input-plaintext-color: #fff;--quarto-scss-export-form-check-label-color: ;--quarto-scss-export-form-check-transition: ;--quarto-scss-export-form-check-input-focus-border: #9badbf;--quarto-scss-export-form-check-input-checked-color: #fff;--quarto-scss-export-form-check-input-checked-bg-color: #375a7f;--quarto-scss-export-form-check-input-checked-border-color: #375a7f;--quarto-scss-export-form-check-input-indeterminate-color: #fff;--quarto-scss-export-form-check-input-indeterminate-bg-color: #375a7f;--quarto-scss-export-form-check-input-indeterminate-border-color: #375a7f;--quarto-scss-export-form-switch-color: rgba(0, 0, 0, 0.25);--quarto-scss-export-form-switch-focus-color: #9badbf;--quarto-scss-export-form-switch-checked-color: #fff;--quarto-scss-export-input-group-addon-border-color: #adb5bd;--quarto-scss-export-form-select-font-family: ;--quarto-scss-export-form-select-bg: #fff;--quarto-scss-export-form-select-disabled-bg: #ebebeb;--quarto-scss-export-form-select-disabled-border-color: ;--quarto-scss-export-form-select-indicator-color: #343a40;--quarto-scss-export-form-select-border-color: #adb5bd;--quarto-scss-export-form-select-focus-border-color: #9badbf;--quarto-scss-export-form-range-track-bg: #f8f9fa;--quarto-scss-export-form-range-thumb-bg: #375a7f;--quarto-scss-export-form-range-thumb-active-bg: #c3ced9;--quarto-scss-export-form-range-thumb-disabled-bg: rgba(255, 255, 255, 0.75);--quarto-scss-export-form-floating-label-disabled-color: #6c757d;--quarto-scss-export-form-feedback-font-style: ;--quarto-scss-export-form-feedback-valid-color: #00bc8c;--quarto-scss-export-form-feedback-invalid-color: #e74c3c;--quarto-scss-export-form-feedback-icon-valid-color: #00bc8c;--quarto-scss-export-form-feedback-icon-invalid-color: #e74c3c;--quarto-scss-export-form-valid-color: #00bc8c;--quarto-scss-export-form-valid-border-color: #00bc8c;--quarto-scss-export-form-invalid-color: #e74c3c;--quarto-scss-export-form-invalid-border-color: #e74c3c;--quarto-scss-export-nav-link-font-size: ;--quarto-scss-export-nav-link-font-weight: ;--quarto-scss-export-nav-link-color: #00bc8c;--quarto-scss-export-nav-link-hover-color: #009670;--quarto-scss-export-nav-tabs-link-active-bg: #222;--quarto-scss-export-nav-pills-link-active-bg: #375a7f;--quarto-scss-export-nav-pills-link-active-color: #fff;--quarto-scss-export-nav-underline-link-active-color: #000;--quarto-scss-export-navbar-padding-x: ;--quarto-scss-export-navbar-light-contrast: #fff;--quarto-scss-export-navbar-dark-contrast: #fff;--quarto-scss-export-navbar-light-icon-color: rgba(255, 255, 255, 0.75);--quarto-scss-export-navbar-dark-icon-color: rgba(255, 255, 255, 0.75);--quarto-scss-export-dropdown-color: #fff;--quarto-scss-export-dropdown-link-active-bg: #375a7f;--quarto-scss-export-dropdown-link-active-color: #fff;--quarto-scss-export-dropdown-link-disabled-color: rgba(255, 255, 255, 0.5);--quarto-scss-export-dropdown-header-color: #6c757d;--quarto-scss-export-dropdown-dark-color: #dee2e6;--quarto-scss-export-dropdown-dark-bg: #343a40;--quarto-scss-export-dropdown-dark-box-shadow: 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30'%3e%3cpath stroke='%23353944' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e")}[data-bs-theme=dark] .navbar-toggler-icon{--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='%23353944' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e")}.card{--bs-card-spacer-y: 1rem;--bs-card-spacer-x: 1rem;--bs-card-title-spacer-y: 0.5rem;--bs-card-title-color: ;--bs-card-subtitle-color: ;--bs-card-border-width: 1px;--bs-card-border-color: rgba(0, 0, 0, 0.175);--bs-card-border-radius: 0.25rem;--bs-card-box-shadow: ;--bs-card-inner-border-radius: calc(0.25rem - 1px);--bs-card-cap-padding-y: 0.5rem;--bs-card-cap-padding-x: 1rem;--bs-card-cap-bg: rgba(52, 58, 64, 0.25);--bs-card-cap-color: ;--bs-card-height: ;--bs-card-color: ;--bs-card-bg: #e9edfb;--bs-card-img-overlay-padding: 1rem;--bs-card-group-margin: 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.nav-link.active{background-color:var(--bs-card-bg);border-bottom-color:var(--bs-card-bg)}.card-header-pills{margin-right:calc(-0.5*var(--bs-card-cap-padding-x));margin-left:calc(-0.5*var(--bs-card-cap-padding-x))}.card-img-overlay{position:absolute;top:0;right:0;bottom:0;left:0;padding:var(--bs-card-img-overlay-padding);border-radius:var(--bs-card-inner-border-radius)}.card-img,.card-img-top,.card-img-bottom{width:100%}.card-img,.card-img-top{border-top-left-radius:var(--bs-card-inner-border-radius);border-top-right-radius:var(--bs-card-inner-border-radius)}.card-img,.card-img-bottom{border-bottom-right-radius:var(--bs-card-inner-border-radius);border-bottom-left-radius:var(--bs-card-inner-border-radius)}.card-group>.card{margin-bottom:var(--bs-card-group-margin)}@media(min-width: 576px){.card-group{display:flex;display:-webkit-flex;flex-flow:row wrap;-webkit-flex-flow:row wrap}.card-group>.card{flex:1 0 0%;-webkit-flex:1 0 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6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-focus-border-color: #a0b1ec;--bs-accordion-btn-focus-box-shadow: 0 0 0 0.25rem rgba(64, 99, 216, 0.25);--bs-accordion-body-padding-x: 1.25rem;--bs-accordion-body-padding-y: 1rem;--bs-accordion-active-color: #1a2856;--bs-accordion-active-bg: #d9e0f7}.accordion-button{position:relative;display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;width:100%;padding:var(--bs-accordion-btn-padding-y) var(--bs-accordion-btn-padding-x);font-size:1rem;color:var(--bs-accordion-btn-color);text-align:left;background-color:var(--bs-accordion-btn-bg);border:0;border-radius:0;overflow-anchor:none;transition:var(--bs-accordion-transition)}@media(prefers-reduced-motion: reduce){.accordion-button{transition:none}}.accordion-button:not(.collapsed){color:var(--bs-accordion-active-color);background-color:var(--bs-accordion-active-bg);box-shadow:inset 0 calc(-1*var(--bs-accordion-border-width)) 0 var(--bs-accordion-border-color)}.accordion-button:not(.collapsed)::after{background-image:var(--bs-accordion-btn-active-icon);transform:var(--bs-accordion-btn-icon-transform)}.accordion-button::after{flex-shrink:0;-webkit-flex-shrink:0;width:var(--bs-accordion-btn-icon-width);height:var(--bs-accordion-btn-icon-width);margin-left:auto;content:"";background-image:var(--bs-accordion-btn-icon);background-repeat:no-repeat;background-size:var(--bs-accordion-btn-icon-width);transition:var(--bs-accordion-btn-icon-transition)}@media(prefers-reduced-motion: reduce){.accordion-button::after{transition:none}}.accordion-button:hover{z-index:2}.accordion-button:focus{z-index:3;border-color:var(--bs-accordion-btn-focus-border-color);outline:0;box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.accordion-header{margin-bottom:0}.accordion-item{color:var(--bs-accordion-color);background-color:var(--bs-accordion-bg);border:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.accordion-item:first-of-type{border-top-left-radius:var(--bs-accordion-border-radius);border-top-right-radius:var(--bs-accordion-border-radius)}.accordion-item:first-of-type .accordion-button{border-top-left-radius:var(--bs-accordion-inner-border-radius);border-top-right-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:not(:first-of-type){border-top:0}.accordion-item:last-of-type{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-item:last-of-type .accordion-button.collapsed{border-bottom-right-radius:var(--bs-accordion-inner-border-radius);border-bottom-left-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:last-of-type .accordion-collapse{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0;border-radius:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}.accordion-flush .accordion-item .accordion-button,.accordion-flush .accordion-item .accordion-button.collapsed{border-radius:0}[data-bs-theme=dark] .accordion-button::after{--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%238ca1e8'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%238ca1e8'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e")}.breadcrumb{--bs-breadcrumb-padding-x: 0;--bs-breadcrumb-padding-y: 0;--bs-breadcrumb-margin-bottom: 1rem;--bs-breadcrumb-bg: ;--bs-breadcrumb-border-radius: ;--bs-breadcrumb-divider-color: rgba(13, 24, 63, 0.75);--bs-breadcrumb-item-padding-x: 0.5rem;--bs-breadcrumb-item-active-color: rgba(13, 24, 63, 0.75);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding:var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg);border-radius:var(--bs-breadcrumb-border-radius)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, ">") /* rtl: var(--bs-breadcrumb-divider, ">") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: 0.75rem;--bs-pagination-padding-y: 0.375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: #4063D8;--bs-pagination-bg: #e9edfb;--bs-pagination-border-width: 1px;--bs-pagination-border-color: #dee2e6;--bs-pagination-border-radius: 0.25rem;--bs-pagination-hover-color: #334fad;--bs-pagination-hover-bg: #f8f9fa;--bs-pagination-hover-border-color: #dee2e6;--bs-pagination-focus-color: #334fad;--bs-pagination-focus-bg: #e9ecef;--bs-pagination-focus-box-shadow: 0 0 0 0.25rem rgba(64, 99, 216, 0.25);--bs-pagination-active-color: #ffffff;--bs-pagination-active-bg: #4063D8;--bs-pagination-active-border-color: #4063D8;--bs-pagination-disabled-color: rgba(13, 24, 63, 0.75);--bs-pagination-disabled-bg: #e9ecef;--bs-pagination-disabled-border-color: #dee2e6;display:flex;display:-webkit-flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out,box-shadow .15s ease-in-out}@media(prefers-reduced-motion: reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.page-link.active,.active>.page-link{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.page-link.disabled,.disabled>.page-link{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(1px*-1)}.page-item:first-child .page-link{border-top-left-radius:var(--bs-pagination-border-radius);border-bottom-left-radius:var(--bs-pagination-border-radius)}.page-item:last-child .page-link{border-top-right-radius:var(--bs-pagination-border-radius);border-bottom-right-radius:var(--bs-pagination-border-radius)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: 0.75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: 0.5rem}.pagination-sm{--bs-pagination-padding-x: 0.5rem;--bs-pagination-padding-y: 0.25rem;--bs-pagination-font-size:0.875rem;--bs-pagination-border-radius: 0.2em}.badge{--bs-badge-padding-x: 0.65em;--bs-badge-padding-y: 0.35em;--bs-badge-font-size:0.75em;--bs-badge-font-weight: 700;--bs-badge-color: #ffffff;--bs-badge-border-radius: 0.25rem;display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline;border-radius:var(--bs-badge-border-radius)}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: 1px solid var(--bs-alert-border-color);--bs-alert-border-radius: 0.25rem;--bs-alert-link-color: inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border);border-radius:var(--bs-alert-border-radius)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-default{--bs-alert-color: var(--bs-default-text-emphasis);--bs-alert-bg: var(--bs-default-bg-subtle);--bs-alert-border-color: var(--bs-default-border-subtle);--bs-alert-link-color: var(--bs-default-text-emphasis)}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:1rem}}.progress,.progress-stacked{--bs-progress-height: 1rem;--bs-progress-font-size:0.75rem;--bs-progress-bg: #e9ecef;--bs-progress-border-radius: 0.25rem;--bs-progress-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.075);--bs-progress-bar-color: #ffffff;--bs-progress-bar-bg: #4063D8;--bs-progress-bar-transition: width 0.6s ease;display:flex;display:-webkit-flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg);border-radius:var(--bs-progress-border-radius)}.progress-bar{display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;justify-content:center;-webkit-justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media(prefers-reduced-motion: reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media(prefers-reduced-motion: reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color: #0d183f;--bs-list-group-bg: #e9edfb;--bs-list-group-border-color: #dee2e6;--bs-list-group-border-width: 1px;--bs-list-group-border-radius: 0.25rem;--bs-list-group-item-padding-x: 1rem;--bs-list-group-item-padding-y: 0.5rem;--bs-list-group-action-color: rgba(13, 24, 63, 0.75);--bs-list-group-action-hover-color: #000;--bs-list-group-action-hover-bg: #f8f9fa;--bs-list-group-action-active-color: #0d183f;--bs-list-group-action-active-bg: #e9ecef;--bs-list-group-disabled-color: rgba(13, 24, 63, 0.75);--bs-list-group-disabled-bg: #e9edfb;--bs-list-group-active-color: #ffffff;--bs-list-group-active-bg: #4063D8;--bs-list-group-active-border-color: #4063D8;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;padding-left:0;margin-bottom:0;border-radius:var(--bs-list-group-border-radius)}.list-group-numbered{list-style-type:none;counter-reset:section}.list-group-numbered>.list-group-item::before{content:counters(section, ".") ". ";counter-increment:section}.list-group-item-action{width:100%;color:var(--bs-list-group-action-color);text-align:inherit}.list-group-item-action:hover,.list-group-item-action:focus{z-index:1;color:var(--bs-list-group-action-hover-color);text-decoration:none;background-color:var(--bs-list-group-action-hover-bg)}.list-group-item-action:active{color:var(--bs-list-group-action-active-color);background-color:var(--bs-list-group-action-active-bg)}.list-group-item{position:relative;display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);color:var(--bs-list-group-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid 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576px){.list-group-horizontal-sm{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-sm>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-sm>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 768px){.list-group-horizontal-md{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-md>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-md>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-md>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 992px){.list-group-horizontal-lg{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-lg>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-lg>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1200px){.list-group-horizontal-xl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1400px){.list-group-horizontal-xxl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xxl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xxl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xxl>.list-group-item.active{margin-top:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}.list-group-flush{border-radius:0}.list-group-flush>.list-group-item{border-width:0 0 var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-default{--bs-list-group-color: var(--bs-default-text-emphasis);--bs-list-group-bg: var(--bs-default-bg-subtle);--bs-list-group-border-color: var(--bs-default-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-default-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-default-border-subtle);--bs-list-group-active-color: var(--bs-default-bg-subtle);--bs-list-group-active-bg: var(--bs-default-text-emphasis);--bs-list-group-active-border-color: var(--bs-default-text-emphasis)}.list-group-item-primary{--bs-list-group-color: var(--bs-primary-text-emphasis);--bs-list-group-bg: var(--bs-primary-bg-subtle);--bs-list-group-border-color: var(--bs-primary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-primary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-primary-border-subtle);--bs-list-group-active-color: var(--bs-primary-bg-subtle);--bs-list-group-active-bg: var(--bs-primary-text-emphasis);--bs-list-group-active-border-color: var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color: var(--bs-secondary-text-emphasis);--bs-list-group-bg: var(--bs-secondary-bg-subtle);--bs-list-group-border-color: var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-secondary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-secondary-border-subtle);--bs-list-group-active-color: var(--bs-secondary-bg-subtle);--bs-list-group-active-bg: var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-info-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-info-border-subtle);--bs-list-group-active-color: var(--bs-info-bg-subtle);--bs-list-group-active-bg: var(--bs-info-text-emphasis);--bs-list-group-active-border-color: var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color: var(--bs-warning-text-emphasis);--bs-list-group-bg: var(--bs-warning-bg-subtle);--bs-list-group-border-color: var(--bs-warning-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-warning-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-warning-border-subtle);--bs-list-group-active-color: var(--bs-warning-bg-subtle);--bs-list-group-active-bg: var(--bs-warning-text-emphasis);--bs-list-group-active-border-color: var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-dark-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-dark-border-subtle);--bs-list-group-active-color: var(--bs-dark-bg-subtle);--bs-list-group-active-bg: var(--bs-dark-text-emphasis);--bs-list-group-active-border-color: var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color: #000;--bs-btn-close-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' 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0.25rem;--bs-toast-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15);--bs-toast-header-color: rgba(13, 24, 63, 0.75);--bs-toast-header-bg: rgba(233, 237, 251, 0.85);--bs-toast-header-border-color: rgba(0, 0, 0, 0.175);width:var(--bs-toast-max-width);max-width:100%;font-size:var(--bs-toast-font-size);color:var(--bs-toast-color);pointer-events:auto;background-color:var(--bs-toast-bg);background-clip:padding-box;border:var(--bs-toast-border-width) solid var(--bs-toast-border-color);box-shadow:var(--bs-toast-box-shadow);border-radius:var(--bs-toast-border-radius)}.toast.showing{opacity:0}.toast:not(.show){display:none}.toast-container{--bs-toast-zindex: 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.value-box-grid{grid-template-columns:var(---bslib-value-box-showcase-w-fs, 1fr) auto}.bslib-value-box.showcase-left-center:not([data-fill-screen=true]) .value-box-grid .value-box-showcase{grid-area:left}.bslib-value-box.showcase-left-center:not([data-fill-screen=true]) .value-box-grid .value-box-area{grid-area:right}.bslib-value-box.showcase-bottom .value-box-grid{grid-template-columns:1fr;grid-template-rows:1fr var(---bslib-value-box-showcase-h, auto);grid-template-areas:"top" "bottom";overflow:hidden}.bslib-value-box.showcase-bottom .value-box-grid .value-box-showcase{grid-area:bottom;padding:0;margin:0}.bslib-value-box.showcase-bottom .value-box-grid .value-box-area{grid-area:top}.bslib-value-box.showcase-bottom[data-full-screen=true] .value-box-grid{grid-template-rows:1fr var(---bslib-value-box-showcase-h-fs, 2fr)}.bslib-value-box.showcase-bottom[data-full-screen=true] .value-box-grid .value-box-showcase{padding:1rem}[data-bs-theme=dark] .bslib-value-box{--bslib-value-box-shadow: 0 0.5rem 1rem rgb(0 0 0 / 50%)}.bslib-card{overflow:auto}.bslib-card .card-body+.card-body{padding-top:0}.bslib-card .card-body{overflow:auto}.bslib-card .card-body p{margin-top:0}.bslib-card .card-body p:last-child{margin-bottom:0}.bslib-card .card-body{max-height:var(--bslib-card-body-max-height, none)}.bslib-card[data-full-screen=true]>.card-body{max-height:var(--bslib-card-body-max-height-full-screen, none)}.bslib-card .card-header .form-group{margin-bottom:0}.bslib-card .card-header .selectize-control{margin-bottom:0}.bslib-card .card-header .selectize-control .item{margin-right:1.15rem}.bslib-card .card-footer{margin-top:auto}.bslib-card .bslib-navs-card-title{display:flex;flex-wrap:wrap;justify-content:space-between;align-items:center}.bslib-card .bslib-navs-card-title .nav{margin-left:auto}.bslib-card .bslib-sidebar-layout:not([data-bslib-sidebar-border=true]){border:none}.bslib-card .bslib-sidebar-layout:not([data-bslib-sidebar-border-radius=true]){border-top-left-radius:0;border-top-right-radius:0}[data-full-screen=true]{position:fixed;inset:3.5rem 1rem 1rem;height:auto !important;max-height:none !important;width:auto !important;z-index:1070}.bslib-full-screen-enter{display:none;position:absolute;bottom:var(--bslib-full-screen-enter-bottom, 0.2rem);right:var(--bslib-full-screen-enter-right, 0);top:var(--bslib-full-screen-enter-top);left:var(--bslib-full-screen-enter-left);color:var(--bslib-color-fg, var(--bs-card-color));background-color:var(--bslib-color-bg, var(--bs-card-bg, var(--bs-body-bg)));border:var(--bs-card-border-width) solid var(--bslib-color-fg, var(--bs-card-border-color));box-shadow:0 2px 4px rgba(0,0,0,.15);margin:.2rem .4rem;padding:.55rem !important;font-size:.8rem;cursor:pointer;opacity:.7;z-index:1070}.bslib-full-screen-enter:hover{opacity:1}.card[data-full-screen=false]:hover>*>.bslib-full-screen-enter{display:block}.bslib-has-full-screen 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j=o[t.charAt(b-1)];if(C[b]=0===m?(C[b+1]<<1|1)&j:(C[b+1]<<1|1)&j|(v[b+1]|v[b])<<1|1|v[b+1],C[b]&d){var x=l(m,b-1);if(x<=u){if(u=x,!((c=b-1)>a))break;p=Math.max(1,2*a-c)}}}if(l(m+1,a)>u)break;v=C}return!(c<0)}},"./src/utils/get-attribute.js":function(t){t.exports=function(t,e){var r=t.getAttribute&&t.getAttribute(e)||null;if(!r)for(var n=t.attributes,s=n.length,i=0;i=48&&t<=57}function i(t,e){for(var i=(t+="").length,a=(e+="").length,o=0,l=0;o=i&&l=a?-1:l>=a&&o=i?1:i-a}i.caseInsensitive=i.i=function(t,e){return i((""+t).toLowerCase(),(""+e).toLowerCase())},Object.defineProperties(i,{alphabet:{get:function(){return e},set:function(t){r=[];var s=0;if(e=t)for(;s0?setTimeout((function(){e(r,n,s)}),1):(t.update(),n(s))}}},"./src/filter.js":function(t){t.exports=function(t){return t.handlers.filterStart=t.handlers.filterStart||[],t.handlers.filterComplete=t.handlers.filterComplete||[],function(e){if(t.trigger("filterStart"),t.i=1,t.reset.filter(),void 0===e)t.filtered=!1;else{t.filtered=!0;for(var r=t.items,n=0,s=r.length;nv.page,a=new g(t[s],void 0,n),v.items.push(a),r.push(a)}return v.update(),r}m(t.slice(0),e)}},this.show=function(t,e){return this.i=t,this.page=e,v.update(),v},this.remove=function(t,e,r){for(var n=0,s=0,i=v.items.length;s-1&&r.splice(n,1),v},this.trigger=function(t){for(var e=v.handlers[t].length;e--;)v.handlers[t][e](v);return v},this.reset={filter:function(){for(var t=v.items,e=t.length;e--;)t[e].filtered=!1;return v},search:function(){for(var t=v.items,e=t.length;e--;)t[e].found=!1;return v}},this.update=function(){var t=v.items,e=t.length;v.visibleItems=[],v.matchingItems=[],v.templater.clear();for(var r=0;r=v.i&&v.visibleItems.lengthe},innerWindow:function(t,e,r){return t>=e-r&&t<=e+r},dotted:function(t,e,r,n,s,i,a){return this.dottedLeft(t,e,r,n,s,i)||this.dottedRight(t,e,r,n,s,i,a)},dottedLeft:function(t,e,r,n,s,i){return e==r+1&&!this.innerWindow(e,s,i)&&!this.right(e,n)},dottedRight:function(t,e,r,n,s,i,a){return!t.items[a-1].values().dotted&&(e==n&&!this.innerWindow(e,s,i)&&!this.right(e,n))}};return function(e){var n=new i(t.listContainer.id,{listClass:e.paginationClass||"pagination",item:e.item||"
    • ",valueNames:["page","dotted"],searchClass:"pagination-search-that-is-not-supposed-to-exist",sortClass:"pagination-sort-that-is-not-supposed-to-exist"});s.bind(n.listContainer,"click",(function(e){var r=e.target||e.srcElement,n=t.utils.getAttribute(r,"data-page"),s=t.utils.getAttribute(r,"data-i");s&&t.show((s-1)*n+1,n)})),t.on("updated",(function(){r(n,e)})),r(n,e)}}},"./src/parse.js":function(t,e,r){t.exports=function(t){var e=r("./src/item.js")(t),n=function(r,n){for(var s=0,i=r.length;s0?setTimeout((function(){e(r,s)}),1):(t.update(),t.trigger("parseComplete"))};return t.handlers.parseComplete=t.handlers.parseComplete||[],function(){var e=function(t){for(var e=t.childNodes,r=[],n=0,s=e.length;n]/g.exec(t)){var e=document.createElement("tbody");return e.innerHTML=t,e.firstElementChild}if(-1!==t.indexOf("<")){var r=document.createElement("div");return r.innerHTML=t,r.firstElementChild}}},a=function(e,r,n){var s=void 0,i=function(e){for(var r=0,n=t.valueNames.length;r=1;)t.list.removeChild(t.list.firstChild)},function(){var r;if("function"!=typeof t.item){if(!(r="string"==typeof t.item?-1===t.item.indexOf("<")?document.getElementById(t.item):i(t.item):s()))throw new Error("The list needs to have at least one item on init otherwise you'll have to add a template.");r=n(r,t.valueNames),e=function(){return r.cloneNode(!0)}}else e=function(e){var r=t.item(e);return i(r)}}()};t.exports=function(t){return new e(t)}},"./src/utils/classes.js":function(t,e,r){var n=r("./src/utils/index-of.js"),s=/\s+/;Object.prototype.toString;function i(t){if(!t||!t.nodeType)throw new Error("A DOM element reference is required");this.el=t,this.list=t.classList}t.exports=function(t){return new i(t)},i.prototype.add=function(t){if(this.list)return this.list.add(t),this;var e=this.array();return~n(e,t)||e.push(t),this.el.className=e.join(" "),this},i.prototype.remove=function(t){if(this.list)return this.list.remove(t),this;var e=this.array(),r=n(e,t);return~r&&e.splice(r,1),this.el.className=e.join(" "),this},i.prototype.toggle=function(t,e){return this.list?(void 0!==e?e!==this.list.toggle(t,e)&&this.list.toggle(t):this.list.toggle(t),this):(void 0!==e?e?this.add(t):this.remove(t):this.has(t)?this.remove(t):this.add(t),this)},i.prototype.array=function(){var t=(this.el.getAttribute("class")||"").replace(/^\s+|\s+$/g,"").split(s);return""===t[0]&&t.shift(),t},i.prototype.has=i.prototype.contains=function(t){return this.list?this.list.contains(t):!!~n(this.array(),t)}},"./src/utils/events.js":function(t,e,r){var n=window.addEventListener?"addEventListener":"attachEvent",s=window.removeEventListener?"removeEventListener":"detachEvent",i="addEventListener"!==n?"on":"",a=r("./src/utils/to-array.js");e.bind=function(t,e,r,s){for(var o=0,l=(t=a(t)).length;o32)return!1;var a=n,o=function(){var t,r={};for(t=0;t=p;b--){var j=o[t.charAt(b-1)];if(C[b]=0===m?(C[b+1]<<1|1)&j:(C[b+1]<<1|1)&j|(v[b+1]|v[b])<<1|1|v[b+1],C[b]&d){var x=l(m,b-1);if(x<=u){if(u=x,!((c=b-1)>a))break;p=Math.max(1,2*a-c)}}}if(l(m+1,a)>u)break;v=C}return!(c<0)}},"./src/utils/get-attribute.js":function(t){t.exports=function(t,e){var r=t.getAttribute&&t.getAttribute(e)||null;if(!r)for(var n=t.attributes,s=n.length,i=0;i=48&&t<=57}function i(t,e){for(var i=(t+="").length,a=(e+="").length,o=0,l=0;o=i&&l=a?-1:l>=a&&o=i?1:i-a}i.caseInsensitive=i.i=function(t,e){return i((""+t).toLowerCase(),(""+e).toLowerCase())},Object.defineProperties(i,{alphabet:{get:function(){return e},set:function(t){r=[];var s=0;if(e=t)for(;s { + // category is URI encoded in EJS template for UTF-8 support + category = decodeURIComponent(atob(category)); if (categoriesLoaded) { activateCategory(category); setCategoryHash(category); @@ -15,7 +17,9 @@ window["quarto-listing-loaded"] = () => { if (hash) { // If there is a category, switch to that if (hash.category) { - activateCategory(hash.category); + // category hash are URI encoded so we need to decode it before processing + // so that we can match it with the category element processed in JS + activateCategory(decodeURIComponent(hash.category)); } // Paginate a specific listing const listingIds = Object.keys(window["quarto-listings"]); @@ -58,7 +62,10 @@ window.document.addEventListener("DOMContentLoaded", function (_event) { ); for (const categoryEl of categoryEls) { - const category = categoryEl.getAttribute("data-category"); + // category needs to support non ASCII characters + const category = decodeURIComponent( + atob(categoryEl.getAttribute("data-category")) + ); categoryEl.onclick = () => { activateCategory(category); setCategoryHash(category); @@ -208,7 +215,9 @@ function activateCategory(category) { // Activate this category const categoryEl = window.document.querySelector( - `.quarto-listing-category .category[data-category='${category}'` + `.quarto-listing-category .category[data-category='${btoa( + encodeURIComponent(category) + )}']` ); if (categoryEl) { categoryEl.classList.add("active"); @@ -231,7 +240,9 @@ function filterListingCategory(category) { list.filter(function (item) { const itemValues = item.values(); if (itemValues.categories !== null) { - const categories = itemValues.categories.split(","); + const categories = decodeURIComponent( + atob(itemValues.categories) + ).split(","); return categories.includes(category); } else { return false; diff --git a/docs/site_libs/quarto-nav/headroom.min.js b/docs/site_libs/quarto-nav/headroom.min.js new file mode 100644 index 0000000..b08f1df --- /dev/null +++ b/docs/site_libs/quarto-nav/headroom.min.js @@ -0,0 +1,7 @@ +/*! + * headroom.js v0.12.0 - Give your page some headroom. Hide your header until you need it + * Copyright (c) 2020 Nick Williams - http://wicky.nillia.ms/headroom.js + * License: MIT + */ + +!function(t,n){"object"==typeof exports&&"undefined"!=typeof module?module.exports=n():"function"==typeof define&&define.amd?define(n):(t=t||self).Headroom=n()}(this,function(){"use strict";function t(){return"undefined"!=typeof window}function d(t){return function(t){return t&&t.document&&function(t){return 9===t.nodeType}(t.document)}(t)?function(t){var n=t.document,o=n.body,s=n.documentElement;return{scrollHeight:function(){return Math.max(o.scrollHeight,s.scrollHeight,o.offsetHeight,s.offsetHeight,o.clientHeight,s.clientHeight)},height:function(){return t.innerHeight||s.clientHeight||o.clientHeight},scrollY:function(){return void 0!==t.pageYOffset?t.pageYOffset:(s||o.parentNode||o).scrollTop}}}(t):function(t){return{scrollHeight:function(){return Math.max(t.scrollHeight,t.offsetHeight,t.clientHeight)},height:function(){return Math.max(t.offsetHeight,t.clientHeight)},scrollY:function(){return t.scrollTop}}}(t)}function n(t,s,e){var n,o=function(){var n=!1;try{var t={get passive(){n=!0}};window.addEventListener("test",t,t),window.removeEventListener("test",t,t)}catch(t){n=!1}return n}(),i=!1,r=d(t),l=r.scrollY(),a={};function c(){var t=Math.round(r.scrollY()),n=r.height(),o=r.scrollHeight();a.scrollY=t,a.lastScrollY=l,a.direction=ls.tolerance[a.direction],e(a),l=t,i=!1}function h(){i||(i=!0,n=requestAnimationFrame(c))}var u=!!o&&{passive:!0,capture:!1};return t.addEventListener("scroll",h,u),c(),{destroy:function(){cancelAnimationFrame(n),t.removeEventListener("scroll",h,u)}}}function o(t){return t===Object(t)?t:{down:t,up:t}}function s(t,n){n=n||{},Object.assign(this,s.options,n),this.classes=Object.assign({},s.options.classes,n.classes),this.elem=t,this.tolerance=o(this.tolerance),this.offset=o(this.offset),this.initialised=!1,this.frozen=!1}return s.prototype={constructor:s,init:function(){return s.cutsTheMustard&&!this.initialised&&(this.addClass("initial"),this.initialised=!0,setTimeout(function(t){t.scrollTracker=n(t.scroller,{offset:t.offset,tolerance:t.tolerance},t.update.bind(t))},100,this)),this},destroy:function(){this.initialised=!1,Object.keys(this.classes).forEach(this.removeClass,this),this.scrollTracker.destroy()},unpin:function(){!this.hasClass("pinned")&&this.hasClass("unpinned")||(this.addClass("unpinned"),this.removeClass("pinned"),this.onUnpin&&this.onUnpin.call(this))},pin:function(){this.hasClass("unpinned")&&(this.addClass("pinned"),this.removeClass("unpinned"),this.onPin&&this.onPin.call(this))},freeze:function(){this.frozen=!0,this.addClass("frozen")},unfreeze:function(){this.frozen=!1,this.removeClass("frozen")},top:function(){this.hasClass("top")||(this.addClass("top"),this.removeClass("notTop"),this.onTop&&this.onTop.call(this))},notTop:function(){this.hasClass("notTop")||(this.addClass("notTop"),this.removeClass("top"),this.onNotTop&&this.onNotTop.call(this))},bottom:function(){this.hasClass("bottom")||(this.addClass("bottom"),this.removeClass("notBottom"),this.onBottom&&this.onBottom.call(this))},notBottom:function(){this.hasClass("notBottom")||(this.addClass("notBottom"),this.removeClass("bottom"),this.onNotBottom&&this.onNotBottom.call(this))},shouldUnpin:function(t){return"down"===t.direction&&!t.top&&t.toleranceExceeded},shouldPin:function(t){return"up"===t.direction&&t.toleranceExceeded||t.top},addClass:function(t){this.elem.classList.add.apply(this.elem.classList,this.classes[t].split(" "))},removeClass:function(t){this.elem.classList.remove.apply(this.elem.classList,this.classes[t].split(" "))},hasClass:function(t){return this.classes[t].split(" ").every(function(t){return this.classList.contains(t)},this.elem)},update:function(t){t.isOutOfBounds||!0!==this.frozen&&(t.top?this.top():this.notTop(),t.bottom?this.bottom():this.notBottom(),this.shouldUnpin(t)?this.unpin():this.shouldPin(t)&&this.pin())}},s.options={tolerance:{up:0,down:0},offset:0,scroller:t()?window:null,classes:{frozen:"headroom--frozen",pinned:"headroom--pinned",unpinned:"headroom--unpinned",top:"headroom--top",notTop:"headroom--not-top",bottom:"headroom--bottom",notBottom:"headroom--not-bottom",initial:"headroom"}},s.cutsTheMustard=!!(t()&&function(){}.bind&&"classList"in document.documentElement&&Object.assign&&Object.keys&&requestAnimationFrame),s}); diff --git a/docs/sitemap.xml b/docs/sitemap.xml deleted file mode 100644 index ca95a41..0000000 --- a/docs/sitemap.xml +++ /dev/null @@ -1,39 +0,0 @@ - - - - https://juliahealth.org/JuliaHealthBlog/about.html - 2024-09-25T00:04:50.342Z - - - https://juliahealth.org/JuliaHealthBlog/posts/mounika-gsoc-mentor/index.html - 2025-02-07T22:16:46.351Z - - - https://juliahealth.org/JuliaHealthBlog/posts/JZubik-gsoc/GSoC_Jan_Zubik_MedPipe3D.html - 2025-02-07T22:16:46.332Z - - - https://juliahealth.org/JuliaHealthBlog/posts/ryan-gsoc/Ryan_GSOC.html - 2024-09-07T18:30:55.137Z - - - https://juliahealth.org/JuliaHealthBlog/posts/dummy/index.html - 2024-09-07T18:30:55.130Z - - - https://juliahealth.org/JuliaHealthBlog/posts/michela-gsoc/Michela_JSoC.html - 2024-09-07T18:30:55.130Z - - - https://juliahealth.org/JuliaHealthBlog/posts/jay-gsoc/gsoc-2024-fellows.html - 2025-02-07T22:18:18.242Z - - - https://juliahealth.org/JuliaHealthBlog/posts/divyansh-gsoc/gsoc-2024-fellows.html - 2025-02-07T22:16:46.339Z - - - https://juliahealth.org/JuliaHealthBlog/index.html - 2024-09-07T18:30:55.130Z - - diff --git a/docs/styles.css b/docs/styles.css index 2ddf50c..c6737b1 100644 --- a/docs/styles.css +++ b/docs/styles.css @@ -1 +1,9 @@ /* css styles */ + +.home-page-package-card:hover { + transition: all 300ms linear; +} + +.home-page-package-card:hover { + border: 1px solid var(--bs-primary); +} \ No newline at end of file diff --git a/ieee-with-url.csl b/ieee-with-url.csl deleted file mode 100644 index eae93a5..0000000 --- a/ieee-with-url.csl +++ /dev/null @@ -1,434 +0,0 @@ - - diff --git a/index.qmd b/index.qmd index 6eb10f1..0130dfd 100644 --- a/index.qmd +++ b/index.qmd @@ -1,17 +1,206 @@ ---- -listing: - contents: posts - sort: "date desc" - fields: [image, date, title, author, reading-time, description] - type: default - categories: true - sort-ui: true - filter-ui: false - feed: true - image-height: "0" -page-layout: full -toc: false -title-block-banner: true ---- +```{=html} +
    + JuliaHealth Logo +

    + Transforming Health Research! +

    +

    + Improving medicine, health and bio-medical research using the power of Julia. +

    +
    +``` -# Welcome to the JuliaHealthBlog! πŸ‘‹ +```{=html} +
    +

    Explore Packages...

    +

    Below are some of the powerful packages developed by the community.

    +
    +
    +
    + Card title +

    + This is a wider card with supporting text below as a natural lead-in to additional + content. This content is a little bit longer. +

    +

    Last updated 3 mins ago

    +
    +
    +
    +
    + Card title +

    + This is a wider card with supporting text below as a natural lead-in to additional + content. This content is a little bit longer. +

    +

    Last updated 3 mins ago

    +
    +
    +
    +
    + Card title +

    + This is a wider card with supporting text below as a natural lead-in to additional + content. This content is a little bit longer. +

    +

    Last updated 3 mins ago

    +
    +
    +
    +
    + Card title +

    + This is a wider card with supporting text below as a natural lead-in to additional + content. This content is a little bit longer. +

    +

    Last updated 3 mins ago

    +
    +
    +
    +
    + Card title +

    + This is a wider card with supporting text below as a natural lead-in to additional + content. This content is a little bit longer. +

    +

    Last updated 3 mins ago

    +
    +
    +
    +
    +

    + + And many more... + +

    +
    +
    +
    +
    +``` + +```{=html} +
    +

    Similar Organizations

    +

    We are not the only one, there are other communities researching on various similar aspects.

    +
    +
    +
    + Card title +

    + This is a wider card with supporting text below as a natural lead-in to additional + content. This content is a little bit longer. +

    +

    Last updated 3 mins ago

    +
    +
    +
    +
    + Card title +

    + This is a wider card with supporting text below as a natural lead-in to additional + content. This content is a little bit longer. +

    +

    Last updated 3 mins ago

    +
    +
    +
    +
    + Card title +

    + This is a wider card with supporting text below as a natural lead-in to additional + content. This content is a little bit longer. +

    +

    Last updated 3 mins ago

    +
    +
    +
    +
    +

    + + And many more... + +

    +
    +
    +
    +
    +``` + +```{=html} +
    +

    FAQs

    +
    +
    + + + +
    +
    + This is the first item's accordion body. It is shown by default, + until the collapse plugin adds the appropriate classes that we use to style each + element. These classes control the overall appearance, as well as the showing and + hiding via CSS transitions. You can modify any of this with custom CSS or overriding + our default variables. It's also worth noting that just about any HTML can go within + the .accordion-body, though the transition does limit overflow. +
    +
    +
    +
    + + + +
    +
    + This is the second item's accordion body. It is hidden by default, + until the collapse plugin adds the appropriate classes that we use to style each + element. These classes control the overall appearance, as well as the showing and + hiding via CSS transitions. You can modify any of this with custom CSS or overriding + our default variables. It's also worth noting that just about any HTML can go within + the .accordion-body, though the transition does limit overflow. +
    +
    +
    +
    + + + +
    +
    + This is the third item's accordion body. It is hidden by default, + until the collapse plugin adds the appropriate classes that we use to style each + element. These classes control the overall appearance, as well as the showing and + hiding via CSS transitions. You can modify any of this with custom CSS or overriding + our default variables. It's also worth noting that just about any HTML can go within + the .accordion-body, though the transition does limit overflow. +
    +
    +
    +
    +``` diff --git a/pages/connect_with_us.qmd b/pages/connect_with_us.qmd new file mode 100644 index 0000000..3b33358 --- /dev/null +++ b/pages/connect_with_us.qmd @@ -0,0 +1,11 @@ +--- +title: "Connect With Us" +--- + +Visit us on GitHub: [https://github.com/JuliaHealth](https://github.com/JuliaHealth) + +Post in the [Biology, Health, and Medicine category](https://discourse.julialang.org/c/domain/bio/15) on Discourse. + +Join us in the `#biology-health-and-medicine` stream on [Zulip](https://julialang.zulipchat.com). + +Chat with us in the `#health-and-medicine` channel on [Slack](https://julialang.slack.com). (Get a Slack invite [here](https://julialang.org/slack/).) \ No newline at end of file diff --git a/pages/meeting_notes.qmd b/pages/meeting_notes.qmd new file mode 100644 index 0000000..b988859 --- /dev/null +++ b/pages/meeting_notes.qmd @@ -0,0 +1,1447 @@ +--- +title: "Meeting Notes" +--- + +These are the public notes for the JuliaHealth Community. +Notes are published publicly here and are available for comments and review on the [public HackMD](https://hackmd.io/@AQm1lp9PSPyir6IoTPAZeQ/SJNu_d3uh). +Additionally, the notes are [hosted publicly on the GitHub](https://github.com/JuliaHealth/juliahealth.github.io/blob/dev/meeting-notes.md) and are open for PRs or edits as needed + +# February 29 2024 + +## Meeting Summary (Americas/Europe/Africa Specific) + +**In Attendance:** Jacob Zelko, Anshul Singhvi, Adam WysokiΕ„ski, Aurora Rossi, Dan Getz, Luna Fazio, Jay Landge, Edwin Mkwanazi, Alice Piller, Thembi Ndimande, Siyabonga Nxumalo, Hlengiwe, Muhammad Mahmoud, Jan Zubik, Sfundo Khumalo, Carlos Castillo Passi, Ram Samarth, Dina Khalid + +**Location:** Virtual (Northeastern University Zoom) + +**Summary:** Introducing new JuliaHealth projects, JuliaHealth blog, Google Summer of Code, and planning a JuliaHealth Day + +**Keywords:** #juliahealth #meeting #americas #africa #europe #neuro #imaging #gsoc #planning + +## Meeting Outcomes + +### Short-Term Outcomes + +* Jacob follows up with Carlos and Boris about synthetic MRI generation + +### Long-Term Outcomes + + + +## Notes + +1. Announcements: + * Meeting recording logistics + +2. New member introductions + * Luna Fazio + * Statistics PhD + * Coming back to epidemiology + * Coming back to health roots + * Adam WysokiΕ„ski + * Creator of NeuroAnalyzer.jl + * Psychiatrist + * Many different modalities of research + * Aurora Rossi + * Functional MRI + * PhD student + * Alice Piller + * Applying Julia in bioinformatics + * Edwin Mkwanazi + * Julia in clinical trials + * Learn more about how to implement more in Julia + * Carlos Castillo + * Creator of KomaMRI + * PhD student + +3. New contributor round-up! + * KomaMRI + * NeuroAnalyzer Adam Wysokinski + +4. JuliaHealth News + * Northeastern University RISE Conference + * A JuliaHealth Blog?!?!? + +5. Task Follow-ups + * Jacob follows up with Carlos and Boris about synthetic MRI generation + +6. GSoC + JuliaHealth + * Projects + * Important dates + * Open discussion + +7. Brainstorming a JuliaHealth Day + * JuliaHealth is growing rapidly!!! + * Might be confusing about where to go/get started + * Three core ares + * Luna + * Had a mixture of working with different data + * Public health approach + * I as a doctor want to predict for patients + * Perhaps it would be interesting to see what problems they have + * Possible approaches + * Jan + * More about pipelines + * What's their strength in practice + * Seeing pipelines in action + * Ram + * How is Julia being used in health already? + +8. Glass Notebooks + * Created by Dale Black + * Link: https://glassnotebook.io + +9. Upcoming and ongoing research opportunities + * Observational Health Research at Northeastern Uni + +10. Upcoming Events + * JuliaCon 2024 + +11. Open Discussion + +# January 25 2024 + +## Meeting Summary (Americas/Europe/Africa Specific) + +**In Attendance:** Jay Sanjay, Abhirath Anand, Carlos Castillo, Boris Enrique, Jacob Zelko + +**Location:** Virtual (JuliaHealth Google Meet) + +**Summary:** Medical imagining, fairness and health equity in observational health, and dashboards! + +**Keywords:** #juliahealth #meeting #americas #africa #europe #fairness #koma #fairness #dashboards + +## Meeting Outcomes + +### Short-Term Outcomes + +- Jacob follows up with Carlos and Boris about synthetic MRI generation + - Pulls in Jakub and Zachary to discussion + +### Long-Term Outcomes + + + +## Notes + +1. New member introductions + + * Carlos Castillo + + - King's College London + + - PhD student + + * Abhirath Anand + + - Final year undergraduate + - Curious about getting more into life sciences + - Biology and healthcare + +2. Announcements: + + * New meeting times + * Last Thursday of every month at 12PM EST + + * Why two separate meetings? + * One for Asia/Oceania + * Thanks Jay Sanjay for running this!!! + * One for Americas/Africa/Europe + * Trying to improve accessibility and inclusion + + * Meeting recordings + * Going forward, meetings will be recorded + * Added to a playlist on Julia YouTube page + +3. New contributor round-up! + + * Nothing this meeting + +4. Running tasks follow-ups: + + * Nothing this meeting + +5. Presentation by Carlos Castillo Passi on GSoC projects on medical imaging. + + - Written using CuDA + - Doing MRI simulation very quickly + - Can be used for machine learning overview + - Built around several packages with MRI + - Incredible work with coverage + - Super friendly GUI + - Bloch equations are hard to understand + - GSoC Project + - Trying to do actual kernel programming + - KernelAbstractions.jl + - Solving DifferentialEquations.jl + - Boost speed + - Implement new algorithms + - Suggested skills + - Experience with Julia + - MRI concepts + - GPU programming + - Goals: + - New Bloch kernel methods + - Further tests on build kite/GPU testing + - Documentation + +6. Fairness and health equity within Observational Health Research + + * Assessing phenotype fairness + + * Forthcoming package + + * Work done so far + * Paper reference: https://arxiv.org/pdf/2203.05174.pdf + +7. Creating dashboards for JuliaHealth + + * Announcement from Genie.jl + + * Custom dashboard components + * Question: What would this look like for JuliaHealth? + * Create a standard interface across JuliaHealth packages + * Can interface with a JuliaHealthDashboards package + * HealthDashboard.jl? + * Custom components for the general JuliaHealth ecosystem could be housed in package + * Researchers can easily build together commonly used health dashboards + +8. Event Reminders + + * Google Summer of Code + + * JuliaCon 2024 + +9. Upcoming and ongoing research opportunities + + * Observational Health Research at Northeastern Uni + + * Glass Notebooks from Dale Black (Not Discussed; saved for next month) + + +# January 20 2024 + +## Meeting Summary (Oceania/Asia specific) + +**In Attendance:** Jay Sanjay, Abhirath Anand, Jacob Zelko + +**Location:** Virtual (JuliaHealth Google Meet) + +**Summary:** Overview of the Oceania/Asia specific JuliaHealth monthly meeting + +**Keywords:** #juliahealth #meeting #asia #oceania #llms #beginner + +## Meeting Outcomes + +### Short-Term Outcomes + + + +### Long-Term Outcomes + + + +## Meeting Notes + + +- Abhirath Anand + + * Final year CS student in India. + + * Former GSoCer. + + + Worked on MetalHead.jl + + + No longer quite interested in Computer CV + + * Interested in JuliaHealth. + +- People excited about separate JuliaHealth meeting + + * Grown to a separate JuliaHealth meeting for Oceania/Asia specific times. + + * Wanted more people to join . + +- Different packages and ideas + + * JuliaHealthLLMs + + + How can we use LLMs for JuliaHealth? + +- How to get started with JuliaHealth - Abhirath + + * Medical imaging looks well-aligned but want to explore some different. + + * What is the observational health subecosystem? + + + Go through documentation of JuliaHealth. + + + Jay can send some. + + + + + +# December 15 2023 + +**In Attendance:** Jacob Zelko, Jay Sanjay, Jakub Mitura, Zach Christensen, Divital coder + +**Location:** Virtual (JuliaHealth Google Meet) + +**Summary:** JuliaHealth full year review, Dicsussions on the upcoming GSoC projects in JuliaHealth. + +**Keywords:** #medical #imaging #gsoc #ohdsi #newyear #observationalHealth + +## Agenda + + + +1. New member introductions +2. New contributor round-up! +3. Running tasks follow-ups: +5. State of the JuliaHealth community discussion + * Talking about the different aspects of the JuliaHealth community + * Mapping the JuliaHealth community + * Accomplishments throughout the year + * JuliaCon 2023 + * GSoC + * Publications/etc. + * Open Problems and ongoing work + * Technical problems + * Making JuliaHealth more accessible for all + * Future goals for the JuliaHealth ecosystem + * Open discussion +6. JuliaCon 2024! +7. Google Summer of Code Discussion + * What it is + * Proposed projects and ideas + * Open discussion +8. Calls for collaboration +9. Open discussion + +## Meeting Outcomes + +### Short-Term Outcomes + * Jacob follows-up with Zach. + + +### Long-Term Outcomes + * Increasing code ownership. + + +## Notes + +1. Introductions + * Divital coder + - Aspiring contributor for the 2024 Julia Organization. +3. Contributor Round-Up + * Shout outs to Farreeda for working on JuliaHealth Observational Health Sub-ecosystem Juliacon proceddings paper. + * Shout outs to Jay-Sanjay for tagging new release of OMOPCDMCohortCreator. +5. State of the JuliaHealth community discussion + * Talking about the different aspects of the JuliaHealth community + * Mapping the JuliaHealth community + * Accomplishments throughout the year + * JuliaCon 2023 + * Birds of Feather: Julia for Health and Medicine – Dilum Aluthge, Jacob Zelko + + 100 Million Patients: Julia for international Health studies + * First ever JuliaHealth GSoC fellow - Fareeda Abdelazeez + * ODHSI Global Symposium 2023 + * Open Problems and ongoing work + * Technical problems + * Making JuliaHealth more accessible for all + * Future goals for the JuliaHealth ecosystem + * Expanding the OMOPCDM for hospital price transparency and transparency coverage. + * Open discussion + * Open discussion on standards across JuliaHealth + * Zach happy to support and think around this + * Schedule one-off discussion + * Making juliahealth calls more Europe+asia/pacific friendly. Suggestions to have a one meet each for american time zone separate and one for asia/pacific time zone +6. JuliaCon 2024! + * Proposal-a-thon +8. Google Summer of Code Discussion + * What is GSoC/JSoC ? + * Proposed projects and ideas + * MedPipe3D + * Loading medical imaging data + * Modeling perspective most generally developed + * Super-voxels image mapping + * Edge matching; can make this code within Julia vs. Cpp + * Display borders of images + * Integrate segmentation like rotations recalling gamma. + * Add basic post-processing like largest corrected components. + * Add patch based data loading with probabilistic oversampling. + * Open discussion +9. Calls for collaboration +10. Open discussion + * JuliaCon 2024 and Proposal-a-thon + * Addressing the β€œParadox of Composition” + + + +# October 27 2023 + +**In Attendance:** Jakub Mitura, Phil Vernes, Jay Sanjay + +**Location:** Virtual (JuliaHealth Google Meet) + +**Summary:** Jakub Mitura presented on his work for MedEval3D, discussion on medical imaging, debrief from the OHDSI Symposium, and some initial conversation about GSoC + +**Keywords:** #medical #imaging #gsoc #ohdsi + +## Agenda + +1. New member introductions +2. New contributor round-up! +3. Running tasks follow-ups: + * Short-term task follow-ups: + * Jacob shares info on waste water management + viral load information + * Long-term task follow-ups: + * Creating a template repository +4. Presentation by Jakub Mitura on sub-ecosystem he created for working with CT, PET, and other medical imaging types of data. +5. Debrief from OHDSI Symposium (Observational Health research venue) +6. Google Summer of Code Project Discussion + * JuliaHealth documentation improvement + * Observational Health Tooling improvements and discussion + * Visualization tools +7. Upcoming and ongoing research opportunities + * Call for collaboration on using JuliaHealth observational health tools for multi-site study +8. Medical Imaging Extension for Real World Evidence exploration +8. Open discussion + +## Meeting Outcomes + +### Short-Term Outcomes + +- Jacob intro's Phil and Jakub +- Jacob follows-up with Phil + +### Long-Term Outcomes + +- Create a template repository for JuliaHealth + +## Notes + +- New member introductions + - Phil Vernes + - Works at JuliaHub + - Developing platform for running Julia jobs + - Many people at JuliaHub using tools within epi + - Can solve many problems in DSL + - Jay Sanjay + - Started contributing to the JuliaHealth ecosystem + - Looking forward to collaborating +- Running tasks follow-ups: + - Short-term task follow-ups: + - Jacob shares info on waste water management + viral load information + - Long-term task follow-ups: + - Creating a template repository + - We need to have a data structure to hold metadata (DICOM, NIFTI, etc.) + - JuliaNeuro + - HDF5 for long-term storage + - Would be great to see everyone using this + - To work on this to bring this together + - Multiple packages could have same +- Presentation by Jakub Mitura on sub-ecosystem he created for working with CT, PET, and other medical imaging types of data. + - Created three packages + - Mainly talking about MedEye3D + - Segment data and iterate to see what is going on + - Wanted to create tools for everything around model creation + - Wanted to make a viewer that is well-suited for the Julia ecosystem + - Most medical viewers are quite "old" + - Not really dynamic + - Hard to show changes within run-time + - Easy to get big increase in Julia + - Usually something like 10x's faster + - We do not yet standardize way to load data + - Metadata is saved to HDF5 format + - Can introduce dynamic annotations + - Can have layers and switch on and switch layers + - Can annotate for saying where is the problem in the viewer + - Viewer can dynamically update + - Questions + - Tested some semi-automatic algorithms + - Do evaluate repeat + - Makes it faster for evaluation and reviewing of medical images + - Depends on OpenGL and NVIDIA drivers + - Working on Docker container that keeps + - What segmentation algorithm? Approach? + - Based on Gaussian probability distributions + - Some relaxation applied + - Based mainly on the units and different kinds + - Becoming more interested in transformers + - Implemented in JAX but want to bring it into Julia + - Segmentation for bladder cancer in image analysis + - Restarted work recently in Julia + - Would be useful for others? + - New segmentation for other ecosystem within Julia +- Upcoming and ongoing research opportunities +- Call for collaboration on using JuliaHealth observational health tools for multi-site study +- Medical Imaging Extension for Real World Evidence exploration + - Idea was to implement package for medical imaging + - Pillars + - Computing statistics across medical imaging + - Complete datasets for experimenting + - Feature segmentation and scanning + - Align probabilistic model between different scans + - Become easier for physicians + - ML model for complex models for image segmentation + - Thing to consider -- need more robustness for image alignment? + - Some transformations are relatively easier to repair + - Elastic deformations + +# September 29 2023 + +**In Attendance:** Tiem van der Deure, Scott Jones, dx/dt + +**Location:** Virtual (JuliaHealth Google Meet) + +**Summary:** Discussion on viral load found in wastewater, GSoD for this fall/GSoC for next summer, and upcoming research opportunities and events + +**Keywords:** #databases #wastewater #interfaces #gsoc #ohdsi + +## Agenda + +1. New member introductions + +2. Running tasks follow-ups: + + a. Short-term task follow-ups: + + b. Long-term task follow-ups: + + i. Creating a template repository + +3. Infectious Disease load for various sewage water data + +4. Upcoming research opportunities and events + + a. Not too early to start thinking about GSoC + + b. Julia and OHDSI Symposium + +5. Open discussion + +## Meeting Outcomes + +### Short-Term Outcomes + +- Jacob shares info on waste water management + viral load information + +### Long-Term Outcomes + + + +## Notes + +- New member introductions + - Tiem van der Deure + - University of Copenhagen PhD + - Vector-borne Disease Modeling + - Epidemiological modeling and climate effects on health + - Rafael Schoueten + - Scott Jones + - Heavily involved in healthcare IT + - dx/dt + +- Google Summer of Code + - Didn't know it existed + - Google Season of Docs is great too + - Best for long-term maintenance + - In the Julia docs ecosystem is kinda a mess + +- OHDSI + Julia + - How difficult it has been to work with EHR from EPIC + - Still a bit manual but getting better + - Turing modeling "making them work" + - Getting them to run + - Making it run fast enough + - Much easier to use but not as fast as otherwise + - Extremely mathy very fast + +- Sewage water information for disease population estimations + - Weekly excerpt + - Infectious disease doctor + - Would be really neat to make some kind of app + - To check wastewater + - Propensity of viruses in ER + - Physician testing for rough understanding of what is happening in community + - You don't just need to look for one disease, but rather multiple co-factors + - Many healthcare systems put together monitoring systems + - NHS (in UK) dismantled their monitoring systems + +# September 29 2023 + +**In Attendance:** Tiem van der Deure, Scott Jones, dx/dt + +**Location:** Virtual (JuliaHealth Google Meet) + +**Summary:** Discussion on viral load found in wastewater, GSoD for this fall/GSoC for next summer, and upcoming research opportunities and events + +**Keywords:** #databases #wastewater #interfaces #gsoc #ohdsi + +## Agenda + +1. New member introductions + +2. Running tasks follow-ups: + + 1. Short-term task follow-ups: + + 2. Long-term task follow-ups: + + - Creating a template repository + +3. Upcoming research opportunities and events + + 1. Not too early to start thinking about GSoC + + 2. Julia and OHDSI Symposium + +4. Infectious Disease load for various sewage water data + +5. Open discussion + +## Meeting Outcomes + +### Short-Term Outcomes + +- Jacob shares info on waste water management + viral load information + +## Notes + +- Introductions + - Tiem van der Deure + - University of Copenhagen PhD + - Vector-borne Disease Modeling + - Epidemiological modeling and climate effects on health + - Rafael Schoueten + - Scott Jones + - Heavily involved in healthcare IT + - dx/dt + +- Google Summer of Code + - Recently discovered by the team + - Google Season of Docs + - Best for long-term maintenance + - Significant challenge organizing in Julia docs ecosystem + +- OHDSI + Julia + - Working with EHR from EPIC is demanding + - Labour intensive albeit improving + - Turing modeling "making them work" + - Getting them to run + - Making it run fast enough + - Trade off ease-of-use for computation speed + - Requires significant mathematical ability for speed gains + +- Sewage water information for disease population estimations + - Weekly excerpt + - Infectious disease doctor + - Would be really neat to make some kind of app to check wastewater + - Propensity of viruses in ER + - Physician testing for rough understanding of what is happening in community + - Ability to look for multiple co-factors instead of just one disease + - Many healthcare systems put together monitoring systems + - NHS (in UK) dismantled their monitoring systems + +- Databases and JuliaHealth + - Show how to do the basics + - Common database errors + - How to address them + - Consider having more people working in this space? + - Not really a problem within ecosystem + - Look at drivers across all packages to see how things work in Julia ecosystem + - See how we can address issues across ecosystem + +# August 25 2023 + +**In Attendance:** Edmund Miller, Jonathan Starr, Clark Evans, Kirill Simonov, Jacob Zelko + +**Location:** Virtual (JuliaHealth Google Meet) + +**Summary:** Recap of events from the JuliaHealth BoF at JuliaCon and introduction to the NumFOCUS OSSci project + +**Keywords:** #numfocus #ossci #juliacon #bof #interoperability #databases #documentation + +## Agenda + +1. New member introductions + +2. Misc Announcements + + 1. CalciumScoring.jl -- Dale Black + + 2. Survival Analyses -- Arin Basu + + 3. Google Summer of Code Fellowship wrapping up + + 4. We are on the Julia Community Calendar! + + 5. Small updates to the JuliaHealth website + +2. Running tasks follow-ups: + + 1. Short-term task follow-ups: + + - @Jacob Set-up HackMD to take notes going forward + + - Copy and paste meeting minutes over to JuliaHealth PR to update at end of meetings + + 2. @Dilum finds out how to live stream JuliaHealth BoF + + - Long-term task follow-ups: + + 3. Creating a template repositoryΒ  + +3. Debrief from JuliaCon + + 1. Interoperability of Julia with health research ecosystems (R ) + + 2. Develop and document tutorials showcasing compositional solutions to JuliaHealth ecosystem problems + + 3. Coordinate with bigger Julia Blog to bridge between communities even better + + 4. Databases and JuliaHealth + +4. Jon Starr and NumFOCUS's OSSci Program + +5. Open discussion on next steps for the JuliaHealth community + +## Meeting Outcomes + +### Short-Term Outcomes + +- @Jacob follow-up with Jonathan about JuliaHealth + OSSci +- @Edmund let Jacob know about blog posts solving problems + +### Long-Term Outcomes + +- Support OSSci about JuliaHealth + +## Notes + +- Introductions + - Clark C. Evans + - Master cobbler of YAML + - Used to work at Prometheus Research + - Sold to IQVIA + - Worked under MechanicalRabbit Umbrella + - Developed FunSQL.jl with Kirill + - Database characterization + - Joined Tufts University CTSA + - Helping with data warehousing + - Objects to query OHDSI databases and EPIC Clarity + - Getting Pluto working + - Jonathan + - Manager for OSSci for NumFOCUS + - Goal: Mapping open source science ecosystem + - Work with Distributed Computing + - Berkeley technology + - Blocks and chains! + - Using Open Source and Science to drive research + - Edmund + - PhD Candidate at Texas Dallas + - Molecular and Cell Biology + - Functional Genomics + - Coming from JuliaCon + - Excited about Health stuff + +- Interoperability of Julia with health research ecosystems (R) + - Easiest way to interoperate is to call them directly from the command line + - Build your own executables + - Most reliable/easiest + - Database approach: + - Build table in one language + - Ingest in another + - Combining executables in one location -- use Docker? + - Can run on several different machines + - Building R packages with Julia backends is possible + + +- Develop and document tutorials showcasing compositional solutions to JuliaHealth ecosystem problems + - Competing Julia with other tutorials? + - Switching over to Julia from what? + - Why are people still not switching? + - Demonstrating the use is one way + - Obviously, one could write more posts + - But there seems to be a lot of content already -- what is missing? + - Does seem like there is two different levels of documentation + - Beginner + - Advanced + - Where are the practical means of solving problems in Julia? + +- Databases and JuliaHealth + - Show how to do the basics + - Common database errors + - How to address them + - Unclear on how to solve it; more people working in this space? + - Not really a problem within ecosystem + - Look at drivers across all packages to see how things work in Julia ecosystem + - See how we can address issues across ecosystem + +- Jonathan Starr and NumFOCUS's OSSci Program + - Getting to deep diving within Julia ecosystem + - Researchers who want to find a package that they can use and develop + - Mapping projects and people to a given tool + - Can look at map to see where packages are needed for a particular ecosystem + - Can click on and connect with researchers + - Highlighting of credit for researchers + - Starting with NumFOCUS projects + - Building out knowledge of all ongoing projects/software + - Julia is little represented right now + - How to show to funders/orgs what projects to support + - How to build support across or collaboration between groups + - Trying to stop abandonware from happening + - Attempting to build social infrastructure + - Q&A + - Tufts doing something very similar -- happy to collaborate + - How can JuliaHealth get started and involved? + - Jonathan: Send me reference page and we can get this started! + - Links: + - About: https://numfocus.org/open-source-science-initiative-ossci + - How To Join: https://opensource.science + - Map of Open Source Science (MOSS) + + +# July 28 2023 + +**In Attendance:** [Attendance Waived for In-Person Meeting + +**Location:** JuliaCon 2023 JuliaHealth Birds of a Feather + +**Summary:** New member backgrounds, problems within the Julia ecosystem related to healthcare, thoughts on addressing issues within a JuliaHealth context, and learning resources for Julia within the context of health. + +**Keywords:** #ehr #genomics #biology #interoperability #database #sql #outreach + +## Agenda + +1. Introductions and what people in the community are using Julia for in health research + +2. What is missing of painful in Julia that is needed to drive health research forward + +3. Thoughts on how to address some of these problems + +4. Open discussion and next steps for JuliaHealth + +### Short-Term Outcomes + +Not Available + +### Long-Term Outcomes + +- ACTION: Develop and document tutorials showcasing compositional solutions to JuliaHealth ecosystem problems. + +- ACTION: Establish cohesive and organized Julia Blog to guide users and highlight official blogs. + +## Meeting Notes + +- Attendee interests and background + + * Here to learn + + * From EHR development and background + + * Genie folks here to support JuliaHealth endeavors + + * Genomics research and prevention + + * Quebec Heart and Lung Institute + + * Representing PumasAI + + * Consulting group + + + Developing health research in Michigan area + + + Aggregating claims data + + + To learn what is going on in the community + + * Creator of [MetaAnalysis.jl](https://github.com/arinbasu/MetaAnalysis.jl) + + * Involved with backend of healthcare IT + + * Working on JuliaHub + + + Learning about packages that are out there + + + Here to support JuliaHealth members + + + New Zealand longitudinal child health + + + Have own secure system + + + Post-COVID syndrome + + + Computational biology + + + Sickle Cell + + + Applying some ML + +* Problems within the Julia ecosystem + + + Julia needs more database connectivity to more easily do operations research + + + Databases are a pain point and composing with other aspects of the ecosystem + + + Interoperability within Julia and other sorts of resources + + + I end up doing the bare minimum in SQL + + + Do we have RAM? + + + Can we pull this into the Julia ecosystem? + + + Crank up the RAM! But only so much scaling + + + Minimal SQL writing + + + Searchlight.jl: Julia ORM layer within + + + Is Genie like a shiny? + + + No, more of a full-stack + + + Goes beyond just visualization dashboards + + + Sequencing data + + + Equally data + + + Everyone uploads data in slightly different ways + + + Make simple ways to pull that data + + + R Conductor --> JuliaConductor? + + + Would make genomic pipelines within Julia pipelines a lot easier + + + We need to understand the underlying structures + + + One of the big pain points + + + Often to have roll your own + + + EpiR --> EpiJ? + + + Power calculators + + + Co-founder of start-up + + + Found unmet need for remote monitoring for neuotropenia + + + Non-invasive screen for neutropenia + + + Device runs Julia + + + Pain points: + + + Testability of hardware + + + LOTS of CI -- bit of a pain + + + How much repetition happens in CI + + + Part of the problem for these problems: + + + There are still going to be folks who use the same organizations + + + Overcoming inertia to do the same or similar things in Julia + + + Wrapping around Julia? + + + Bringing it into the R ecosystem + + + Leading to big impacts for callable things from R by having smaller static binaries + + + Wrapping Julia packages in R + + + N3C -- National COVID Cohort Collaborative + + + Went to many healthcare systems across the US to get COVID data + + + Shelled out to Palantir + + + Open source tools within the ecosystem + + + JuliaHub has Boeing board member + + + Trusted within security community + + + Could help in this situation + +- Thoughts on how to address some of these problems + + * Using other packages outside of Julia + + + If you have some way to wrap around it + + + Getting support + + + PythonCall.jl or RCall.jl + + + Not clear how to make this compositional + + * The paradox of compositionality + + + Blog posts go a huge ways to solving problems + + + Tutorials showing how things can be combined together + + + Promotional type material + + + Nice docs are *nice* + + * The Julia Blog itself + + + Mentions JuliaBloggers but doesn't help with guiding users to read + + + Blogs need to go on as official blogs + + + Julia Forem -- is it maintained? + + + Hook into the tags from blogs + + + Cross-posting where appropriate + + * How to learn Julia within the context of health + + + Carpentries for learning resources + +# June 30 2023 + +## Meeting Summary + +**In Attendance:** Jacob Zelko, Fareeda Abdelazeez, Zachary Christensen + +**Location:** Virtual + +**Summary:** Discussed new members, upcoming JuliaCon, JuliaHealth Birds of a Feather discussion on topics like neural decoding and OMOP tooling, managing logistics for Julia organizations, and JuliaHealth PR reviews. + +**Keywords:** #brain #imaging #neural #decoding #collaboration #community #engagement + +## Agenda + +1. New member welcomes! + +2. Planning JuliaHealth Birds of a Feather + + 1. Topics? + 2. Facilitators? + 3. Creating actionable outcomes? + +3. Open discussion on [Julia Orgs, How Do You Manage Logistics?](https://discourse.julialang.org/t/julia-orgs-how-do-you-manage-logistics/100430/11?u=thecedarprince) + +4. Misc topics + + 1. Julia for Health Informatics Research & Bridging community organizations + + 1. Open Discussion on [The Graphs Ecosystem](https://discourse.julialang.org/t/the-graphs-ecosystem/99463?u=thecedarprince) + +## Meeting Outcomes + +### Short-Term Outcomes + +- @Jacob Set-up HackMD to take notes going forward + + - Copy and paste meeting minutes over to JuliaHealth PR to update at end of meetings + +### Long-Term Outcomes + +- ACTION: Creating a template repositoryΒ  + +## Meeting Notes + +- New members: + + - Zachary Christensen + + - Neuroimaging research + + - MD/PhD + + - Trying to finish this year!!! + + - Lots of background work like in JuliaData + + - Works on making Julia interface + +- Announcement: JuliaCon about 1 month away! + + - We have our own track: biology and medicine + - Many people working on different things + +- JuliaHealth Birds of a Feather Discussion + + - Possible Topics: + + - Neural decodingΒ  + + - Inspired by MATLAB: Β  + - Sister organization: + + - OMOP Tooling for Real World Data + + - How to start collaborations? + + - Maybe grant collaborations? + + - Getting access to datasets + + - Coming up with different research questions + + - How can we integrate across the community? + + - What problem can we solve? + + - Become a community resource to point to packages + + - Don’t need to keep recreating or developing new packages + + - Packages could be applications built on top of a specific use case + - Combining old packages in new ways + +- Open discussion on [Julia Orgs, How Do You Manage Logistics?](https://discourse.julialang.org/t/julia-orgs-how-do-you-manage-logistics/100430/11?u=thecedarprince) + + - Have multiple persons part of the organizations + + - Sharing meeting documentation + + - Share Google Doc at the beginning or before a meeting in announcement + + - Publish notes on website publicly + + - PR to update the JuliaHealth website with new tab for meeting minutes + + - ACTION: Using HackMD to take notes going forward + - Copy and paste meeting minutes over to JuliaHealth PR to update at end of meetings + + - Consistent APIs for JuliaHealth + + - Initial first pass with HealthBase.jl: Β  + + - As free as possible from niche + + - Could become quickly overwhelming or run risk of bikeshedding + + - ArrayInterface is a learning example in this context + + - Light dependency package is great with a well-described APIΒ  + + - How to move forward and get momentum + + - Without it turning into a mess + + - Common ontologies: Β  + + - JuliaHealth PR Reviews + + - PR Checklist: + + - Purpose + + - Reduce cognitive load + + - JuliaHealth package forks: Β  + + - ACTION: Creating a template repositoryΒ  + +# May 26 2023 + +## Meeting Summary + +**In Attendance:** Jacob Zelko, Dilum Aluthge, Asher Wasserman, Fareeda Abdelazeez, Kyle Beggs + +**Location:** Virtual + +**Summary:** First JuliaHealth community call to meet other Julians, learn how we can galvanize the Juliahealth Community, and open discussion on paths forward + +**Keywords:** #data #analysis #hemodynamics #omop #machine #learning + +## Agenda + +1. Introductions + +2. What people are using Julia for in health research + +3. Selected topics and state within the Julia ecosystem: + + 1. Observational Health + 2. Medical Imaging + 3. Machine Learning and Health + 4. Interoperability Standards + 5. Drug Discovery + +4. Standard Interfaces + +## Meeting Outcomes + +### Short-Term Outcomes + +- @Dilum finds out how to live stream JuliaHealth BoF + +### Long-Term Outcomes + + + +## Meeting Notes + +1. Introductions + + 1. Dilum Aluthge – MD/PhD Student Brown University (BCBI), PumasAI + + 1. Julia Community Involvement + + 1. Pkg + 2. General Registry + 3. Continuous Integration + + 2. JuliaHealth and beyond + + 1. Originally created JuliaHealth to bring people together in health + 2. BioJulia folks are a great source of inspiration for packages! + + 3. Birds of a Feather!!! COME VISIT! – Friday July 28th, 4PM EST in Boston, MA! + + 2. Asher Wasserman – Astronomy PhD, Data Scientist in BioTech + + 1. Julia Community Involvement + + 1. Differential Equations + 2. One off deployments + + 3. Fareeda Abdelazeez – GSoC JuliaHealth (First GSoC Student!!!!!) + + 1. Julia Community Involvement + + 1. Observational Health tooling JuliaHealth! + + 4. Kyle Beggs – Software Engineer in small Optics company, Finishing PhD in MechE + + 1. Julia Community Involvement + + 1. PDEs + 2. Hemodynamics research focus + 3. Take advantage of these tools for imaging, segmentation + +2. What people are using Julia for in health research + + 1. Asher: Cancer patient data + + 1. PDFs and other data formatsΒ  + + 1. CDA documents + + 2. How to structure this ad hoc type of data into common data model + + 3. Developing processes to automatically make these documents useful + + 4. How do we clean the data to match actual reality + + 5. How do we make this data actionable/useful + + 6. Could match towards goals of OHDSI/observational health + + 1. Analyses at population level? + 2. Outcome propensity scores? + 3. Patient phenotype development? + + 7. Role of Julia: + + 1. Mainly as a scripting language + + 2. Supplement to a lot of SQL scripting (FunSQL discovered) + + 3. Python is generally being deployed because of software devs + + 1. How to not crash AWS, etc. + + 4. Julia deployment for risk (?) + + 5. Survival Analysis in Julia; lifelines in Python otherwise + + 2. Kyle: Vascular Surgical Planning + + 1. Unobvious on where to place graft, etc – educated guesses + + 2. Creating a tool to simulate operations + + 3. Why Julia? + + 1. Existing tools are open source but really GUI-driven + + 2. Integration across ecosystem would be even better for hemodynamics in Julia + + 3. Give a base to simulate the mechanics involved with this + + 1. JuliaFEM, etc.Β  + + 4. Mesh list methods + + 1. Point clouds + 2. Main application is within hemodynamics + + 3. Fareeda: JuliaHealth GSoC Student + + 1. Working on OMOP Common Data Model + + 2. Standard model for observational health patient data + + 3. Develop infrastructure of JuliaHealth to work with OMOP CDM data + + 1. Improve DBConnector + 2. OMOPCDMCohortCreator.jl – add tooling + 3. OHDSIAPI.jl – creating interfaces for ATHENA/ATLAS + + 4. Patient Level Prediction tooling + + 1. Using MLJ algorithms + + 2. Attempting to solve a research question + + 1. Evaluate success of package + + 5. Stretch goals: + + 1. Cohort Quality and underlying data is β€œgood” + 2. Build support for OBDC connections + + 4. Overlap with other organizations + + 1. Doesn’t happen in a vacuum + + 2. Serving as a bridge between a bridge and a community between other groups + + 3. What should be JuliaHealth? + + 1. Bringing together peopleΒ  + +3. Selected topics and state within the Julia ecosystem: + + 1. Observational Health + 2. Medical Imaging + 3. Machine Learning and Health + 4. Interoperability Standards + 5. Drug Discovery + +4. Standard Interfaces + + +June 30th, 2023 + +Attending: + +Agenda: + +1. New member welcomes! + +2. Planning JuliaHealth Birds of a Feather + + 1. Topics? + 2. Facilitators? + 3. Creating actionable outcomes? + +3. Open discussion on [Julia Orgs, How Do You Manage Logistics?](https://discourse.julialang.org/t/julia-orgs-how-do-you-manage-logistics/100430/11?u=thecedarprince) + +4. Misc topics + + 1. Julia for Health Informatics Research & Bridging community organizations + + 1. Open Discussion on [The Graphs Ecosystem](https://discourse.julialang.org/t/the-graphs-ecosystem/99463?u=thecedarprince) + +Notes:Β  + +- New members: + + - Zachary Christensen + + - Neuroimaging research + + - MD/PhD + + - Trying to finish this year!!! + + - Lots of background work like in JuliaData + + - Works on making Julia interface + +- Announcement: JuliaCon about 1 month away! + + - We have our own track: biology and medicine + - Many people working on different things + +- JuliaHealth Birds of a Feather Discussion + + - Possible Topics: + + - Neural decodingΒ  + + - Inspired by MATLAB: Β  + - Sister organization: + + - OMOP Tooling for Real World Data + + - How to start collaborations? + + - Maybe grant collaborations? + + - Getting access to datasets + + - Coming up with different research questions + + - How can we integrate across the community? + + - What problem can we solve? + + - Become a community resource to point to packages + + - Don’t need to keep recreating or developing new packages + + - Packages could be applications built on top of a specific use case + - Combining old packages in new ways + +- Open discussion on [Julia Orgs, How Do You Manage Logistics?](https://discourse.julialang.org/t/julia-orgs-how-do-you-manage-logistics/100430/11?u=thecedarprince) + + - Have multiple persons part of the organizations + + - Sharing meeting documentation + + - Share Google Doc at the beginning or before a meeting in announcement + + - Publish notes on website publicly + + - PR to update the JuliaHealth website with new tab for meeting minutes + + - ACTION: Using HackMD to take notes going forward + - Copy and paste meeting minutes over to JuliaHealth PR to update at end of meetings + + - Consistent APIs for JuliaHealth + + - Initial first pass with HealthBase.jl: Β  + + - As free as possible from niche + + - Could become quickly overwhelming or run risk of bikeshedding + + - ArrayInterface is a learning example in this context + + - Light dependency package is great with a well-described APIΒ  + + - How to move forward and get momentum + + - Without it turning into a mess + + - Common ontologies: Β  + + - JuliaHealth PR Reviews + + - PR Checklist: + + - Purpose + - Reduce cognitive load + + - JuliaHealth package forks: Β  + + - ACTION: Creating a template repositoryΒ  + diff --git a/pages/related_organizations.qmd b/pages/related_organizations.qmd new file mode 100644 index 0000000..9d811eb --- /dev/null +++ b/pages/related_organizations.qmd @@ -0,0 +1,28 @@ +--- +title: "Related Organizations" +--- + +This is a (not necessarily comprehensive) list of organizations that focus primarily on developing and maintaining open-source Julia packages related to the life sciences and health sciences. + +If you would like to add an organization to this list, please feel free to [make a pull request](https://github.com/JuliaHealth/juliahealth.github.io/blob/dev/{{fd_rpath}}). + +## Julia community organizations + +* [BioJulia](https://github.com/BioJulia) – Biology, bioinformatics, and computational biology ([website](https://biojulia.dev) | [Gitter](https://gitter.im/BioJulia/home)) +* [EcoJulia](https://github.com/EcoJulia) - Ecology ([website](https://ecojulia.github.io)) +* [JuliaHealth](https://github.com/JuliaHealth) – Medicine, health care, public health, and biomedical research ([website](https://juliahealth.org)) +* [JuliaEpi](https://github.com/JuliaEpi) – Epidemiology +* [JuliaNeuro](https://github.com/JuliaNeuro) - Neuroscience ([website](https://julianeuro.github.io)) +* [JuliaNeuroscience](https://github.com/JuliaNeuroscience) - Neuroscience +* [MagneticResonanceImaging](https://github.com/MagneticResonanceImaging) - Magnetic resonance imaging + +## Labs and research groups + +* [BCBI](https://github.com/bcbi) – Center for Biomedical Informatics at Brown University ([website](https://brown.edu/go/bcbi)) +* [Holy Lab](https://github.com/HolyLab) - Holy Lab at Washington University in St. Louis ([website](http://holylab.wustl.edu/)) +* [InPhyT](https://github.com/InPhyT) - Interdisciplinary Physics Team + +## Companies + +* [Beacon Biosignals](https://github.com/beacon-biosignals) - Intelligent brain monitoring technologies ([website](https://beacon.bio)) +* [PumasAI](https://github.com/PumasAI) - Pharmaceutical modeling and simulation ([website](https://pumas.ai)) diff --git a/partials/margin_header.html b/partials/margin_header.html deleted file mode 100644 index 4f60bda..0000000 --- a/partials/margin_header.html +++ /dev/null @@ -1,6 +0,0 @@ -Navigation & Tips: - -
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    Description of the emojis used in the title -
      -
    • πŸ“ **Action Plan**: A clear, structured plan that guides each step of the MedPipe3D pipeline.
      • -
    • 🩻 **3D Medical Images**: Medical imaging data, such as MRI scans in Nifti format.
      • -
    • πŸ“Ž **AI Model**: The initial AI model that will be trained and refined within the pipeline.
      • -
    • πŸ“‰ **Loss Function**: A function that measures the model’s performance during training, guiding the optimization process.
      • -
    • πŸ—ƒοΈ **Data Loading**: Preparation and loading of data and metadata into HDF5 format.
      • -
    • πŸ“š **Data Splitting**: Dividing data into training, validation, and test sets.
      • -
    • ♻️ **Data Augmentation**: Increasing data variability through augmentation.
      • -
    • πŸ§‘β€πŸ« **AI Training**: Using Lux.jl framework to train the AI model.
      • -
    • πŸ€– **Model**: The trained AI model that can perform tasks like segmentation on medical images.
      • -
    • πŸ‘οΈ **Data for Visualization**: Output data, such as masks and segmentations.
      • -
    • πŸ“ˆ **Performance Logs**: Logs and metrics documenting the AI's performance.
      • -
    • β€οΈβ€πŸ©Ή **Purpose of MedPipe3D**
      • -
    - - - -
    -In this post, I'd like to summarize what I did this summer and everything I learned along the way, rebuilding the MedPipe3D medical imaging pipeline. I will not start typically, but so that anyone even a novice can visualize what this project has achieved, while the latter part is intended for more experienced readers. It will be easiest to divide it into 4 steps separated by ➑️ in the title above. Each emoji stands for a different piece of pipeliner and will be described below. - -πŸ“πŸ©»πŸ“ŽπŸ“‰ **What we need from the user** - -MedPipe3D requires four essential inputs from the user to get started: a clear action plan πŸ“, 3D medical images like MRI scans 🩻, an AI model πŸ“Ž, and a loss function πŸ“‰. - -πŸ—ƒοΈπŸ“šβ™»οΈπŸ§‘β€πŸ« **The Pipeline essential AI manufacturing line** - -Following the plan πŸ“, MedPipe3D loads data, pre-processes, and organizes it πŸ—ƒοΈ. Allowing data to be easily split πŸ“š, and efficiently augmented ♻️ in many ways for learning AI πŸ§‘β€πŸ« model effectively. In the end, performing testing and post-processing for better determination of AI skills. -It's designed to transform raw medical data into a format that your AI can learn from, segmenting meaningful patterns and structures. - -πŸ€–πŸ‘οΈπŸ“ˆ **Results and Insights** - -MedPipe3D is a tool for researchers and for that, it cannot do without analysis, testing, and evaluation. The result of the pipeline is a model πŸ€– as well as data πŸ‘οΈ and logs πŸ“ˆ needed in MedEval3D that are ready for visualization and further analysis with MedEye3D. In a nutshell, it makes visualizing results easy, tumor locations or other medical features directly as masks on the scans. - -β€οΈβ€πŸ©Ή **Purpose-Driven Technology** - -MedPipe3D's mission goes beyond technology. It's about providing the tools to create AIs that support healthcare professionals in making faster, more accurate decisions, with the ultimate goal of saving lives. - -This four-part journey captures the heart of the MedPipe3D toolkit for advancing medical AI, from raw data to life-saving insight. - -## Introduction - -**MedPipe3D** is a framework created from hundreds of hours over summer vacation, thousands of lines of code, hundreds of mistakes, and most importantly the guidance of my mentor and author of all of these libraries Dr. [Jakub Mitura](https://www.linkedin.com/in/jakub-mitura-7b2013151/). -At its core, MedPipe3D combines sophisticated data handling from **MedImage** thanks to the hard work of [Divyansh Goyal](https://www.linkedin.com/in/divyansh-goyal-34654b200/). Newly developed pipeline for model training, validation, and testing with existing **MedEval3D**, and result visualization with **MedEye3D**. -Unfortunately, not all of the project's goals have been fully achieved, and thereby there is one section ➑️ too many. Hopefully not for long. My name is [Jan Zubik](https://www.linkedin.com/in/janzubik/), and I wrote this entire library from scratch, which is currently my most complex project. - -If you are a data scientist, programmer, or code enthusiast, I invite you to read the next section where I go into detail and present **version 1** of this tool in detail. - -I'm a 3rd-year student of BSc in Data Science and Machine Learning, I know that many things can be done better, expanded, debugged, and optimized. Now it just works, **but don't hesitate to write to me personally** on [LinkedIn](https://www.linkedin.com/in/janzubik/), [Julia's Slack](https://julialang.slack.com/team/U06L685B6TD) or [GitHub](https://github.com/JanZubik)! -With your comments, and direct critique **you will help me** to be a better programmer and one day MedPipe3D will contribute in a tiny way to save someone's life! - -Exact work from the Google Summer of Code project you will find in [GitHub the repository.](https://github.com/JuliaHealth/MedPipe3D.jl/tree/GSoC-'24-MedPipe3D) - - -# Project Goals - -The primary goal was to develop MedPipe3D and enhance MedImage, a Julia package designed to streamline the process of GPU-accelerated medical image segmentation. The project aimed to merge existing librariesβ€”MedEye3D, MedEval3D, and MedImageβ€”into a cohesive pipeline that facilitates advanced data handling, preprocessing, augmentation, model training, validation, testing with post-processing and visualization for medical imaging applications. - - - -# Tasks - -- πŸ†™ - Fully finished, with great potential for further development -- βœ… - Fully completed -- ⚠️ - Partially uncompleted -- ❌ - Unreached - -Full list of all major parts and minor tasks (all tasks set up in the original GSOC plan were completed at least minimum level, and many additional improvements above minimum were implemented) -
    - -1. **Helpful functions to support the MedImage format βœ…** - - Debugging rotations βœ… - - Crop MedImage or 3D array βœ… - - Pad MedImage or 3D array βœ… - - Pad with edge values βœ… - - Calculating the average of the edges of the picture πŸ†™ - -2. **Integrate Augmentations for Medical Data βœ…** - - Brightness transform βœ… - - Contrast augmentation transform βœ… - - Gamma Transform βœ… - - Gaussian noise transform βœ… - - Rician noise transform βœ… - - Mirror transform βœ… - - Scale transform πŸ†™ - - Gaussian blur transform βœ… - - Simulate low-resolution transform πŸ†™ - - Elastic deformation transform πŸ†™ - -3. **Develop a Pipeline ⚠️** - - Structured configuration of all hyperparameters πŸ†™ - - Interactive creation of configuration βœ… - - Creating a structured configuration of hyperparameters in JSON πŸ†™ - - Loading data into HDF5 βœ… - - Cropping and padding to real coordinates of the main picture βœ… - - Calculate Median and Mean Spacing with resampling πŸ†™ - - Cropping and padding to specific or average dimensions βœ… - - Standardization and normalization βœ… - - Managing index groups (channels) for batch requirements in HDF5 βœ… - - Divide into train, validation, test specified as % βœ… - - Divide with a specific division specified in JSON βœ… - - Equal distribution when there are multiple classes βœ… - - Extracting data and creating 5-dimensional tensors for batched learning βœ… - - Hole images data loading βœ… - - Patch-based data loading with probabilistic oversampling βœ… - - Obtaining the necessary elements for learning βœ… - - Get optimizer, loss function, and performance metrics βœ… - - Apply augmentations βœ… - - Train βœ… - - Initializing model βœ… - - The learning epoch βœ… - - Epoch with early stopping functionality βœ… - - Inferring βœ… - - Validation βœ… - - Evaluate metric βœ… - - Evaluate validation loss βœ… - - Validation with largest connected componentβœ… - - Testing βœ… - - Evaluate test set βœ… - - Invertible augmentations evaluation βœ… - - Patch-based invertible augmentations evaluation βœ… - - Logging ⚠️ - - Returning the necessary results ⚠️ - - Logging connection to TensorBoard ❌ - - Logging errors and warnings ❌ - - Visualization ⚠️ - - Returning data in Nifti format βœ… - - Automated visualization in MedEye3D ❌ - -4. **Optimize Performance with GPU Acceleration** - - Augmentations βœ… - - Learning, Validation, Testing βœ… - - Largest connected component βœ… - -5. **Documentation ⚠️** - - Comments in important places in the code ⚠️ - - Documentation of the function ⚠️ - - Read me ⚠️ - - Documentation on juliahealth.org ❌ - -
    - -## Integrate augmentations for medical data πŸ†™ -Augmenting medical data is a crucial step for enhancing model robustness, especially given the variations in imaging conditions and patient anatomy. - -- This pipeline currently supports multiple augmentation techniques: - - Brightness transform βœ… - - Contrast augmentation transform βœ… - - Gamma Transform βœ… - - Gaussian noise transform βœ… - - Rician noise transform βœ… - - Mirror transform βœ… - - Scale transform πŸ†™ - - Gaussian blur transform βœ… - - Simulate low-resolution transform πŸ†™ - - Elastic deformation transform πŸ†™ - -Which have been fully integrated. Each of these methods helps the model generalize better by simulating diverse imaging scenarios. - -![](./Augmentations.png) - -Comments: - -Augmentations such as scaling, and low-resolution simulation use interpolation that is not yet GPU-accelerated. - -Elastic deformation with simulation of different tissue elasticities is a potential development opportunity that would further improve the model's adaptability by mimicking more complex variations found in medical imaging. - -## Invertible augmentations and support test time augmentations πŸ†™ -This section focuses on the ability to apply reversible augmentations to test data, allowing the model to be evaluated with different transformations. Only rotation is available at this time. The function `evaluate_patches` performs this evaluation by applying specified augmentations, dividing the test data into patches, and reconstructing the full image from the patches. During testing, one can choose to use of largest connected component post-processing. Metrics are calculated and results are saved for analysis. - -
    -evaluate_test: - -```julia -# ... -for test_group in test_groups - test_data, test_label, attributes = fetch_and_preprocess_data([test_group], h5, config) - results, test_metrics = evaluate_patches(test_data, test_label, tstate, model, config) - y_pred, metr = process_results(results, test_metrics, config) - save_results(y_pred, attributes, config) - push!(all_test_metrics, metr) -end -# ... -``` - -```julia -function evaluate_patches(test_data, test_label, tstate, model, config, axis, angle) - println("Evaluating patches...") - results = [] - test_metrics = [] - tstates = [tstate] - test_time_augs = [] - - for i in config["learning"]["n_invertible"] - data = rotate_mi(test_data, axis, angle) - for tstate_curr in tstates - patch_results = [] - patch_size = Tuple(config["learning"]["patch_size"]) - idx_and_patches, paded_data_size = divide_into_patches(test_data, patch_size) - coordinates = [patch[1] for patch in idx_and_patches] - patch_data = [patch[2] for patch in idx_and_patches] - for patch in patch_data - y_pred_patch, _ = infer_model(tstate_curr, model, patch) - push!(patch_results, y_pred_patch) - end - idx_and_y_pred_patch = zip(coordinates, patch_results) - y_pred = recreate_image_from_patches(idx_and_y_pred_patch, paded_data_size, patch_size, size(test_data)) - if config["learning"]["largest_connected_component"] - y_pred = largest_connected_component(y_pred, config["learning"]["n_lcc"]) - end - metr = evaluate_metric(y_pred, test_label, config["learning"]["metric"]) - push!(test_metrics, metr) - end - end - return results, test_metrics -end -``` - -```julia -function divide_into_patches(image::AbstractArray{T, 5}, patch_size::Tuple{Int, Int, Int}) where T - println("Dividing image into patches...") - println("Size of the image: ", size(image)) - - # Calculate the required padding for each dimension (W, H, D) - pad_size = ( - (size(image, 1) % patch_size[1]) != 0 ? patch_size[1] - size(image, 1) % patch_size[1] : 0, - (size(image, 2) % patch_size[2]) != 0 ? patch_size[2] - size(image, 2) % patch_size[2] : 0, - (size(image, 3) % patch_size[3]) != 0 ? patch_size[3] - size(image, 3) % patch_size[3] : 0 - ) - - # Pad the image if necessary - padded_image = image - if any(pad_size .> 0) - padded_image = crop_or_pad(image, (size(image, 1) + pad_size[1], size(image, 2) + pad_size[2], size(image, 3) + pad_size[3])) - end - - # Extract patches - patches = [] - for x in 1:patch_size[1]:size(padded_image, 1) - for y in 1:patch_size[2]:size(padded_image, 2) - for z in 1:patch_size[3]:size(padded_image, 3) - patch = view( - padded_image, - x:min(x+patch_size[1]-1, size(padded_image, 1)), - y:min(y+patch_size[2]-1, size(padded_image, 2)), - z:min(z+patch_size[3]-1, size(padded_image, 3)), - :, - : - ) - push!(patches, [(x, y, z), patch]) - end - end - end - println("Size of padded image: ", size(padded_image)) - return patches, size(padded_image) -end - -function recreate_image_from_patches( - coords_with_patches, - padded_size, - patch_size, - original_size -) - println("Recreating image from patches...") - reconstructed_image = zeros(Float32, padded_size...) - - # Place patches back into their original positions - for (coords, patch) in coords_with_patches - x, y, z = coords - reconstructed_image[ - x:x+patch_size[1]-1, - y:y+patch_size[2]-1, - z:z+patch_size[3]-1, - :, - : - ] = patch - end - - # Crop the reconstructed image to remove any padding - final_image = reconstructed_image[ - 1:original_size[1], - 1:original_size[2], - 1:original_size[3], - :, - : - ] - println("Size of the final image: ", size(final_image)) - return final_image -end -``` -
    - -Comment:
    -In this section, there is significant potential to incorporate additional types of invertible augmentations. - -## Patch-based data loading with probabilistic oversampling βœ… -In this section, patches are extracted using `extract_patch` from the medical images for model training, with a probability-based method to decide between a random patch or a patch with non-zero labels. -Helper functions like `get_random_patch` and `get_centered_patch` determine the starting indices and dimensions for the patches based on given configurations, while padding methods ensure consistency even if the patch exceeds the original image dimensions. Probabilistic oversampling, as configured, allows for more balanced and informative data sampling, which improves the model's ability to detect specific medical features. - - -
    -extract_patch: - -```julia -function extract_patch(image, label, patch_size, config) - # Fetch the oversampling probability from the config - println("Extracting patch.") - oversampling_probability = config["learning"]["oversampling_probability"] - # Generate a random number to decide which patch extraction method to use - random_choice = rand() - - if random_choice <= oversampling_probability - return extract_nonzero_patch(image, label, patch_size) - else - - return get_random_patch(image, label, patch_size) - end -end -#Helper function, in case the mask is emptyClick to apply -function extract_nonzero_patch(image, label, patch_size) - println("Extracting a patch centered around a non-zero label value.") - indices = findall(x -> x != 0, label) - if isempty(indices) - # Fallback to random patch if no non-zero points are found - return get_random_patch(image, label, patch_size) - else - # Choose a random non-zero index to center the patch around - center = indices[rand(1:length(indices))] - return get_centered_patch(image, label, center, patch_size) - end -end -# Function to get a patch centered around a specific index -function get_centered_patch(image, label, center, patch_size) - center_coords = Tuple(center) - half_patch = patch_size .Γ· 2 - start_indices = center_coords .- half_patch - end_indices = start_indices .+ patch_size .- 1 - - # Calculate padding needed - pad_beg = ( - max(1 - start_indices[1], 0), - max(1 - start_indices[2], 0), - max(1 - start_indices[3], 0) - ) - pad_end = ( - max(end_indices[1] - size(image, 1), 0), - max(end_indices[2] - size(image, 2), 0), - max(end_indices[3] - size(image, 3), 0) - ) - - # Adjust start_indices and end_indices after padding - start_indices_adj = start_indices .+ pad_beg - end_indices_adj = end_indices .+ pad_beg - - # Convert padding values to integers - pad_beg = Tuple(round.(Int, pad_beg)) - pad_end = Tuple(round.(Int, pad_end)) - - # Pad the image and label using pad_mi - image_padded = pad_mi(image, pad_beg, pad_end, 0) - label_padded = pad_mi(label, pad_beg, pad_end, 0) - - # Extract the patch - image_patch = image_padded[ - start_indices_adj[1]:end_indices_adj[1], - start_indices_adj[2]:end_indices_adj[2], - start_indices_adj[3]:end_indices_adj[3] - ] - label_patch = label_padded[ - start_indices_adj[1]:end_indices_adj[1], - start_indices_adj[2]:end_indices_adj[2], - start_indices_adj[3]:end_indices_adj[3] - ] - - return image_patch, label_patch -end - -function get_random_patch(image, label, patch_size) - println("Extracting a random patch.") - # Check if the patch size is greater than the image dimensions - if any(patch_size .> size(image)) - # Calculate the needed size to fit the patch - needed_size = map(max, size(image), patch_size) - # Use crop_or_pad to ensure the image and label are at least as large as needed_size - image = crop_or_pad(image, needed_size) - label = crop_or_pad(label, needed_size) - end - - # Calculate random start indices within the new allowable range - start_x = rand(1:size(image, 1) - patch_size[1] + 1) - start_y = rand(1:size(image, 2) - patch_size[2] + 1) - start_z = rand(1:size(image, 3) - patch_size[3] + 1) - start_indices = [start_x, start_y, start_z] - end_indices = start_indices .+ patch_size .- 1 - - # Extract the patch directly when within bounds - image_patch = image[start_indices[1]:end_indices[1], start_indices[2]:end_indices[2], start_indices[3]:end_indices[3]] - label_patch = label[start_indices[1]:end_indices[1], start_indices[2]:end_indices[2], start_indices[3]:end_indices[3]] - - return image_patch, label_patch -end - -``` -
    - -## Calculate Median and Mean Spacing with resampling πŸ†™ -This part ensures that all images in the dataset have consistent real coordinates, spacing, and shape. It's a critical factor in medical imaging for accurate analysis. Calculating and applying set values, median or mean across images ensures uniformity. - -#### Resample images to target image πŸ†™ -This step aligns each image to the reference coordinates of the main image, ensuring that all images share a common spatial alignment. The `resample_to_image` function from MedImage.jl is used here, applying interpolation to adjust each image. - - -
    -resample_images_to_target: - -```julia -if resample_images_to_target && !isempty(Med_images) - println("Resampling $channel_type files in channel '$channel_folder' to the first $channel_type in the channel.") - reference_image = Med_images[1] - Med_images = [resample_to_image(reference_image, img, interpolator) for img in Med_images] -end -``` -
    - -Comment:
    -`Resample_to_image` uses interpolation that is not yet GPU-accelerated in this implementation, this step slows down the data preparation phase significantly. - -#### Ensure uniform spacing across the entire dataset πŸ†™ -This step brings all images to a consistent voxel spacing across the dataset using `resample_to_spacing` from MedImage.jl. This uniform spacing is crucial for creating a standardized dataset where each image voxel represents the same physical volume. - - -
    -esample_to_spacing: - -```julia -if resample_images_spacing == "set" - println("Resampling all $channel_type files to target spacing: $target_spacing") - target_spacing = Tuple(Float32(s) for s in target_spacing) - channels_data = [[resample_to_spacing(img, target_spacing, interpolator) for img in channel] for channel in channels_data] -elseif resample_images_spacing == "avg" - println("Calculating average spacing across all $channel_type files and resampling.") - all_spacings = [img.spacing for channel in channels_data for img in channel] - avg_spacing = Tuple(Float32(mean(s)) for s in zip(all_spacings...)) - println("Average spacing calculated: $avg_spacing") - channels_data = [[resample_to_spacing(img, avg_spacing, interpolator) for img in channel] for channel in channels_data] -elseif resample_images_spacing == "median" - println("Calculating median spacing across all $channel_type files and resampling.") - all_spacings = [img.spacing for channel in channels_data for img in channel] - median_spacing = Tuple(Float32(median(s)) for s in all_spacings) - println("Median spacing calculated: $median_spacing") - channels_data = [[resample_to_spacing(img, median_spacing, interpolator) for img in channel] for channel in channels_data] -elseif resample_images_spacing == false - println("Skipping resampling of $channel_type files.") - # No resampling will be applied, channels_data remains unchanged. -end -``` -
    - -Comment:
    -`Resample_to_spacing` uses interpolation that is not yet GPU-accelerated in this implementation, this step slows down the data preparation phase significantly. - -#### Resizing all channel files to average or target size βœ… -To create a cohesive 5D tensor, all images in each channel are resized to a uniform shape, either the average size of all images or a specific target size. This resizing process uses `crop_or_pad`, ensuring that all images match the specified dimensions, making them suitable for model input. - -
    -crop_or_pad: - -```julia -if resample_size == "avg" - sizes = [size(img.voxel_data) for img in channels_data for img in img] # Get sizes from all images - avg_dim = map(mean, zip(sizes...)) - avg_dim = Tuple(Int(round(d)) for d in avg_dim) - println("Resizing all $channel_type files to average dimension: $avg_dim") - channels_data = [[crop_or_pad(img, avg_dim) for img in channel] for channel in channels_data] -elseif resample_size != "avg" - target_dim = Tuple(resample_size) - println("Resizing all $channel_type files to target dimension: $target_dim") - channels_data = [[crop_or_pad(img, target_dim) for img in channel] for channel in channels_data] -end -``` -
    - -## Basic Post-processing operations -Post-processing operations involve the algorithm `largest_connected_components`. It is achieved by label initialization and propagation in the segmented mask. -The `initialize_labels_kernel` function assigns unique labels to different regions. - -
    -initialize_labels_kernel: - -```julia -@kernel function initialize_labels_kernel(mask, labels, width, height, depth) - idx = @index(Global, Cartesian) - i = idx[1] - j = idx[2] - k = idx[3] - - if i >= 1 && i <= width && j >= 1 && j <= height && k >= 1 && k <= depth - if mask[i, j, k] == 1 - labels[i, j, k] = i + (j - 1) * width + (k - 1) * width * height - else - labels[i, j, k] = 0 - end - end -end -``` -
    -Propagate_labels_kernel iteratively updates the labels to maintain connected regions. -propagate_labels_kernel: -
    - -```julia -@kernel function propagate_labels_kernel(mask, labels, width, height, depth) - idx= @index(Global, Cartesian) - i = idx[1] - j = idx[2] - k = idx[3] - - if i >= 1 && i <= width && j >= 1 && j <= height && k >= 1 && k <= depth - if mask[i, j, k] == 1 - current_label = labels[i, j, k] - for di in -1:1 - for dj in -1:1 - for dk in -1:1 - if di == 0 && dj == 0 && dk == 0 - continue - end - ni = i + di - nj = j + dj - nk = k + dk - if ni >= 1 && ni <= width && nj >= 1 && nj <= height && nk >= 1 && nk <= depth - if mask[ni, nj, nk] == 1 && labels[ni, nj, nk] < current_label - labels[i, j, k] = labels[ni, nj, nk] - end - end - end - end - end - end - end -end -``` -
    -This process facilitates the identification of the largest connected components in 3D space, helping to isolate relevant medical structures, such as tumors, in the segmented mask. Allowing determining how many such areas are to be returned. - -
    -largest_connected_components: - -```julia -function largest_connected_components(mask::Array{Int32, 3}, n_lcc::Int) - width, height, depth = size(mask) - mask_gpu = CuArray(mask) - labels_gpu = CUDA.fill(0, size(mask)) - dev = get_backend(labels_gpu) - ndrange = (width, height, depth) - workgroupsize = (3, 3, 3) - - # Initialize labels - initialize_labels_kernel(dev)(mask_gpu, labels_gpu, width, height, depth, ndrange = ndrange) - CUDA.synchronize() - - # Propagate labels iteratively - for _ in 1:10 - propagate_labels_kernel(dev, workgroupsize)(mask_gpu, labels_gpu, width, height, depth, ndrange = ndrange) - CUDA.synchronize() - end - - # Download labels back to CPU - labels_cpu = Array(labels_gpu) - - # Find all unique labels and their sizes - unique_labels = unique(labels_cpu) - label_sizes = [(label, count(labels_cpu .== label)) for label in unique_labels if label != 0] - - # Sort labels by size and get the top n_lcc - sort!(label_sizes, by = x -> x[2], rev = true) - top_labels = label_sizes[1:min(n_lcc, length(label_sizes))] - - # Create a mask for each of the top n_lcc components - components = [labels_cpu .== label[1] for label in top_labels] - return components -end -``` -
    - -## Structured configuration of all hyperparameters πŸ†™ - -Hyperparameters for the entire pipeline are stored in a JSON configuration file, enabling straightforward adjustments for experimentation (just swap values, save and resume the study). This structured setup allows easy modification of key parameters, such as data set preparation, training settings, data augmentation, and resampling options. - - -
    -Example configuration: - -```JSON -{ - "model": { - "patience": 10, - "early_stopping_metric": "val_loss", - "optimizer_name": "Adam", - "loss_function_name": "l1", - "early_stopping": true, - "early_stopping_min_delta": 0.01, - "optimizer_args": "lr=0.001", - "num_epochs": 10 - }, - "data": { - "batch_complete": false, - "resample_size": [200,101,49], - "resample_to_target": false, - "resample_to_spacing": false, - "batch_size": 3, - "standardization": false, - "target_spacing": null, - "channel_size": 1, - "normalization": false, - "has_mask": true - }, - "augmentation": { - "augmentations": { - "Brightness transform": { - "mode": "additive", - "value": 0.2 - } - }, - "p_rand": 0.5, - "processing_unit": "GPU", - "order": [ - "Brightness transform" - ] - }, - "learning": { - "Train_Val_Test_JSON": false, - "largest_connected_component": false, - "n_lcc": 1, - "n_folds": 3, - "invertible_augmentations": false, - "n_invertible": true, - - "class_JSON_path": false, - "additional_JSON_path": false, - "patch_size": [50,50,50], - "metric": "dice", - "n_cross_val": false, - "patch_probabilistic_oversampling": false, - "oversampling_probability": 1.0, - "test_train_validation": [ - 0.6, - 0.2, - 0.2 - ], - "shuffle": false - } -} - -``` -
    - -Comments:
    -The current configuration is loaded as a dictionary, which simplifies access and modification. This setup presents a strong foundation for integrating automated search algorithms for hyperparameter tuning, enabling more efficient model optimization.
    -The configuration structure could be reorganized and re-named to improve readability, making it easier for users to locate and adjust specific parameters. - -## Visualization of algorithm outputs ⚠️ -This module provides basic visualization functionality by saving output masks and images first to MedImage format and then to Nifti format. The `create_nii_from_medimage` function from MedImage.jl generates Nifti files, which can be loaded into MedEye3D for 3D visualization. - -Comments:
    -Integrating this visualization module more fully with the pipeline could eliminate unnecessary steps. By automatically loading output masks and images as raw data into MedEye3D for 3D visualization and supporting a more efficient end-to-end workflow. - -## K-fold cross-validation functionality βœ… -K-fold cross-validation is implemented to evaluate model performance more robustly. The data is split into multiple folds, with each fold serving as a validation set once, while the others form the training set. This functionality provides a better assessment of model performance across different subsets of the data. - -
    -K-fold cross-validation functionality: - -```julia -... - tstate = initialize_train_state(rng, model, optimizer) - if config["learning"]["n_cross_val"] - n_folds = config["learning"]["n_folds"] - all_tstate = [] - combined_indices = [indices_dict["train"]; indices_dict["validation"]] - shuffled_indices = shuffle(rng, combined_indices) - for fold in 1:n_folds - println("Starting fold $fold/$n_folds") - train_groups, validation_groups = k_fold_split(combined_indices, n_folds, fold, rng) - - tstate = initialize_train_state(rng, model, optimizer) - final_tstate = epoch_loop(num_epochs, train_groups, validation_groups, h5, model, tstate, config, loss_function, num_classes) - - push!(all_tstate, final_tstate) - end - else - final_tstate = epoch_loop(num_epochs, train_groups, validation_groups, h5, model, tstate, config, loss_function, num_classes) - end - return final_tstate -... -``` -
    - -The `k_fold_split` function organizes the indices for each fold, ensuring comprehensive coverage of the dataset during training. - -
    -k_fold_split - -```julia -function k_fold_split(data, n_folds, current_fold) - fold_size = length(data) Γ· n_folds - validation_start = (current_fold - 1) * fold_size + 1 - validation_end = validation_start + fold_size - 1 - validation_indices = data[validation_start:validation_end] - train_indices = [data[1:validation_start-1]; data[validation_end+1:end]] - return train_indices, validation_indices -end -``` -
    - -# Conclusions and Future Development -I have successfully established a foundation for a medical imaging pipeline, addressing significant challenges in data handling, model training, and augmentation integration. The integration of dataset-wide functions has significantly enhanced the reproducibility and handling of batched data with GPU support enabling scalability of experiments, making it easier for researchers and practitioners to produce better results. - -# Future Development -As we look to the future, there are several areas where MedPipe3D can be expanded and improved to better serve the medical AI community. These include: - -## Necessary Enhancements - -Comprehensive Logging: Develop detailed logging mechanisms that capture a wide range of events, including system statuses, model performance metrics, and user activities, to facilitate debugging and system optimization. This is currently executed as a simple `println` function. - -TensorBoard Integration: Implement an interface for TensorBoard to allow users to visualize training dynamics in real time, providing insights into model behavior and performance trends. - -Error and Warning Logs: Introduce advanced error and warning logging capabilities to alert users of potential issues before they affect the pipeline's performance, ensuring smoother operations and maintenance. - -Automated Visualization: Integrate MedEye3D directly into MedPipe3D to enable automated visualization of outputs, such as segmentation masks or other relevant medical imaging features. This feature would provide users with real-time visual feedback on model performance and data quality. -Code-Level Documentation: Due to needed changes in the fundamental structure of the pipeline in the final phase of the project, it is necessary to reevaluate all documentation. - -Official JuliaHealth Documentation: Extend the documentation efforts to include official entries on juliahealth.org, providing a centralized and authoritative resource for users seeking to learn more about MedPipe3D and its capabilities with examples shown - -## Potential Enhancements -GPU support for interpolation will allow for significant acceleration of such functions as Scale transform, Simulate, Low-resolution transform, Elastic deformation transform, and Resampling spacing. - -Add more reversible augmentations to test time. - -Calculating the average of the edges of the picture: checking the type of photo and calculating more correctly on this basis - -Elastic deformation transforms with the simulation of different tissue elasticities. - -# Acknowledgments πŸ™‡β€β™‚οΈ - -I would like to express my deepest gratitude to my mentor Dr. [Jakub Mitura](https://www.linkedin.com/in/jakub-mitura-7b2013151/) for his invaluable guidance and support throughout this project. His expertise and encouragement were instrumental in overcoming challenges and achieving project milestones. diff --git a/posts/_metadata.yml b/posts/_metadata.yml deleted file mode 100644 index 7af761d..0000000 --- a/posts/_metadata.yml +++ /dev/null @@ -1,24 +0,0 @@ -# options specified here will apply to all posts in this folder - -# freeze computational output -# (see https://quarto.org/docs/projects/code-execution.html#freeze) -execute: - freeze: auto - eval: true - echo: true - output: true - -comments: - utterances: - repo: JuliaHealth/JuliaHealthBlog - theme: github-dark - issue-term: title - label: Comment - -# bibliography: https://raw.githubusercontent.com/pat-alt/bib/main/bib.bib -citation: true - -crossref: - fig-prefix: Figure - tbl-prefix: Table - diff --git a/posts/divyansh-gsoc/ct_windowing.png b/posts/divyansh-gsoc/ct_windowing.png deleted file mode 100644 index cef0d3a5cdf4ed51b5d730f315a1c02d27eb0cb4..0000000000000000000000000000000000000000 GIT binary patch literal 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Motivation to develop this application was to provide to rapidly growing Julia language scientific community tool for research in three dimensional medical images. Package is based on multiple open source software packages, yet most prominent is utilization of OpenGl specification to enable GPU acceleration. Application was tested both on Linux and Windows platforms and in both cases latency observed by the user in most common interaction like scrolling, annotation and change of displayed plane was very small. Thanks to utilization of many modern packages and methodologies developed package is providing convenient visualization in rapid prototyping with medical image segmentation algorithms. Application also is easily extendable and will be included in medical image segmentation framework that is currently in development.}, - affiliation = {Medical University of Lublin; Chair and Department of Nuclear Medicine, Medical University of Lublin}, - author = {Mitura, Jakub and Chrapko, Beata E.}, - doi = {10.26348/znwwsi.25.57}, - journal = {Zeszyty Naukowe Warszawskiej WyΕΌszej SzkoΕ‚y Informatyki}, - keywords = {OpenGl; Computer Tomography; PET/CT; medical image annotation; medical image visualization}, - language = {English}, - pages = {57--67}, - title = {3D Medical Segmentation Visualization in Julia with MedEye3d}, - volume = {nr 25}, - year = {2021}, -} diff --git a/posts/divyansh-gsoc/supervoxel_rendering_fixed.png b/posts/divyansh-gsoc/supervoxel_rendering_fixed.png deleted file mode 100644 index a0df9c743ab6b82060a293c657512ece63d3f6a1..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 132685 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[**LinkedIn**](https://www.linkedin.com/in/michela-rocchetti-261793218/), [**GitHub**](https://github.com/MichelaRocchetti) - -# Project Description - -*IPUMS* is the "world's largest available single database of census microdata", providing survey and census data from around the world. -It includes several projects that provide a wide variety of datasets. -The information and data collected by *IPUMS* are useful for comparative research, as well as for the analysis of individuals in their life contexts. -These data can be used to create a more comprehensive dataset that will facilitate research on the social determinants of health for different types of diseases, social communities, and geographical areas. - -![](./IPUMS_grid_logo.png) - -> To learn more about IPUMS, visit the [website](https://www.ipums.org) - -# Tasks and Goals -The primary objectives of this proposal are to: - -1. Develop a native Julia package to interact with the APIs available around the datasets *IPUMS* provides. - -2. Provide useful utilities within this package for manipulating *IPUMS* datasets. - -3. Compose this package with the wider Julia ecosystem to enable novel research in health, economics, and more. - -To achieve this, the work was distributed as follows: - -1. Expand some of the functionality developed in `ipumsr` *IPUMS* NHGIS - - Create a link between OpenAPI documentation and the functions internally used in IPUMS.jl: - updating already present functions, determining if updating is needed, and testing them - - Develop functionality similar to the get_metadata_nghis function present in ipumsr - -2. Update *IPUMS* documentation - - Set up and deploy DocumenterVitepress.jl - - Write a blog post on how IPUMS.jl can be composed within the ecosystem. - - -# How the work was done - -The first task was to migrate documents from Documenter to DocumenterVitepress.This issue aims to support the significant refactoring underway across JuliaHealth, aimed at improving the discoverability and cohesion of the JuliaHealth ecosystem, particularly about documentation. This issue is intended to create a more attractive entry point for new Julia users interested in health research within the Julia community. -To accomplish this task, a dependency of DocumenterVitepress was added to the docs directory of the IPUMS.jl repository. -Once this was done, the Documenter.jl make.jl file was migrated into a DocumenterVitepress.jl make.jl file. Working on the make.jl file, the pages structure were added to the web page explaining the IPUMS.jl package. With this in mind, those were added: - 1. Home: to explain the main purpose of the package - 2. Workflows: to explain the working process - 3. How to: to give general information - 4. Tutorials: to show how to use IPUMS.jl - 5. Examples: some examples of activities - 6. Mission: to explain why the package is useful for the community - 7. References: references used to write the pages. - -This first task takes some time, especially setting up GitHub and cloning the repository locally. At this point, my experience with GitHub was really limited and I had to learn how to use the Git environment from scratch, for example how to do continuous integration (to commit code to a shared repository), documentation release and merge, and local testing. I found the support of my mentors and searching for material online was really helpful. - -The second task was to update the documentation of IPUMS.jl by modifying the functionality within the model folder in the IPUMS.jl folder. The main aim of this task was to -a description of the function and its attributes, an example of possible implementation and result, and finally to show how to use it. The documentation to be updated as of several types of functions: - 1. Data extract - 2. Data set - 3. Data Table - 4. Time series table - 5. Error - 6. Shapefile. - Each of these macro-categories (from 1 to 4) contains a set of functions, each signaling the different expected output and specific purpose. - Information about what each function does, and the meaning of each specific input variable, has been found on the *IPUMS* website and references have been made in the written documentation. - -# How to work with IPUMS - After writing down the description of the function and the inputs, examples were formulated, starting from the *IPUMS* website: when you register at [IPUMS](https://uma.pop.umn.edu/usa/user/new), an API key is given. -which is used, among other things, to run pre-written code on the website. This code contains examples of these functions, and these examples -have been adapted by changing some input values and adapting them to work in the Julia framework. The latter task was done by simply rewriting some structures, such as dictionaries, maps, or lists, in the -Julia language. -Here is a small guide on how to set up working with the API: -1. Create an *IPUMS* account -2. Log in to your account -3. Copy the API key, which can be obtained from the [website](https://account.ipums.org/api_keys) -4. Use the key to run the code that is already available on the [*IPUMS* Developer Portal](https://developer.ipums.org/docs/v2/reference/), where you will also find information about the variables and packages. - -# Functions testing - - A final task was to test the functions in the 'api_IPUMSAPI.jl' file. In this file, the function to be tested and other functions are defined and the most important ones are extracted to be available in the - available throughout the framework. Some of the functions to be tested were the following: - - 1. `metadata_nhgis_data_tables_get` - 2. `metadata_nhgis_datasets_dataset_data_tables_data_table_get` - 3. `metadata_nhgis_datasets_dataset_get` - 4. `metadata_nhgis_datasets_get` - - Before working on the Julia files, testing and understanding the original R function was done using R studio. - -![](./rstudio.png) - -Each function was then tested using the API key from the *IPUMS* registration as well as other input examples taken from the documentation or the *IPUMS* website. -or from the *IPUMS* website. All functions were displayed successfully, giving the expected result, so it can be concluded that the translation from R to Julia is successful. - -```{julia} -#| eval: false - -using IPUMS -using OpenAPI - -api_key = "insert your key here" - -version = "2" -page_number = 1 -page_size = 2500 -#media_type = - -api = IPUMSAPI("https://api.ipums.org", Dict("Authorization" => api_key)); - -res1 = metadata_nhgis_data_tables_get(api, version) - -res2 = metadata_nhgis_datasets_dataset_get(api, "2022_ACS1", "2"); - -res3 = metadata_nhgis_datasets_dataset_data_tables_data_table_get(api, "2022_ACS1","B01001", "2"); - -res4 = metadata_nhgis_datasets_get(api, "2"); -``` - -An example of the output is: - -```{json} -. . . - -{ - "name": "NT1", - "nhgisCode": "AAA", - "description": "Total Population", - "universe": "Persons", - "sequence": 1, - "datasetName": "1790_cPop", - "nVariables": [ - 1 - ] -} - -. . . -``` - -# Accomplished Goals and Future Development - -The project was a 90-hour small project and during this time the documentation was completed and the testing of the metadata function was done, as well as the migration from Documenter.jl to DocumenterVitepress.jl. -During these months some things took longer than I expected because of some problems that occurred, so some things were missing in relation to the original plan. However, this time was useful for learning new things: - - I saw how to work with a package under development, how to work with large datasets, and how to write documentation - - I had the opportunity to better understand how to work with Git and GitHub - - I learned some new things about R, which was a completely unknown language to me. - - I deepened my knowledge of Julia, a language I had worked with during my time at university. - - I had the chance to work on a large open-source project, to be part of a large community, and to learn how to communicate with it efficiently. - -A special thanks goes to my mentors, Jacob Zelko and Krishna Bhogaonker, for helping me through this process. - -Future developments of this work could include deepening the work that my mentors and I have started, with the possibility of integrating this package with other machine learning packages in Julia and, from there, doing new analyses of the data in terms of social and geographical implications for 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I am a Data Engineer at [Brown Center for Biomedical Informatics](https://bcbi.brown.edu/). This summer, I had the privilege of co-mentoring a talented student, [Jay Sanjay](https://www.linkedin.com/in/jay-landge-589439260/) alongside [Jacob Zelko (\@TheCedarPrince)](https://jacobzelko.com) on a [project](https://summerofcode.withgoogle.com/programs/2024/projects/ZXVIYAXG) for Google Summer of Code (aka [GSoC](https://summerofcode.withgoogle.com/)). Here, I would like to share my experience as a co-mentor, offering insights for future mentors and students alike. - -Before diving into my experience, let me provide some background on how it all started. At JuliaCon 2023, I had the chance to meet Jacob Zelko and have been following his work at [JuliaHealth](https://juliahealth.org/) ever since. One day, I received a message from Jacob asking if I'd be interested in co-mentoring Jay for his GSoC project. Fortunately, I was already working on several projects at BCBI involving Julia programming, [OMOP CDM databases](https://ohdsi.github.io/CommonDataModel/cdm54.html) and [OHDSI](https://ohdsi.org/) tools, all of which were closely aligned with Jay's project. - -> Feel free to visit Jay's work on [OMOPCDMPathways.jl](https://github.com/JuliaHealth/OMOPCDMPathways.jl) or read about his [fellowship experience from this post](https://juliahealth.org/JuliaHealthBlog/posts/jay-gsoc/gsoc-2024-fellows.html). - -# Mentor-Mentee Relationship - -Jay, being a proactive student with a strong involvement in JuliaHealth, worked closely with Jacob to build a [proposal for the project](https://summerofcode.withgoogle.com/organizations/the-julia-language/projects/details/ZXVIYAXG) several months before GSoC began this year. His early involvement and familiarity with the community set a solid foundation for the project. Jacob, with his extensive experience mentoring GSoC students over the years, brought invaluable insights not only for Jay but also for me, as I was just beginning my journey as a mentor. - -Jacob established regular weekly Zoom meetings for the three of us to discuss Jay's progress, review his accomplishments, and plan the next steps. During these meetings, I focused on taking detailed notes to ensure we stayed organized and up to date with all the tasks. We used [Trello](https://trello.com/), a project management tool, to track progress and manage project tasks efficiently. Additionally, we stayed connected thoughout the week via a dedicated slack channel for any ongoing discussions or questions (on the [Julia Slack](https://julialang.org/slack/#the_julia_language_slack)). - -# Technical Discussion - -Jay's project "Developing Tooling for Observational Health Research in Julia" was inspired by the [TreatmentPatterns R package](https://www.sciencedirect.com/science/article/pii/S016926072200462X?via%3Dihub) [@markus2022treatmentpatterns]. The main goal of the project was to enhance observational research capabilities within the JuliaHealth ecosystem. To help Jay get started, [Jacob created 10 to 15 GitHub issues](https://github.com/JuliaHealth/OMOPCDMPathways.jl/issues?q=), each linked to a specific function that Jay planned to work on. - -During our weekly meeting, we discussed the challenges Jay encountered, any roadblacks in his progress, and reviewed the pull requests he submitted on GitHub. These sessions allowed us to provide timely feedback and guide Jay through complex technical issues, ensuring steady progress throughout the project. - -# Learnings and Observations - -Jay's proactive approach, steady progress, thoughtful questions, and clear focus on completing the project are qualities from which every student can benefit. His dedication to learning and problem-solving made a significant impact on the success of the project. - -## Tips for Mentees - -From a mentee's perspective having the following qualities would be helpful - -1. **Stick to the proposal:** While it's natural to feel the urge explore new ideas beyond the original proposal, it's essential to remain focused on the original proposal due to time constrains. - -2. **Adaptability and open-mindedness:** Be open to feedback and willing to adjust the tasks as you face challenges. - -3. **Time Management:** Many students juggle internships, interviews and other commitments during the summer. So it's to manage time effectively and discuss with the mentor about the progress during those times. - -4. **Effective communication:** Stay up to date with any updates from GSoC or from the mentor. Keeping your mentor updated about your progress or any challenges helps build a collaborative and supportive mentor relationship. - -## Tips for Mentors - -On the other hand, Jacob demonstrated what it means to be an effective mentor. He showed me how to foster a supportive, collaborative relationship with the student. These are the lessons that I will carry forward in future mentorship roles: - -From a mentor's perspective having the following qualities would be helpful - -1. **Clear communication:** Communicating well in advance about the availability to meet or to review the work, having frequent meetings with the mentee would be helpful. - -2. **Encouragement:** While offering support, it's important to encourage the mentee to take ownership of the project. - -3. **Commitment and time:** Mentoring GSoC is a voluntary role, often taken on in addition to regular professional work. Balancing GSoC with other work commitments requires effective time management and commitment. - -4. **Structured Guidance:** Providing a well-organized plan, such as using task management tools like Trello and GitHub issues, ensures that the mentee can follow a clear path towards success completion of the project. - -# Conclusion - -Google Summer of Code offers an incredible opportunity for students to hone their programming skills while contributing to impactful open-source projects. It was a rewarding experience to be part of this journey as a co-mentor, and I am grateful to Jacob for giving me the chance to be involved in such a meaning project with the JuliaHealth community. - -Through this experience, I not only gained insights into effective mentorship but also deepened my understanding of open-source collaboration and its potential to drive innovation in healthcare. I'm excited to explore further ways I can contribute to the JuliaHealth ecosystem and continue supporting the community. - -## Let's Keep in Touch! - -If you would like to know more about me, you can connect with me on [Linkedin](https://www.linkedin.com/in/mounika-thakkallapally/). diff --git a/posts/mounika-gsoc-mentor/references.bib b/posts/mounika-gsoc-mentor/references.bib deleted file mode 100644 index d6d69f1..0000000 --- a/posts/mounika-gsoc-mentor/references.bib +++ /dev/null @@ -1,9 +0,0 @@ -@article{markus2022treatmentpatterns, - title={TreatmentPatterns: an R package to facilitate the standardized development and analysis of treatment patterns across disease domains}, - author={Markus, Aniek F and Verhamme, Katia MC and Kors, Jan A and Rijnbeek, Peter R}, - journal={Computer Methods and Programs in Biomedicine}, - volume={225}, - pages={107081}, - year={2022}, - publisher={Elsevier} -} diff --git a/posts/ryan-gsoc/Benchmark_Page.png b/posts/ryan-gsoc/Benchmark_Page.png 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    \n Description of the emojis used in the title\n
      \n
    • πŸ“ **Action Plan**: A clear, structured plan that guides each step of the MedPipe3D pipeline.
      • \n
    • 🩻 **3D Medical Images**: Medical imaging data, such as MRI scans in Nifti format.
      • \n
    • πŸ“Ž **AI Model**: The initial AI model that will be trained and refined within the pipeline.
      • \n
    • πŸ“‰ **Loss Function**: A function that measures the model’s performance during training, guiding the optimization process.
      • \n
    • πŸ—ƒοΈ **Data Loading**: Preparation and loading of data and metadata into HDF5 format.
      • \n
    • πŸ“š **Data Splitting**: Dividing data into training, validation, and test sets.
      • \n
    • ♻️ **Data Augmentation**: Increasing data variability through augmentation.
      • \n
    • πŸ§‘β€πŸ« **AI Training**: Using Lux.jl framework to train the AI model.
      • \n
    • πŸ€– **Model**: The trained AI model that can perform tasks like segmentation on medical images.
      • \n
    • πŸ‘οΈ **Data for Visualization**: Output data, such as masks and segmentations.
      • \n
    • πŸ“ˆ **Performance Logs**: Logs and metrics documenting the AI's performance.
      • \n
    • β€οΈβ€πŸ©Ή **Purpose of MedPipe3D**
      • \n
    \n\n
    \n\n
    \nIn this post, I'd like to summarize what I did this summer and everything I learned along the way, rebuilding the MedPipe3D medical imaging pipeline. I will not start typically, but so that anyone even a novice can visualize what this project has achieved, while the latter part is intended for more experienced readers. It will be easiest to divide it into 4 steps separated by ➑️ in the title above. Each emoji stands for a different piece of pipeliner and will be described below.\n\nπŸ“πŸ©»πŸ“ŽπŸ“‰ **What we need from the user**\n\nMedPipe3D requires four essential inputs from the user to get started: a clear action plan πŸ“, 3D medical images like MRI scans 🩻, an AI model πŸ“Ž, and a loss function πŸ“‰.\n\nπŸ—ƒοΈπŸ“šβ™»οΈπŸ§‘β€πŸ« **The Pipeline essential AI manufacturing line**\n\nFollowing the plan πŸ“, MedPipe3D loads data, pre-processes, and organizes it πŸ—ƒοΈ. Allowing data to be easily split πŸ“š, and efficiently augmented ♻️ in many ways for learning AI πŸ§‘β€πŸ« model effectively. In the end, performing testing and post-processing for better determination of AI skills. \nIt's designed to transform raw medical data into a format that your AI can learn from, segmenting meaningful patterns and structures.\n\nπŸ€–πŸ‘οΈπŸ“ˆ **Results and Insights**\n\nMedPipe3D is a tool for researchers and for that, it cannot do without analysis, testing, and evaluation. The result of the pipeline is a model πŸ€– as well as data πŸ‘οΈ and logs πŸ“ˆ needed in MedEval3D that are ready for visualization and further analysis with MedEye3D. In a nutshell, it makes visualizing results easy, tumor locations or other medical features directly as masks on the scans.\n\nβ€οΈβ€πŸ©Ή **Purpose-Driven Technology**\n\nMedPipe3D's mission goes beyond technology. It's about providing the tools to create AIs that support healthcare professionals in making faster, more accurate decisions, with the ultimate goal of saving lives.\n\nThis four-part journey captures the heart of the MedPipe3D toolkit for advancing medical AI, from raw data to life-saving insight.\n\n## Introduction\n\n**MedPipe3D** is a framework created from hundreds of hours over summer vacation, thousands of lines of code, hundreds of mistakes, and most importantly the guidance of my mentor and author of all of these libraries Dr. [Jakub Mitura](https://www.linkedin.com/in/jakub-mitura-7b2013151/).\nAt its core, MedPipe3D combines sophisticated data handling from **MedImage** thanks to the hard work of [Divyansh Goyal](https://www.linkedin.com/in/divyansh-goyal-34654b200/). Newly developed pipeline for model training, validation, and testing with existing **MedEval3D**, and result visualization with **MedEye3D**.\nUnfortunately, not all of the project's goals have been fully achieved, and thereby there is one section ➑️ too many. Hopefully not for long. My name is [Jan Zubik](https://www.linkedin.com/in/janzubik/), and I wrote this entire library from scratch, which is currently my most complex project.\n\nIf you are a data scientist, programmer, or code enthusiast, I invite you to read the next section where I go into detail and present **version 1** of this tool in detail.\n\nI'm a 3rd-year student of BSc in Data Science and Machine Learning, I know that many things can be done better, expanded, debugged, and optimized. Now it just works, **but don't hesitate to write to me personally** on [LinkedIn](https://www.linkedin.com/in/janzubik/), [Julia's Slack](https://julialang.slack.com/team/U06L685B6TD) or [GitHub](https://github.com/JanZubik)!\nWith your comments, and direct critique **you will help me** to be a better programmer and one day MedPipe3D will contribute in a tiny way to save someone's life!\n\nExact work from the Google Summer of Code project you will find in [GitHub the repository.](https://github.com/JuliaHealth/MedPipe3D.jl/tree/GSoC-'24-MedPipe3D)\n\n\n# Project Goals\n\nThe primary goal was to develop MedPipe3D and enhance MedImage, a Julia package designed to streamline the process of GPU-accelerated medical image segmentation. The project aimed to merge existing librariesβ€”MedEye3D, MedEval3D, and MedImageβ€”into a cohesive pipeline that facilitates advanced data handling, preprocessing, augmentation, model training, validation, testing with post-processing and visualization for medical imaging applications.\n\n\n\n# Tasks\n\n- πŸ†™ - Fully finished, with great potential for further development\n- βœ… - Fully completed\n- ⚠️ - Partially uncompleted\n- ❌ - Unreached\n\nFull list of all major parts and minor tasks (all tasks set up in the original GSOC plan were completed at least minimum level, and many additional improvements above minimum were implemented)\n
    \n\n1. **Helpful functions to support the MedImage format βœ…**\n - Debugging rotations βœ…\n - Crop MedImage or 3D array βœ…\n - Pad MedImage or 3D array βœ…\n - Pad with edge values βœ…\n - Calculating the average of the edges of the picture πŸ†™\n\n2. **Integrate Augmentations for Medical Data βœ…**\n - Brightness transform βœ…\n - Contrast augmentation transform βœ…\n - Gamma Transform βœ…\n - Gaussian noise transform βœ…\n - Rician noise transform βœ…\n - Mirror transform βœ…\n - Scale transform πŸ†™\n - Gaussian blur transform βœ…\n - Simulate low-resolution transform πŸ†™\n - Elastic deformation transform πŸ†™\n\n3. **Develop a Pipeline ⚠️**\n - Structured configuration of all hyperparameters πŸ†™\n - Interactive creation of configuration βœ…\n - Creating a structured configuration of hyperparameters in JSON πŸ†™\n - Loading data into HDF5 βœ…\n - Cropping and padding to real coordinates of the main picture βœ…\n - Calculate Median and Mean Spacing with resampling πŸ†™\n - Cropping and padding to specific or average dimensions βœ…\n - Standardization and normalization βœ…\n - Managing index groups (channels) for batch requirements in HDF5 βœ…\n - Divide into train, validation, test specified as % βœ…\n - Divide with a specific division specified in JSON βœ…\n - Equal distribution when there are multiple classes βœ…\n - Extracting data and creating 5-dimensional tensors for batched learning βœ…\n - Hole images data loading βœ…\n - Patch-based data loading with probabilistic oversampling βœ…\n - Obtaining the necessary elements for learning βœ…\n - Get optimizer, loss function, and performance metrics βœ…\n - Apply augmentations βœ…\n - Train βœ…\n - Initializing model βœ…\n - The learning epoch βœ…\n - Epoch with early stopping functionality βœ…\n - Inferring βœ…\n - Validation βœ…\n - Evaluate metric βœ…\n - Evaluate validation loss βœ…\n - Validation with largest connected componentβœ…\n - Testing βœ…\n - Evaluate test set βœ…\n - Invertible augmentations evaluation βœ…\n - Patch-based invertible augmentations evaluation βœ…\n - Logging ⚠️\n - Returning the necessary results ⚠️\n - Logging connection to TensorBoard ❌\n - Logging errors and warnings ❌\n - Visualization ⚠️\n - Returning data in Nifti format βœ…\n - Automated visualization in MedEye3D ❌\n\n4. **Optimize Performance with GPU Acceleration**\n - Augmentations βœ…\n - Learning, Validation, Testing βœ…\n - Largest connected component βœ…\n\n5. **Documentation ⚠️**\n - Comments in important places in the code ⚠️\n - Documentation of the function ⚠️\n - Read me ⚠️\n - Documentation on juliahealth.org ❌\n\n
    \n\n## Integrate augmentations for medical data πŸ†™\nAugmenting medical data is a crucial step for enhancing model robustness, especially given the variations in imaging conditions and patient anatomy. \n\n- This pipeline currently supports multiple augmentation techniques:\n - Brightness transform βœ…\n - Contrast augmentation transform βœ…\n - Gamma Transform βœ…\n - Gaussian noise transform βœ…\n - Rician noise transform βœ…\n - Mirror transform βœ…\n - Scale transform πŸ†™\n - Gaussian blur transform βœ…\n - Simulate low-resolution transform πŸ†™\n - Elastic deformation transform πŸ†™\n\nWhich have been fully integrated. Each of these methods helps the model generalize better by simulating diverse imaging scenarios.\n\n![](./Augmentations.png)\n\nComments:\n\nAugmentations such as scaling, and low-resolution simulation use interpolation that is not yet GPU-accelerated.\n\nElastic deformation with simulation of different tissue elasticities is a potential development opportunity that would further improve the model's adaptability by mimicking more complex variations found in medical imaging.\n\n## Invertible augmentations and support test time augmentations πŸ†™\nThis section focuses on the ability to apply reversible augmentations to test data, allowing the model to be evaluated with different transformations. Only rotation is available at this time. The function `evaluate_patches` performs this evaluation by applying specified augmentations, dividing the test data into patches, and reconstructing the full image from the patches. During testing, one can choose to use of largest connected component post-processing. Metrics are calculated and results are saved for analysis.\n\n
    \nevaluate_test:\n\n```julia\n# ...\nfor test_group in test_groups\n test_data, test_label, attributes = fetch_and_preprocess_data([test_group], h5, config)\n results, test_metrics = evaluate_patches(test_data, test_label, tstate, model, config)\n y_pred, metr = process_results(results, test_metrics, config)\n save_results(y_pred, attributes, config)\n push!(all_test_metrics, metr)\nend\n# ...\n```\n\n```julia\nfunction evaluate_patches(test_data, test_label, tstate, model, config, axis, angle)\n println(\"Evaluating patches...\")\n results = []\n test_metrics = []\n tstates = [tstate]\n test_time_augs = []\n\n for i in config[\"learning\"][\"n_invertible\"]\n data = rotate_mi(test_data, axis, angle)\n for tstate_curr in tstates\n patch_results = []\n patch_size = Tuple(config[\"learning\"][\"patch_size\"])\n idx_and_patches, paded_data_size = divide_into_patches(test_data, patch_size)\n coordinates = [patch[1] for patch in idx_and_patches]\n patch_data = [patch[2] for patch in idx_and_patches]\n for patch in patch_data\n y_pred_patch, _ = infer_model(tstate_curr, model, patch)\n push!(patch_results, y_pred_patch)\n end\n idx_and_y_pred_patch = zip(coordinates, patch_results)\n y_pred = recreate_image_from_patches(idx_and_y_pred_patch, paded_data_size, patch_size, size(test_data))\n if config[\"learning\"][\"largest_connected_component\"]\n y_pred = largest_connected_component(y_pred, config[\"learning\"][\"n_lcc\"])\n end\n metr = evaluate_metric(y_pred, test_label, config[\"learning\"][\"metric\"])\n push!(test_metrics, metr)\n end\n end\n return results, test_metrics\nend\n```\n\n```julia\nfunction divide_into_patches(image::AbstractArray{T, 5}, patch_size::Tuple{Int, Int, Int}) where T\n println(\"Dividing image into patches...\")\n println(\"Size of the image: \", size(image)) \n\n # Calculate the required padding for each dimension (W, H, D)\n pad_size = (\n (size(image, 1) % patch_size[1]) != 0 ? patch_size[1] - size(image, 1) % patch_size[1] : 0,\n (size(image, 2) % patch_size[2]) != 0 ? patch_size[2] - size(image, 2) % patch_size[2] : 0,\n (size(image, 3) % patch_size[3]) != 0 ? patch_size[3] - size(image, 3) % patch_size[3] : 0\n )\n\n # Pad the image if necessary\n padded_image = image\n if any(pad_size .> 0)\n padded_image = crop_or_pad(image, (size(image, 1) + pad_size[1], size(image, 2) + pad_size[2], size(image, 3) + pad_size[3]))\n end\n\n # Extract patches\n patches = []\n for x in 1:patch_size[1]:size(padded_image, 1)\n for y in 1:patch_size[2]:size(padded_image, 2)\n for z in 1:patch_size[3]:size(padded_image, 3)\n patch = view(\n padded_image,\n x:min(x+patch_size[1]-1, size(padded_image, 1)),\n y:min(y+patch_size[2]-1, size(padded_image, 2)),\n z:min(z+patch_size[3]-1, size(padded_image, 3)),\n :,\n :\n )\n push!(patches, [(x, y, z), patch])\n end\n end\n end\n println(\"Size of padded image: \", size(padded_image))\n return patches, size(padded_image)\nend\n\nfunction recreate_image_from_patches(\n coords_with_patches,\n padded_size,\n patch_size,\n original_size\n)\n println(\"Recreating image from patches...\")\n reconstructed_image = zeros(Float32, padded_size...)\n \n # Place patches back into their original positions\n for (coords, patch) in coords_with_patches\n x, y, z = coords\n reconstructed_image[\n x:x+patch_size[1]-1,\n y:y+patch_size[2]-1,\n z:z+patch_size[3]-1,\n :,\n :\n ] = patch\n end\n\n # Crop the reconstructed image to remove any padding\n final_image = reconstructed_image[\n 1:original_size[1],\n 1:original_size[2],\n 1:original_size[3],\n :,\n :\n ]\n println(\"Size of the final image: \", size(final_image))\n return final_image\nend\n```\n
    \n\nComment:
    \nIn this section, there is significant potential to incorporate additional types of invertible augmentations.\n\n## Patch-based data loading with probabilistic oversampling βœ…\nIn this section, patches are extracted using `extract_patch` from the medical images for model training, with a probability-based method to decide between a random patch or a patch with non-zero labels.\nHelper functions like `get_random_patch` and `get_centered_patch` determine the starting indices and dimensions for the patches based on given configurations, while padding methods ensure consistency even if the patch exceeds the original image dimensions. Probabilistic oversampling, as configured, allows for more balanced and informative data sampling, which improves the model's ability to detect specific medical features.\n\n\n
    \nextract_patch:\n\n```julia\nfunction extract_patch(image, label, patch_size, config)\n # Fetch the oversampling probability from the config\n println(\"Extracting patch.\")\n oversampling_probability = config[\"learning\"][\"oversampling_probability\"]\n # Generate a random number to decide which patch extraction method to use\n random_choice = rand()\n\n if random_choice <= oversampling_probability\n return extract_nonzero_patch(image, label, patch_size)\n else\n\n return get_random_patch(image, label, patch_size)\n end\nend\n#Helper function, in case the mask is emptyClick to apply\nfunction extract_nonzero_patch(image, label, patch_size)\n println(\"Extracting a patch centered around a non-zero label value.\")\n indices = findall(x -> x != 0, label)\n if isempty(indices)\n # Fallback to random patch if no non-zero points are found\n return get_random_patch(image, label, patch_size)\n else\n # Choose a random non-zero index to center the patch around\n center = indices[rand(1:length(indices))]\n return get_centered_patch(image, label, center, patch_size)\n end\nend\n# Function to get a patch centered around a specific index\nfunction get_centered_patch(image, label, center, patch_size)\n center_coords = Tuple(center)\n half_patch = patch_size .Γ· 2\n start_indices = center_coords .- half_patch\n end_indices = start_indices .+ patch_size .- 1\n\n # Calculate padding needed\n pad_beg = (\n max(1 - start_indices[1], 0),\n max(1 - start_indices[2], 0),\n max(1 - start_indices[3], 0)\n )\n pad_end = (\n max(end_indices[1] - size(image, 1), 0),\n max(end_indices[2] - size(image, 2), 0),\n max(end_indices[3] - size(image, 3), 0)\n )\n\n # Adjust start_indices and end_indices after padding\n start_indices_adj = start_indices .+ pad_beg\n end_indices_adj = end_indices .+ pad_beg\n\n # Convert padding values to integers\n pad_beg = Tuple(round.(Int, pad_beg))\n pad_end = Tuple(round.(Int, pad_end))\n\n # Pad the image and label using pad_mi\n image_padded = pad_mi(image, pad_beg, pad_end, 0)\n label_padded = pad_mi(label, pad_beg, pad_end, 0)\n\n # Extract the patch\n image_patch = image_padded[\n start_indices_adj[1]:end_indices_adj[1],\n start_indices_adj[2]:end_indices_adj[2],\n start_indices_adj[3]:end_indices_adj[3]\n ]\n label_patch = label_padded[\n start_indices_adj[1]:end_indices_adj[1],\n start_indices_adj[2]:end_indices_adj[2],\n start_indices_adj[3]:end_indices_adj[3]\n ]\n\n return image_patch, label_patch\nend\n\nfunction get_random_patch(image, label, patch_size)\n println(\"Extracting a random patch.\")\n # Check if the patch size is greater than the image dimensions\n if any(patch_size .> size(image))\n # Calculate the needed size to fit the patch\n needed_size = map(max, size(image), patch_size)\n # Use crop_or_pad to ensure the image and label are at least as large as needed_size\n image = crop_or_pad(image, needed_size)\n label = crop_or_pad(label, needed_size)\n end\n\n # Calculate random start indices within the new allowable range\n start_x = rand(1:size(image, 1) - patch_size[1] + 1)\n start_y = rand(1:size(image, 2) - patch_size[2] + 1)\n start_z = rand(1:size(image, 3) - patch_size[3] + 1)\n start_indices = [start_x, start_y, start_z]\n end_indices = start_indices .+ patch_size .- 1\n\n # Extract the patch directly when within bounds\n image_patch = image[start_indices[1]:end_indices[1], start_indices[2]:end_indices[2], start_indices[3]:end_indices[3]]\n label_patch = label[start_indices[1]:end_indices[1], start_indices[2]:end_indices[2], start_indices[3]:end_indices[3]]\n\n return image_patch, label_patch\nend\n\n```\n
    \n\n## Calculate Median and Mean Spacing with resampling πŸ†™\nThis part ensures that all images in the dataset have consistent real coordinates, spacing, and shape. It's a critical factor in medical imaging for accurate analysis. Calculating and applying set values, median or mean across images ensures uniformity.\n\n#### Resample images to target image πŸ†™\nThis step aligns each image to the reference coordinates of the main image, ensuring that all images share a common spatial alignment. The `resample_to_image` function from MedImage.jl is used here, applying interpolation to adjust each image.\n\n\n
    \nresample_images_to_target:\n\n```julia\nif resample_images_to_target && !isempty(Med_images)\n println(\"Resampling $channel_type files in channel '$channel_folder' to the first $channel_type in the channel.\")\n reference_image = Med_images[1]\n Med_images = [resample_to_image(reference_image, img, interpolator) for img in Med_images]\nend\n```\n
    \n\nComment:
    \n`Resample_to_image` uses interpolation that is not yet GPU-accelerated in this implementation, this step slows down the data preparation phase significantly.\n\n#### Ensure uniform spacing across the entire dataset πŸ†™\nThis step brings all images to a consistent voxel spacing across the dataset using `resample_to_spacing` from MedImage.jl. This uniform spacing is crucial for creating a standardized dataset where each image voxel represents the same physical volume.\n\n\n
    \nesample_to_spacing:\n\n```julia\nif resample_images_spacing == \"set\"\n println(\"Resampling all $channel_type files to target spacing: $target_spacing\")\n target_spacing = Tuple(Float32(s) for s in target_spacing)\n channels_data = [[resample_to_spacing(img, target_spacing, interpolator) for img in channel] for channel in channels_data]\nelseif resample_images_spacing == \"avg\"\n println(\"Calculating average spacing across all $channel_type files and resampling.\")\n all_spacings = [img.spacing for channel in channels_data for img in channel]\n avg_spacing = Tuple(Float32(mean(s)) for s in zip(all_spacings...))\n println(\"Average spacing calculated: $avg_spacing\")\n channels_data = [[resample_to_spacing(img, avg_spacing, interpolator) for img in channel] for channel in channels_data]\nelseif resample_images_spacing == \"median\"\n println(\"Calculating median spacing across all $channel_type files and resampling.\")\n all_spacings = [img.spacing for channel in channels_data for img in channel]\n median_spacing = Tuple(Float32(median(s)) for s in all_spacings)\n println(\"Median spacing calculated: $median_spacing\")\n channels_data = [[resample_to_spacing(img, median_spacing, interpolator) for img in channel] for channel in channels_data]\nelseif resample_images_spacing == false\n println(\"Skipping resampling of $channel_type files.\")\n # No resampling will be applied, channels_data remains unchanged.\nend\n```\n
    \n\nComment:
    \n`Resample_to_spacing` uses interpolation that is not yet GPU-accelerated in this implementation, this step slows down the data preparation phase significantly.\n\n#### Resizing all channel files to average or target size βœ…\nTo create a cohesive 5D tensor, all images in each channel are resized to a uniform shape, either the average size of all images or a specific target size. This resizing process uses `crop_or_pad`, ensuring that all images match the specified dimensions, making them suitable for model input.\n\n
    \ncrop_or_pad:\n\n```julia\nif resample_size == \"avg\"\n sizes = [size(img.voxel_data) for img in channels_data for img in img] # Get sizes from all images\n avg_dim = map(mean, zip(sizes...))\n avg_dim = Tuple(Int(round(d)) for d in avg_dim)\n println(\"Resizing all $channel_type files to average dimension: $avg_dim\")\n channels_data = [[crop_or_pad(img, avg_dim) for img in channel] for channel in channels_data]\nelseif resample_size != \"avg\"\n target_dim = Tuple(resample_size)\n println(\"Resizing all $channel_type files to target dimension: $target_dim\")\n channels_data = [[crop_or_pad(img, target_dim) for img in channel] for channel in channels_data]\nend\n```\n
    \n\n## Basic Post-processing operations\nPost-processing operations involve the algorithm `largest_connected_components`. It is achieved by label initialization and propagation in the segmented mask.\nThe `initialize_labels_kernel` function assigns unique labels to different regions.\n\n
    \ninitialize_labels_kernel:\n\n```julia\n@kernel function initialize_labels_kernel(mask, labels, width, height, depth)\n idx = @index(Global, Cartesian)\n i = idx[1]\n j = idx[2]\n k = idx[3]\n \n if i >= 1 && i <= width && j >= 1 && j <= height && k >= 1 && k <= depth\n if mask[i, j, k] == 1\n labels[i, j, k] = i + (j - 1) * width + (k - 1) * width * height\n else\n labels[i, j, k] = 0\n end\n end\nend\n```\n
    \nPropagate_labels_kernel iteratively updates the labels to maintain connected regions.\npropagate_labels_kernel:\n
    \n\n```julia\n@kernel function propagate_labels_kernel(mask, labels, width, height, depth)\n idx= @index(Global, Cartesian)\n i = idx[1]\n j = idx[2]\n k = idx[3]\n\n if i >= 1 && i <= width && j >= 1 && j <= height && k >= 1 && k <= depth\n if mask[i, j, k] == 1\n current_label = labels[i, j, k]\n for di in -1:1\n for dj in -1:1\n for dk in -1:1\n if di == 0 && dj == 0 && dk == 0\n continue\n end\n ni = i + di\n nj = j + dj\n nk = k + dk\n if ni >= 1 && ni <= width && nj >= 1 && nj <= height && nk >= 1 && nk <= depth\n if mask[ni, nj, nk] == 1 && labels[ni, nj, nk] < current_label\n labels[i, j, k] = labels[ni, nj, nk]\n end\n end\n end\n end\n end\n end\n end\nend\n```\n
    \nThis process facilitates the identification of the largest connected components in 3D space, helping to isolate relevant medical structures, such as tumors, in the segmented mask. Allowing determining how many such areas are to be returned.\n\n
    \nlargest_connected_components:\n\n```julia\nfunction largest_connected_components(mask::Array{Int32, 3}, n_lcc::Int)\n width, height, depth = size(mask)\n mask_gpu = CuArray(mask)\n labels_gpu = CUDA.fill(0, size(mask))\n dev = get_backend(labels_gpu)\n ndrange = (width, height, depth)\n workgroupsize = (3, 3, 3)\n\n # Initialize labels\n initialize_labels_kernel(dev)(mask_gpu, labels_gpu, width, height, depth, ndrange = ndrange)\n CUDA.synchronize()\n\n # Propagate labels iteratively\n for _ in 1:10 \n propagate_labels_kernel(dev, workgroupsize)(mask_gpu, labels_gpu, width, height, depth, ndrange = ndrange)\n CUDA.synchronize()\n end\n\n # Download labels back to CPU\n labels_cpu = Array(labels_gpu)\n \n # Find all unique labels and their sizes\n unique_labels = unique(labels_cpu)\n label_sizes = [(label, count(labels_cpu .== label)) for label in unique_labels if label != 0]\n\n # Sort labels by size and get the top n_lcc\n sort!(label_sizes, by = x -> x[2], rev = true)\n top_labels = label_sizes[1:min(n_lcc, length(label_sizes))]\n\n # Create a mask for each of the top n_lcc components\n components = [labels_cpu .== label[1] for label in top_labels]\n return components\nend\n```\n
    \n\n## Structured configuration of all hyperparameters πŸ†™\n\nHyperparameters for the entire pipeline are stored in a JSON configuration file, enabling straightforward adjustments for experimentation (just swap values, save and resume the study). This structured setup allows easy modification of key parameters, such as data set preparation, training settings, data augmentation, and resampling options.\n\n\n
    \nExample configuration:\n\n```JSON\n{\n \"model\": {\n \"patience\": 10,\n \"early_stopping_metric\": \"val_loss\",\n \"optimizer_name\": \"Adam\",\n \"loss_function_name\": \"l1\",\n \"early_stopping\": true,\n \"early_stopping_min_delta\": 0.01,\n \"optimizer_args\": \"lr=0.001\",\n \"num_epochs\": 10\n },\n \"data\": {\n \"batch_complete\": false,\n \"resample_size\": [200,101,49],\n \"resample_to_target\": false,\n \"resample_to_spacing\": false,\n \"batch_size\": 3,\n \"standardization\": false,\n \"target_spacing\": null,\n \"channel_size\": 1,\n \"normalization\": false,\n \"has_mask\": true\n },\n \"augmentation\": {\n \"augmentations\": {\n \"Brightness transform\": {\n \"mode\": \"additive\",\n \"value\": 0.2\n }\n },\n \"p_rand\": 0.5,\n \"processing_unit\": \"GPU\",\n \"order\": [\n \"Brightness transform\"\n ]\n },\n \"learning\": {\n \"Train_Val_Test_JSON\": false,\n \"largest_connected_component\": false,\n \"n_lcc\": 1,\n \"n_folds\": 3,\n \"invertible_augmentations\": false,\n \"n_invertible\": true,\n \n \"class_JSON_path\": false,\n \"additional_JSON_path\": false,\n \"patch_size\": [50,50,50],\n \"metric\": \"dice\",\n \"n_cross_val\": false,\n \"patch_probabilistic_oversampling\": false,\n \"oversampling_probability\": 1.0,\n \"test_train_validation\": [\n 0.6,\n 0.2,\n 0.2\n ],\n \"shuffle\": false\n }\n}\n\n```\n
    \n\nComments:
    \nThe current configuration is loaded as a dictionary, which simplifies access and modification. This setup presents a strong foundation for integrating automated search algorithms for hyperparameter tuning, enabling more efficient model optimization.
    \nThe configuration structure could be reorganized and re-named to improve readability, making it easier for users to locate and adjust specific parameters.\n\n## Visualization of algorithm outputs ⚠️\nThis module provides basic visualization functionality by saving output masks and images first to MedImage format and then to Nifti format. The `create_nii_from_medimage` function from MedImage.jl generates Nifti files, which can be loaded into MedEye3D for 3D visualization.\n\nComments:
    \nIntegrating this visualization module more fully with the pipeline could eliminate unnecessary steps. By automatically loading output masks and images as raw data into MedEye3D for 3D visualization and supporting a more efficient end-to-end workflow. \n\n## K-fold cross-validation functionality βœ…\nK-fold cross-validation is implemented to evaluate model performance more robustly. The data is split into multiple folds, with each fold serving as a validation set once, while the others form the training set. This functionality provides a better assessment of model performance across different subsets of the data.\n\n
    \nK-fold cross-validation functionality:\n\n```julia\n...\n tstate = initialize_train_state(rng, model, optimizer)\n if config[\"learning\"][\"n_cross_val\"]\n n_folds = config[\"learning\"][\"n_folds\"]\n all_tstate = []\n combined_indices = [indices_dict[\"train\"]; indices_dict[\"validation\"]]\n shuffled_indices = shuffle(rng, combined_indices)\n for fold in 1:n_folds\n println(\"Starting fold $fold/$n_folds\")\n train_groups, validation_groups = k_fold_split(combined_indices, n_folds, fold, rng)\n \n tstate = initialize_train_state(rng, model, optimizer)\n final_tstate = epoch_loop(num_epochs, train_groups, validation_groups, h5, model, tstate, config, loss_function, num_classes)\n \n push!(all_tstate, final_tstate)\n end\n else\n final_tstate = epoch_loop(num_epochs, train_groups, validation_groups, h5, model, tstate, config, loss_function, num_classes)\n end\n return final_tstate\n... \n```\n
    \n\nThe `k_fold_split` function organizes the indices for each fold, ensuring comprehensive coverage of the dataset during training.\n\n
    \nk_fold_split\n\n```julia\nfunction k_fold_split(data, n_folds, current_fold)\n fold_size = length(data) Γ· n_folds\n validation_start = (current_fold - 1) * fold_size + 1\n validation_end = validation_start + fold_size - 1\n validation_indices = data[validation_start:validation_end]\n train_indices = [data[1:validation_start-1]; data[validation_end+1:end]]\n return train_indices, validation_indices\nend\n```\n
    \n\n# Conclusions and Future Development\nI have successfully established a foundation for a medical imaging pipeline, addressing significant challenges in data handling, model training, and augmentation integration. The integration of dataset-wide functions has significantly enhanced the reproducibility and handling of batched data with GPU support enabling scalability of experiments, making it easier for researchers and practitioners to produce better results.\n\n# Future Development\nAs we look to the future, there are several areas where MedPipe3D can be expanded and improved to better serve the medical AI community. These include:\n\n## Necessary Enhancements\n\nComprehensive Logging: Develop detailed logging mechanisms that capture a wide range of events, including system statuses, model performance metrics, and user activities, to facilitate debugging and system optimization. This is currently executed as a simple `println` function.\n\nTensorBoard Integration: Implement an interface for TensorBoard to allow users to visualize training dynamics in real time, providing insights into model behavior and performance trends.\n\nError and Warning Logs: Introduce advanced error and warning logging capabilities to alert users of potential issues before they affect the pipeline's performance, ensuring smoother operations and maintenance.\n\nAutomated Visualization: Integrate MedEye3D directly into MedPipe3D to enable automated visualization of outputs, such as segmentation masks or other relevant medical imaging features. This feature would provide users with real-time visual feedback on model performance and data quality.\nCode-Level Documentation: Due to needed changes in the fundamental structure of the pipeline in the final phase of the project, it is necessary to reevaluate all documentation.\n\nOfficial JuliaHealth Documentation: Extend the documentation efforts to include official entries on juliahealth.org, providing a centralized and authoritative resource for users seeking to learn more about MedPipe3D and its capabilities with examples shown\n\n## Potential Enhancements\nGPU support for interpolation will allow for significant acceleration of such functions as Scale transform, Simulate, Low-resolution transform, Elastic deformation transform, and Resampling spacing.\n\nAdd more reversible augmentations to test time.\n\nCalculating the average of the edges of the picture: checking the type of photo and calculating more correctly on this basis\n\nElastic deformation transforms with the simulation of different tissue elasticities.\n\n# Acknowledgments πŸ™‡β€β™‚οΈ\n\nI would like to express my deepest gratitude to my mentor Dr. [Jakub Mitura](https://www.linkedin.com/in/jakub-mitura-7b2013151/) for his invaluable guidance and support throughout this project. His expertise and encouragement were instrumental in overcoming challenges and achieving project milestones.\n\n", + "supporting": [ + "GSoC_Jan_Zubik_MedPipe3D_files" + ], + "filters": [], + "includes": {} + } +} \ No newline at end of file diff --git a/_freeze/blog/posts/divyansh-gsoc/gsoc-2024-fellows/execute-results/html.json b/_freeze/blog/posts/divyansh-gsoc/gsoc-2024-fellows/execute-results/html.json new file mode 100644 index 0000000..22a4ef5 --- /dev/null +++ b/_freeze/blog/posts/divyansh-gsoc/gsoc-2024-fellows/execute-results/html.json @@ -0,0 +1,12 @@ +{ + "hash": "f865f6b24acb146c30a159e6e0d16fdc", + "result": { + "engine": "julia", + "markdown": "---\ntitle: \"GSoC '24: Adding functionalities to medical imaging visualizations\"\ndescription: \"A summary of my project for Google Summer of Code - 2024\"\nauthor: \"Divyansh Goyal\"\ndate: \"11/1/2024\"\nbibliography: ./references.bib\ncsl: ./../../ieee-with-url.csl\ntoc: true\nengine: julia\nimage: false\ncategories:\n - gsoc\n - openGl\n - imaging\n - neuro\n---\n\n\n\n\n\n# Hello Everyone! πŸ‘‹\n\nI am Divyansh, an undergraduate student from Guru Gobind Singh Indraprastha university, majoring in Artificial Intelligence and Machine Learning. Stumbling upon projects under the Juliahealth sub-ecosystem of medical imaging packages, the intricacies of imaging modalities and file formats, reflected in their relevant project counterparts, captured my interest. Working with standards such as NIfTI (Neuroimaging Informatics Technology Initiative) and DICOM (Digital Imaging and Communications in Medicine) with MedImages.jl, I became interested in the visualization routines of such imaging datasets and their integration within the segmentation pipelines for modern medical-imaging analysis.\n\nIn this post, I’d like to summarize what I did this summer and everything I learned along the way, contributing to MedEye3d.jl medical imaging visualizer under GSOC-2024!\n\n> If you want to learn more about me, you can connect with me on [**LinkedIn**](https://www.linkedin.com/in/divyansh-goyal-34654b200/) and follow me on [**GitHub**](https://github.com/divital-coder)\n\n# Background\n\n## What is MedEye3d.jl?\n\n[MedEye3D.jl](https::/github.com/Juliahealth/MedEye3d.jl) is a package under the Julia language ecosystem designed to facilitate the visualization and annotation of medical images. Tailored specifically for medical applications, it offers a range of functionalities to enhance the interpretation and analysis of medical images. MedEye3D aims to provide an essential tool for 3D medical imaging workflow within Julia. The underlying combination of [Rocket.jl](https://github.com/ReactiveBayes/Rocket.jl) and [ModernGL.jl](https://github.com/JuliaGL/ModernGL.jl) ensures the high-performance robust visualizations that the package has to offer.\n\nMedEye3d.jl is open-source and comes with an intuitive user interface (To learn more about MedEye3d, you can read the paper introducing it [here](https://doi.org/10.26348/znwwsi.25.57) [@Mitura2021]).\n\n## What features does this project encompass?\n\nThis project covers implementation of several tasks that will enable the establishment of additional important functionalities within the MedEye3D package, facilitating enhancements within the visualization’s windowing for MRI and PET data, support for super voxels (sv), improved load times, high-level functionality implementation and robust viewing for multiple images.\n\n# Project Goals\n\nThe goals outlined by Dr. Jakub Mitura (my project mentor) and I, beginning of this summer were:\n\n1. Migration of package reliance from [Rocket.jl](https://github.com/reactivebayes/Rocket.jl) to base Julia channel and macros: The first decision that was made was to fix the issue of screen tearing and flicker, resulting from the Rocket.jl's actor-subscription mechanism present at the core of MedEye3d.jl's event-driven programming. Here, Julia's threadsafe and asynchronous [channels](https://docs.julialang.org/en/v1/manual/asynchronous-programming/) provided a way to introduce reactive programming and state management within MedEye3d without the tradeoffs resulting from external packages such as Rocket\n\n2. Implementation of high level functions with simplified basic usage: Prior to this, MedEye3d involved initialization of data, texture specifications and text display for a final visualization. To reduce complexity, methods to abstract such chores were devised and implemented which resulted in the exposure of functions for loading images, accessing display data and modification of display data. This also encompassed the loading of images via [MedImages.jl](https://github.com/juliahealth/MedImages.jl) which required prior work for the integration of C++ [ITK](https://github.com/InsightSoftwareConsortium/ITK) backend for image I/O.\n\n3. Improved precompilation with decreased outputs to reduce start time\n\n4. Automatic windowing for most common MRI and PET modalities: This task is a step in the direction of maintaining consistent visualizations across MRI and PET’s most common modalities, to mimic images similar to what is displayed within [3dSlicer](https://www.slicer.org/) for the same.\n\n5. Adding support for multi-image viewing with crosshair marker for image registration\n\n6. Adding support for the display of [SuperVoxels](https://doi.org/10.1016/j.cagd.2022.102080) sv with borders within the image slices to better understand anatomical regions within slices: Supervoxels, described either through indicator masks or meshes, encapsulate regions of interest with distinct image characteristics.\n\nAdditionally, we had a few stretch goals which are going to be a work in progress:\n\n1. Visualization of structures by 3D rendering using OpenGL,\n\n2. Support for MedVoxelHD visualization by voxel-based Hausdorff distance computation.\n\n3. Support for OSX users\n\n# Tasks\n\n## 1. Migration of package from Rocket to Julia's Base.Channel\n\nInitially, there was significant screen-tearing evident from the pixelated display of the rendered text and main image which, furthermore exhibited flickering upon scrolling through the slices in the relevant displayed image's planar views i.e (Transversal, Coronal and Saggital). Troubleshooting along the way, we narrowed down the issue within the Rocket's actor-subscription mechanism and decided to integrate Julia's Base.Channel within [MedEye3d.jl](https://github.com/Juliahealth/MedEye3d.jl) for handling the event and state management routine. Julia has asynchronous, threadsafe [channels](https://docs.julialang.org/en/v1/manual/asynchronous-programming/#Communicating-with-Channels) which facilitate in asynchronous programming with the help of a producer-consumer mechanism. An example usage of Base.Channel is as follows:\n\n```julia\nfunction consumer(channel::Base.Channel)\n while(true)\n channelData::String = take!(channel)\n println(\"Channel got \" * channelData)\n end\nend\n\nnewChannel = Base.Channel(100)\n\n@async consumer(newChannel)\nput!(newChannel, \"apples\")\n```\n\nJulia’s multiple dispatch made for the architectural setup of MedEye3d, facilitated fixing the issue of screen tearing. Below is how the `on_next!` function, invokes different reactive components based on the types of arguments it is dealing with.\n\n> Dump data in channel -> fetch data from the channel in an event loop -> invoke `on_next!(state, channelData)` -> invoke relevant functionality based on the type of arguments passed\n\n![](./multiple_dispatch_code.png)\n\nThe end result was a visualizer with a seamless display of a CT image without any pixelating artifacts.\n\n![](./fixed_screen_tear.png)\n\n## 2. Implementation of high level functions with simplified basic usage\n\nImplementing a bare-bones image visualization required a lot of function calls and definitions, in order to execute the following phases:\n\n1. Rendering an image-plane with OpenGL\n\n2. Loading data slices from the image\n\n3. Creating texture specifications for modalities\n\n4. Producing the final segmentation display\n\nIn order to simplify basic usage, high-level abstractions were put in place with the help of [MedImages.jl](https://github.com/MedImages.jl) (under ongoing development) library to load images in the form of MedImage objects to formulate a single display function for the user. Further simplifications were made to accommodate options for the user to manipulate the imaging data that is displayed currently in the visualizer i.e retrieval of voxel arrays and their modification. Taking this in mind, the following relevant functions were exposed:\n\n```julia\nMedEye3d.SegmentationDisplay.displayImage()\n```\n\n```julia\nMedEye3d.DisplayDataManag.getDisplayedData()\n```\n\n```julia\nMedEye3d.DisplayDataManag.setDisplayedData()\n```\n\nPutting all of the above functions to use together, we can launch the visualizer, retrieve the displayed voxel data and modify it to our liking. A sample script to achieve the former, is highlighted below:\n\n```julia\nusing MedEye3d\nctNiftiImage = \"/home/hurtbadly/Downloads/ct_soft_study.nii.gz\"\nmedEyeStruct = MedEye3d.SegmentationDisplay.displayImage(ctNiftiImage)\ndisplayData = MedEye3d.DisplayDataManag.getDisplayedData(medEyeStruct, [Int32(1), Int32(2)]) #passing the active texture number\n\n# We need to check if the return type of the displayData is a single Array{Float32,3} or a vector{Array{Float32,3}}\n# Now in this case we are setting Gaussian noise over the manualModif Texture voxel layer, and the manualModif texture defaults to 2 for active number\n\ndisplayData[2][:, :, :] = randn(Float32, size(displayData[2]))\nMedEye3d.DisplayDataManag.setDisplayedData(medEyeStruct, displayData)\n```\n\nThe result of this [Gaussian noise](https://www.sfu.ca/sonic-studio-webdav/handbook/Gaussian_Noise.html) within the annotation layer, made for an outcome like the following:\n\n![](./gaussian_noise_annotation.png)\n\n## 3. Improved precompilation with decreased outputs to reduce start time\n\nPreviously, the package's precompilation was failing in Julia v1.9 and v1.10 due to pattern matching errors arising after the usage of match macros from the [Match.jl](https://github.com/JuliaServices/Match.jl) pkg in MedEye3d's keymapping workflow between GLFW callbacks from mouse and keyboard. The relevant equivalent native conditional (if-else) statements, resolved the issue and facilitated in successful precompilation of the package. Further, only following minimal outputs were produced during precompilation:\n\n![](./precompilation_outputs.png)\n\nChanges highlighted within the following pull-request:\n\n[https://github.com/JuliaHealth/MedEye3d.jl/pull/12](https://github.com/JuliaHealth/MedEye3d.jl/pull/12)\n\n## 4. Automatic [windowing](https://youtu.be/HaL-G43kwKA) for most common MRI and PET modalities\n\nWindowing is a crucial aspect of medical imaging, particularly in MRI (Magnetic Resonance Imaging) and PET (Positron Emission Tomography) modalities. It enables radiologists to enhance the contrast of images, highlighting specific features and improving the overall diagnostic accuracy. Windowing involves controlling the display range of pixel values to optimize the contrast between different tissues or structures. The display range is defined by two values: the minimum (min) and maximum (max) values that contribute to the final range of pixels that are displayed. By adjusting these values, radiologists can enhance or suppress specific features in the image, facilitating a more accurate diagnosis.\n\nThe `setTextureWindow` function utilizes a set of predefined keymap controls to simplify the windowing process. The F1-F7 keys are designated for controlling windowing in MRI and PET modalities. The keymap controls are as follows:\n\n* F1: Display wide window for bone (CT) or increase minimum value for PET\n\n* F2: Display window for soft tissues (CT) or increase minimum value for PET\n\n* F3: Display wide window for lung viewing (CT) or increase minimum value for PET\n\n* F4: Decrease minimum value for display\n\n* F5: Increase minimum value for display\n\n* F6: Decrease maximum value for display\n\n* F7: Increase maximum value for display\n\nImplementation of `setTextureWindow` Function\n\nThe `setTextureWindow` function is designed to update the texture window settings based on the input keymap control. The function takes three arguments:\n\n* `activeTextur`: The current texture specification\n* `stateObject`: The state data fields\n* `windowControlStruct`: The window control structure containing the letter code for the keymap control\n\nThe function performs the following steps:\n\n1. Checks the letter code of the keymap control and updates the minimum and maximum values of the texture specification accordingly.\n2. Updates the uniforms for the texture specification using the `controlMinMaxUniformVals` function.\n\n```julia\nfunction setTextureWindow(activeTextur::TextureSpec, stateObject::StateDataFields, windowControlStruct::WindowControlStruct)\n activeTexturName = activeTextur.name\n displayRange = activeTextur.minAndMaxValue[2] - activeTextur.minAndMaxValue[1]\n activeTexturStudyType = activeTextur.studyType\n if windowControlStruct.letterCode == \"F1\"\n if activeTexturStudyType == \"CT\"\n #Bone windowing in CT\n activeTextur.minAndMaxValue = Float32.([400, 1000])\n elseif activeTexturStudyType == \"PET\"\n activeTextur.minAndMaxValue[1] += 0.10 * displayRange #windowing for pet, in the case of PET simply increase the minimum by 20% , doing the same in f1,f2 and f3\n end\n elseif windowControlStruct.letterCode == \"F2\"\n if activeTexturStudyType == \"CT\"\n activeTextur.minAndMaxValue = Float32.([-40, 350])\n elseif activeTexturStudyType == \"PET\"\n activeTextur.minAndMaxValue[1] += 0.10 * displayRange\n end\n elseif windowControlStruct.letterCode == \"F3\"\n if activeTexturStudyType == \"CT\"\n activeTextur.minAndMaxValue = Float32.([-426, 1000])\n elseif activeTexturStudyType == \"PET\"\n activeTextur.minAndMaxValue[1] += 0.10 * displayRange\n end\n elseif windowControlStruct.letterCode == \"F4\"\n activeTextur.minAndMaxValue[1] -= 0.20 * displayRange\n elseif windowControlStruct.letterCode == \"F5\"\n activeTextur.minAndMaxValue[1] += 0.20 * displayRange\n elseif windowControlStruct.letterCode == \"F6\"\n activeTextur.minAndMaxValue[2] -= 0.20 * displayRange\n elseif windowControlStruct.letterCode == \"F7\"\n activeTextur.minAndMaxValue[2] += 0.20 * displayRange\n elseif windowControlStruct.letterCode == \"F8\"\n activeTextur.uniforms.maskContribution -= 0.10\n elseif windowControlStruct.letterCode == \"F9\"\n activeTextur.uniforms.maskContribution += 0.10\n end\n\n stateObject.mainForDisplayObjects.listOfTextSpecifications = map(texture -> texture.name == activeTexturName ? activeTextur : texture, stateObject.mainForDisplayObjects.listOfTextSpecifications)\n coontrolMinMaxUniformVals(activeTextur)\nend\n```\n> Bone windowing in CT\n\n![](./ct_windowing.png)\n\n> Bone windowing in PET\n\n![](./pet_windowing.png)\n\n## 5. Adding support for multi-image viewing with crosshair marker for image registration\n\nFollowing the mid-term evaluation, MedEye3d.jl underwent a significant enhancement, whereby a multi-image display capability was implemented through a series of refinements. Specifically, a novel approach was adopted, whereby separate OpenGL [fragment shaders](https://www.khronos.org/opengl/wiki/Fragment_Shader) were introduced to concurrently render images on either side of the visualizer, namely the left and right views. Prior to integrating voxel data into the fragment shaders, an initial series of tests involved evaluating individual colors to validate the integrity of the double image display. A screenshot from one of these critical testing phases is presented below:\n![](./multi_fragment_shader.png)\n\nThe shaders were further manipulated to automatically initialize for each of the images separately. Further, the reactive aspect of the visualizer in multi-image display mode was iterated upon and now, instead of a single state management struct, a vector of states was being passed around, facilitating the user to scroll each of the images separately just by simply hovering their mouse over either of the image, activating its relevant associated state struct.\n\nDown below, is the struct for state that handles all of the things currently related with an image:\n\n```julia\n@with_kw mutable struct StateDataFields\n currentDisplayedSlice::Int = 1 # stores information what slice number we are currently displaying\n mainForDisplayObjects::forDisplayObjects = forDisplayObjects() # stores objects needed to display using OpenGL and GLFW\n onScrollData::FullScrollableDat = FullScrollableDat()\n textureToModifyVec::Vector{TextureSpec} = [] # texture that we want currently to modify - if list is empty it means that we do not intend to modify any texture\n isSliceChanged::Bool = false # set to true when slice is changed set to false when we start interacting with this slice - thanks to this we know that when we start drawing on one slice and change the slice the line would star a new on new slice\n textDispObj::ForWordsDispStruct = ForWordsDispStruct()# set of objects and constants needed for text diplay\n currentlyDispDat::SingleSliceDat = SingleSliceDat() # holds the data displayed or in case of scrollable data view for accessing it\n calcDimsStruct::CalcDimsStruct = CalcDimsStruct() #data for calculations of necessary constants needed to calculate window size , mouse position ...\n valueForMasToSet::valueForMasToSetStruct = valueForMasToSetStruct() # value that will be used to set pixels where we would interact with mouse\n lastRecordedMousePosition::CartesianIndex{3} = CartesianIndex(1, 1, 1) # last position of the mouse related to right click - usefull to know onto which slice to change when dimensions of scroll change\n forUndoVector::AbstractArray = [] # holds lambda functions that when invoked will undo last operations\n maxLengthOfForUndoVector::Int64 = 15 # number controls how many step at maximum we can get back\n fieldKeyboardStruct::KeyboardStruct = KeyboardStruct()\n displayMode::DisplayMode = SingleImage\n imagePosition::Int64 = 1\n switchIndex::Int = 1\n mainRectFields::GlShaderAndBufferFields = GlShaderAndBufferFields()\n crosshairFields::GlShaderAndBufferFields = GlShaderAndBufferFields()\n textFields::GlShaderAndBufferFields = GlShaderAndBufferFields()\n spacingsValue::Union{Vector{Tuple{Float64,Float64,Float64}},Tuple{Float64,Float64,Float64}} = [(1.0, 1.0, 1.0)]\n originValue::Union{Vector{Tuple{Float64,Float64,Float64}},Tuple{Float64,Float64,Float64}} = [(1.0, 1.0, 1.0)]\n supervoxelFields::GlShaderAndBufferFields = GlShaderAndBufferFields()\nend\n```\n\nAfter the integrity of the fragment shaders was verified in multi-image, voxel data for the images was integrated and further modifications to the high-level functions were made and eventually the following script produced a rather appealing result.\n\nScript for loading the same NIFTI image twice in the visualizer for side-by-side display:\n\n```julia\nusing MedEye3d\nctNiftiImage = \"/home/hurtbadly/Downloads/ct_soft_study.nii.gz\"\nMedEye3d.SegmentationDisplay.displayImage([[ctNiftiImage],[ctNifitImage]])\n```\n>Results in :\n\n![](./multi_image_ct.png)\n\nCrosshair marker for image registration are displayed in the relevant passive image to hightlight the same anatomical regions based on the spatial meta-data of the images i.e spacing, origin and direction. In order to achive the crosshair rendering in the passive image, the following action items were devised:\n\n(a) Retrieval of GLFW Mouse Callbacks for x and y position of the cursor in window coordinates (0 to window-width) from the active image\n\n(b) Conversion of these x and y window coordinates into their relevant active image x and y texture coordinates\n\n(c) Conversion of these texture coordinates into real space point with the help of spatial metadata\n\n(d) Conversion of the real space point into the texture coordinates of the passive image\n\n(e) Conversion of the passive image texture coordinates into their relevant OpenGL coordinate system values (-1 to 1)\n\n(f) Rendering of crosshair on OpenGL coordinate in passive image\n\nConversion between different coordinate systems and accounting for the image's spatial metadata during calculating proved to be challenging at first, but with multiple revisions, a final solution was achieved with seemingly no noticeable amount of lag or delay. One such frame of [CT] images with crosshair display in multi-image is depicted below:\n\n![](./multi_image_ct_crosshair.png)\n\n>Another frame from the openGL rendering cycle, highlighting PET images with crosshair display in multi-image mode:\n\n![](./pet_multi_image.png)\n\n## 6. Adding support for the display of [SuperVoxels](https://doi.org/10.1016/j.cagd.2022.102080) sv with borders within the image slices to better understand anatomical regions within slices\n\nIn enhancing MedEye3d's functionality, supporting super voxels (sv) with boundaries becomes paramount. The sv rendering, effectively capturing gradients, serves as the cornerstone for detecting these boundaries within both MRI and PET volumes. Supervoxels, described either through indicator masks or meshes, encapsulate regions of interest with distinct image characteristics.\nBy integrating boundary detection for super-voxels, MedEye3d can offer enhanced segmentation capabilities, enabling more precise delineation and analysis of anatomical structures and pathological regions within medical imaging data.\n\n[Supervoxels](https://www.sciencedirect.com/topics/computer-science/superpixel) are basically a collection of voxels that share similar image properties. For example: in MRI scans of the brain cortex, super voxels could represent clusters of voxels corresponding to specific anatomical regions or functional areas. The main objective of this task was to add support for the display of super voxel-based segmentation of images, followed by some janitorial tasks:\n\n1. Display of the borders of super-voxels (sv), extracted using the machine learning algorithms.\n\n2. Checking image gradient agreement with super-voxel borders.\n\nThis initial workflow involved, the initialization of relevant buffers in OpenGL for dynamic rendering of lines over the image display, namely vertex array buffers (vao), vertex buffers (vbo) and edge buffers (ebo). Further, these buffers are updated on a scroll event, where the information from the currently displayed slice is passed to the event handler, which invokes a function that updates the vertex buffer (vbo) with new vertices pertaining to the relevant slice number and planar view, precalculated from an [HDF5](https://www.neonscience.org/resources/learning-hub/tutorials/about-hdf5) file during initialization of the visualizer. For instance, if the user is scrolling in the 3rd axis (transversal plane) and is currently on slice 40, the supervoxel display will pertain to edges specifically calculated for that specific slice in that plane.\n\nEventually, with ever so increasing number of attempts and a few hurdles along the way, one of which particularly stood out since it marked our first step towards a good direction:\n\n> Challenges in rendering\n\n![](./supervoxel_rendering_issue.png)\n\nAt last, an appealing result hit our sight.\n\n> Final result\n\n> *Note:* The image borders are intentional to emphasize the size of the visualizer which is currently defaulted to a certain width and height.\n\n![](./supervoxel_rendering_fixed.png)\n\n> *Note:* However, There are a few things left to cover here, most of which revolve around MedImages.jl and documentation for the same. List of PRs that facilitated the completion of the tasks highlighted above:\n\n(a) [https://github.com/JuliaHealth/MedEye3d.jl/pull/21](https://github.com/JuliaHealth/MedEye3d.jl/pull/21)\n\n(b) [https://github.com/JuliaHealth/MedEye3d.jl/pull/20](https://github.com/JuliaHealth/MedEye3d.jl/pull/20)\n\n(c) [https://github.com/JuliaHealth/MedEye3d.jl/pull/16](https://github.com/JuliaHealth/MedEye3d.jl/pull/16)\n\n(d) [https://github.com/JuliaHealth/MedEye3d.jl/pull/14](https://github.com/JuliaHealth/MedEye3d.jl/pull/14)\n\n(e) [https://github.com/JuliaHealth/MedEye3d.jl/pull/13](https://github.com/JuliaHealth/MedEye3d.jl/pull/13)\n\n(f) [https://github.com/JuliaHealth/MedEye3d.jl/pull/12](https://github.com/JuliaHealth/MedEye3d.jl/pull/12)\n\n# Contributions Beyond Coding\n\n## 1. Mentoring and Guidance\n\nI regularly organized meetings with my mentor to seek guidance on project direction and troubleshooting issues in the visualizer. This ensured that I stayed on track, received timely feedback, and addressed any challenges that arose.\n\n## 2. Package Documentation and Community Contribution\n\nI contributed to other medical imaging sub-ecosystem packages in JuliaHealth, including [MedImages.jl](https://github.com/Juliahealth/MedImages.jl) and [MedEval3D.jl](https://github.com/Juliahealth/MedEval3D.jl). Specifically, I set up documentation for these packages using DocuementerVitepress.jl. This not only enhanced the functionality of these packages but also helped maintain a coherent and organized package ecosystem.\n\n## 3. Multirepo Management and Collaboration\n\nIn addition to my work on the MedEye3d visualizer, I made significant contributions to other JuliaHealth repositories, including [MedImages.jl](https://github.com/JuliaHealth/MedImages.jl) and worked over an [Insight Toolkit](https://github.com/InsightSoftwareConsortium/ITK) wrapper library [ITKIOWrapper.jl](https://github.com/JuliaHealth/ITKIOWrapper.jl) for support in image I/O down the road in MedImages.jl. I also maintained relevant documentation and ensured continuous collaboration and synchronization across these packages.\n\n# Conclusions and Future Development\n\nWithin the scope of this 350-hour project, a comprehensive range of objectives were successfully addressed. Noteworthy achievements include:\n\n1. Fixed screen tear and flicker within the visualizer. Integration of threadsafe Julia channels.\n\n2. Achieved multi-image display over CT and PET modalities with crosshair rendering (Although, only one modality can be visualize at a time, i.e either CT | CT or PET | PET).\n\n3. Achieved supervoxel display in single image display mode.\n\n4. Achieved automatic windowing of MRI and PET most common modalities.\n\nFuture work would include:\n\n- Support for the users on Darwin (Apple-based platforms).\n\n- Apart from that, we would need to add a function that dynamically allocates the texture number to the manual modification mask, regardless of the number of images passed for display, which is currently defaulted to 2.\n\n- Also, in the future, we would explore the stretch goals a bit more rigorously, particularly the implementation of [MedVoxelHD](https://doi.org/10.1016/j.softx.2024.101744) within MedEye3d.\n\n# Acknowledgements πŸ™‡β€β™‚οΈ\n\n1. [Jakub Mitura](https://orcid.org/0000-0003-1823-6823): aka, [Dr. Jakub Mitura](https://github.com/jakubMitura14)\n\n2. [Carlos Castillo Passi](https://scholar.google.com/citations?user=WzleS8YAAAAJ&hl=en): aka, [cncastillo](https://github.com/cncastillo)\n\nI would like to thank my mentor Dr. Jakub Mitura, for his help through out every phase of this project. The troubleshooting routines around problems would have rendered the project unsuccessful, if not for the support and guidance of my mentor throughout each part of this project. I would also like to thank Jacob Zelko, for leading the Juliahealth community with such vast expertise and leading efforts for engagement amongst the members through monthly meetings. My sincere gratitude towards your support, help and guidance through out the fellowship.\n\n", + "supporting": [ + "gsoc-2024-fellows_files/figure-html" + ], + "filters": [], + "includes": {} + } +} \ No newline at end of file diff --git a/_freeze/blog/posts/dummy/index/execute-results/html.json b/_freeze/blog/posts/dummy/index/execute-results/html.json new file mode 100644 index 0000000..087246a --- /dev/null +++ b/_freeze/blog/posts/dummy/index/execute-results/html.json @@ -0,0 +1,12 @@ +{ + "hash": "7d5dad8f3ee7be424d689c2679bf2562", + "result": { + "engine": "julia", + "markdown": "---\ntitle: \"Dummy Post\"\ndescription: \"Post description\"\nauthor: \"Foobar\"\ndate: \"6/22/2024\"\ntoc: true\nengine: julia\ncategories:\n - news\n - code\n - analysis\n---\n\n\n\n\n# Seciton 1\n\nSmall dummy blog post\n\n\n\n\n::: {#2 .cell execution_count=1}\n``` {.julia .cell-code}\n2 + 2\n```\n\n::: {.cell-output .cell-output-display execution_count=1}\n```\n4\n```\n:::\n:::\n\n\n\n::: {#4 .cell execution_count=1}\n``` {.julia .cell-code}\nprintln(2 + 2)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n4\n```\n:::\n:::\n\n\n\n\n\n\n# Section 2\n\n# Section 3\n\n", + "supporting": [ + "index_files" + ], + "filters": [], + "includes": {} + } +} \ No newline at end of file diff --git a/_freeze/blog/posts/jay-gsoc/gsoc-2024-fellows/execute-results/html.json b/_freeze/blog/posts/jay-gsoc/gsoc-2024-fellows/execute-results/html.json new file mode 100644 index 0000000..45b12f6 --- /dev/null +++ b/_freeze/blog/posts/jay-gsoc/gsoc-2024-fellows/execute-results/html.json @@ -0,0 +1,12 @@ +{ + "hash": "324bd489afc82dd6b096e80510af90c7", + "result": { + "engine": "julia", + "markdown": "---\ntitle: \"GSoC '24: Developing Tooling for Observational Health Research in Julia\"\ndescription: \"A summary of my project for Google Summer of Code - 2024\"\nauthor: \"Jay Sanjay Landge\"\ndate: \"9/7/2024\"\nbibliography: ./references.bib\ncsl: ./../../ieee-with-url.csl\ntoc: true\nengine: julia\nimage: false\ncategories:\n - gsoc\n - sql\n - observational health \n - analysis\n---\n\n\n\n\n\n# Hi Everyone! πŸ‘‹\n\nI am Jay Sanjay, and I am pursuing a Bachelor's degree in Computational Sciences and Engineering at the Indian Institute of Technology (IIT) in Hyderabad, India. Coming from a mathematics and data analysis background, I was initially introduced to Julia at my university lectures. Later, I delved more into the language and the JuliaHealth community - an intersection of Julia, Health Research, Data Sciences, and Informatics. Here, I met some of the great folks in JuliaHealth and I decided to take it on as a full-fledged summer project. \nIn this blog, I will briefly describe what my project is and what I did as a part of it.\n\n\n1. You can find my [**GSoC project archive link**](https://summerofcode.withgoogle.com/archive/2024/projects/ZXVIYAXG)\n\n2. You can also find the related publication of my work on [**Zenodo**](https://zenodo.org/records/14674051)\n\n3. If you want to know more about me, you can connect with me on [**LinkedIn**](https://www.linkedin.com/in/jay-landge-589439260/) and follow me on [**GitHub**](https://github.com/Jay-sanjay)\n\n\n# Background\n\n## What Is Observational Health Research?\n\nObservational Health Research refers to studies that analyze real-world data (such as patient medical claims, electronic health records, etc.) to understand patient health. These studies often encompass a vast amount of data concerning patient care. An outstanding challenge here is that these datasets can become very complex and grow large enough to require advanced computing methods to process this information.\n\n## What Are Patient Pathways?\n\nPatient pathways refer to the journey that patients with specific medical conditions undergo in terms of their treatment. This concept goes beyond simple drug uptake statistics and looks at the sequence of treatments patients receive over time, including first-line treatments and subsequent therapies. Understanding patient pathways is essential for analyzing treatment patterns, adherence to clinical guidelines, and the disbursement of drugs.\nTo analyze patient pathways, one would typically use real-world data from sources such as electronic health records, claims data, and registries. However, barriers such as data interoperability and resource requirements have hindered the full utilization of real-world data for this purpose.\n\nSo to address these challenges we (the JuliaHealth organization and I) want to develop a set of tools to extract and analyze these patient pathways. These sets of tools are based on the Observational Medical Outcomes Partnership (OMOP) Common Data Model, which standardizes healthcare data to promote interoperability.\n\n\n# Project Description\n\nAs part of this project with JuliaHealth, I developed a new package called [**OMOPCDMPathways.jl**](https://github.com/JuliaHealth/OMOPCDMPathways.jl). This package is designed for deployment in research projects, particularly those related to health and medical data analysis. This project takes much inspiration from the paper [_TreatmentPatterns: An r package to facilitate the standardized development and analysis of treatment patterns across disease domains_](https://www.sciencedirect.com/science/article/pii/S016926072200462X?via%3Dihub) [@markus2022treatmentpatterns] and explores the implementation of some of those ideas to develop new tools within the JuliaHealth Observational Health Subecosystem for exploring patient pathways. Additional new features and approaches were added and explored within this project. Additionally, I have authored a developer guide for the package, providing instructions on its use and contribution. This project provided me with hands-on experience in developing production-level code and exposed me to open-source software development practices. I had the opportunity to work in a team, under my mentors, and ensured the integration of the package with the rest of JuliaHealth, facilitating its adoption and usability within the community. \n\n# Project Goals\n\nAs a part of the development, I was majorly engaged in crafting the following functionalities:\n\n1. Selecting treatments of interest: The first decision that was made was to decide the time from which the desired treatments of interest should be included in the treatment pathway study. Here the [periodPriorToIndex](https://github.com/JuliaHealth/OMOPCDMPathways.jl/issues/1) specifies the period (i.e. number of days) before the index date from which treatments should be included.\n\n2. Find Treatment History of Patients: Create the [treatment history](https://github.com/JuliaHealth/OMOPCDMPathways.jl/issues/4) of a patient based on target, event, and exit cohorts. Then filter patient events based on the start and end dates of the target cohort. Third, Calculate the duration of treatment eras and the gap between treatments.\n\n3. Filters: Filter the treatment history based on the [minEraDuration](https://github.com/JuliaHealth/OMOPCDMPathways.jl/issues/5) parameter and [EraCollapse](https://github.com/JuliaHealth/OMOPCDMPathways.jl/issues/2) parameter.\n\n4. Create a Continuous Integration and Continuous Development pipeline for the package. \n\n5. Implement the combinationWindow function, which combines treatments with various overlapping strategies.\n\nAdditionally, we had a few stretch goals which were:\n\n1. Composing with JuliaStats Ecosystem\n\n2. Novel Visualizations for Pathways\n\n# Tasks\n\n## 1. Setting Up the Package in JuliaHealth Channel\n\nInitially, there was no package as such for generating pathways, so I had to build it from scratch. First, I created the repository with the name [OMOPCDMPathways.jl](https://github.com/JuliaHealth/OMOPCDMPathways.jl). Once the repository was created, we needed to have a skeleton for a standard Julia repository. For this, we used the [PkgTemplates.jl](https://juliaci.github.io/PkgTemplates.jl/stable/user/) this provided a basic skeleton for the repository that included - folders for test suites, documentation, src code files, GitHub files, README and LICENSE file, TOML and citation files. All this we can further edit and modify as per our work. By default, PkgTemplate.jl uses [Documenter.jl](https://documenter.juliadocs.org/stable/) for the documentation part but as suggested and discussed with my mentor we decided to shift to [DocumenterVitepress.jl](https://luxdl.github.io/DocumenterVitepress.jl/dev/) for the documentation part. However, we still faced some deployment issues in the new documentation due to a few mistakes in the `make.jl` file, thanks to [Anshul Singhvi](https://github.com/asinghvi17) for helping fix the [Deployment issues with DocumenterVitepress](https://github.com/JuliaHealth/OMOPCDMPathways.jl/issues/15). With this, we were ready with the documentation set up and fully functional. After we had shifted to DocumenterVitepress the main task now was to host the documentation, this was done using Github-Actions, detailed steps for hosting are provided at [this](https://documenter.juliadocs.org/stable/man/hosting/#Hosting-Documentation) page. Then we added the CodeCov to our package by triggering it via a dummy function and a corresponding test case for it. Also, the CI for the package was set up with it. And, now finally the repository was ready with test coverage, CI, and documentation fully functional repository ready. Here's some snapshots of the documentation set-up:\n\n![](./image.png)\n\n> Initial documentation with Documenter.jl\n\n![](./img2.png)\n\n> New documentation using DocumenterVitepress.jl\n\nSo, as a part of it, I created this [documentation](https://luxdl.github.io/DocumenterVitepress.jl/dev/documenter_to_vitepress_docs_example) which provides detailed steps for converting docs from Documenter to DocumenterVitepress.\n\n## 2. Loading the PostgreSQL Database\n\nThe main database we worked on/built analysis was the freely available OMOPCDM Database. The Database was formatted within a PostgreSQL database with [installation instructions here](https://www.devart.com/dbforge/postgresql/how-to-install-postgresql-on-linux/) are some instructions on how to set up Postgres in a Linux machine. However, I was provided with some more extra synthetic data from my mentor for further testing of the functionalities. Being a very large database we had to strategically download it further, my mentor helped me in setting up the Postgres on my local machine. Once, the database was set up proper testing was performed on it to check if things were as expected. With this, we were done with the database setup as well and could finally dive into the actual code logic for the Pathways synthesis.\n\n## 3. Testing and Development setup on my local computer\n \nTo get a proper environment for functionality creation and concurrent testing we required a proper testing setup so that we could test the new functions made at the same time. This was done using [Revise.jl](https://timholy.github.io/Revise.jl/stable/), which helps to keep Julia sessions running without frequent restarts when making changes to code. It allowed me to edit my code, update packages, or switch git branches during a session, with changes applied immediately in the next command. My mentor helped me set it up, added Revise.jl to the global Julia environment, also [PackageCompatUI](https://github.com/GunnarFarneback/PackageCompatUI.jl) that provides a terminal text interface to the [compat] section of a Julia Project.toml file, and finally made a Julia script by the name β€œstartup.jl” out of it. This script was then added to `/home/jay-sanjay/.julia/config/` path in my local computer. \n\nHere is the sample for the startup.jl file:\n\n```julia\nusing PackageCompatUI\nusing PkgTemplates\nusing Revise\n\n###################################\n# HELPER FUNCTIONS\n###################################\nfunction template()\n Template(;\n user=\"jay-sanjay\",\n dir=\"~/FOSS\",\n authors=\"jaysanjay and contributors\",\n julia=v\"1.6\",\n plugins=[\n ProjectFile(; version=v\"0.0.1\"),\n Git(),\n Readme(),\n License(; name=\"MIT\"),\n GitHubActions(; extra_versions=[\"1.6\", \"1\", \"nightly\"]),\n TagBot(),\n Codecov(),\n Documenter{GitHubActions}(),\n Citation(; readme = true),\n RegisterAction(),\n BlueStyleBadge(),\n Formatter(;style = \"blue\")\n ],\n )\nend\n\n```\n\n\n## 4. Selecting Treatments of Interest\n\nSo, as a part of this, we used the previously mentioned research paper and discussion with the mentors we came up with logic for it. The first thing to do was to determine the moment in time from which selected treatments of interest should be included in the treatment pathway. The default is all treatments starting after the index date of the target cohort. For example, for a target cohort consisting of newly diagnosed patients, treatments after the moment of first diagnosis are included. However, it would also be desirable to include (some) treatments before the index date, for instance in case a specific disease diagnosis is only confirmed after initiating treatment. Therefore, periodPriorToIndex specifies the period (i.e. number of days) before the index date from which treatments should be included. We have created two dispatches for this function.\nAfter that proper testing and documentation are also added.\n\nA basic implementation for it is:\n\n1. Construct a SQL query to select cohort_definition_id, subject_id, and cohort_start_date from a specified table, filtering by cohort_id.\n\n2. The SQL query construction and execution was done using the [FunSQL.jl](https://mechanicalrabbit.github.io/FunSQL.jl/stable/) library, in the below-shown manner:\n\n```julia\nsql = From(tab) |>\n Where(Fun.in(Get.cohort_definition_id, cohort_id...)) |>\n Select(Get.cohort_definition_id, Get.subject_id, Get.cohort_start_date) |>\n q -> render(q, dialect=dialect)\n```\n3. Executes the constructed SQL query using a database connection, fetching the results into a data frame.\n\n4. If the DataFrame is not empty, convert cohort_start_date to DateTime and subtract date_prior from each date, then return the modified DataFrame.\n\nThis was then be called this:\n```julia\nperiod_prior_to_index(\n cohort_id = [1, 1, 1, 1, 1], \n conn; \n date_prior = Day(100), \n tab=cohort\n )\n```\n\n\n## 5. Filters Applied\n\nAfter this, we where needed to get the patient's database filtered more finely so that there are minimal variations that can be ignored. The duration of the above extracted event eras may vary a lot and it can be preferable to limit to only treatments exceeding a minimum duration. Hence, minEraDuration specifies the minimum time an event era should last to be included in the analysis. All these implementations were more of Dataframe manipulation where I used [DataFrames.jl](https://dataframes.juliadata.org/stable/) package.\n\nAfter that proper testing and documentation are also added.\n\nA basic implementation for the minEraDuration is:\nIt filters the treatment history `DataFrame` to retain only those rows where the duration between `drug_exposure_end` and `drug_exposure_start` is at least `minEraDuration`.\nThis function can be used as follows:\n```julia\n#| eval: false \n\ncalculate_era_duration(test_df, 920000)\n\n#= ... =#\n\n4Γ—3 DataFrame\n Row β”‚ person_id drug_exposure_start drug_exposure_end \n β”‚ Int64 Float64 Int64 \n─────┼───────────────────────────────────────────────────\n 1 β”‚ 1 -3.7273e8 -364953600\n 2 β”‚ 1 2.90304e7 31449600\n 3 β”‚ 1 -8.18208e7 -80006400\n 4 β”‚ 1 1.32918e9 1330387200\n```\n\n\nAnother filter we worked on is the EraCollapse. So, let's suppose a case where an individual receives the same treatment for a long period\nof time (e.g. need for chronic treatment). Then it's highly likely that the person would require refills. Now as patients are not 100% adherent, there might be a gap between two subsequent event eras. Usually, these eras are still considered as one treatment episode, and the eraCollapseSize deals with the maximum gap within which two eras of the same event cohort would be collapsed into one era (i.e. seen as a continuous treatment instead of a stop and re-initiation of the same treatment).\nAfter that proper testing and documentation are also added.\n\nA basic implementation for the eraCollapseSize is:\n(a) Sorts the data frame by event_start_date and event_end_date.\n(b) Calculates the gap between each era and the previous era.\n(c) Filters out rows with gap_same > eraCollapseSize.\n\nThese functions can be used as follows:\n```julia\n#| eval: false \n\n#= ... =#\n\nEraCollapse(treatment_history = test_df, eraCollapseSize = 400000000)\n4Γ—4 DataFrame\n Row β”‚ person_id drug_exposure_start drug_exposure_end gap_same \n β”‚ Int64 Float64 Int64 Float64 \n─────┼───────────────────────────────────────────────────────────────\n 1 β”‚ 1 -5.33347e8 -532483200 -1.86373e9\n 2 β”‚ 1 -3.7273e8 -364953600 1.59754e8\n 3 β”‚ 1 -8.18208e7 -80006400 2.83133e8\n 4 β”‚ 1 2.90304e7 31449600 1.09037e8\n```\n\n\n## 6. Treatment History of the Patients\n\nThe `create_treatment_history` function constructs a detailed treatment history for patients in a target cohort by processing and filtering event cohort data from a given DataFrame. It begins by isolating the target cohort based on its `cohort_id`, adding a new column for the `index_year` derived from the cohort’s start date. Then, it selects relevant event cohorts based on a provided list of cohort IDs and merges them with the target cohort on the `subject_id` to associate events with individuals in the target group. The function applies different filtering criteria depending on whether the user is interested in treatments starting or ending within a specified period before the target cohort's start date (defined by `periodPriorToIndex`). It keeps only the event cohorts that match the filtering condition, ensuring that only relevant treatments are considered. After filtering, the function calculates time gaps between consecutive cohort events for each patient, adding these gaps to the DataFrame. The final DataFrame provides a history of treatments, including the dates of events and the time intervals between them, offering a clear timeline of treatment for each patient. After that proper testing and documentation are also added.\n\n\n## 7. CombinationWindow Functionality To Combine Overlapping Treatments\n\nNow once we have the filtering of the treatments done, we need to combine the overlapping treatments based on some set of rules. The combinationWindow specifies the time that two event eras need to overlap to be considered a combination treatment. If there are more than two overlapping event eras, we sequentially combine treatments, starting from the first two overlapping event eras. \n\nThe `combination_Window` function processes a patient's treatment history by identifying overlapping treatment events and combining them into continuous treatment periods based on certain rules. It first converts `event_cohort_id` into strings and sorts the treatment data by `person_id`, `event_start_date`, and `event_end_date`. The helper function `selectRowsCombinationWindow` calculates gaps between consecutive treatments, marking rows where treatments overlap or occur too closely. In the main loop, the function checks these overlaps and gaps against a specified `combinationWindow`. If treatments overlap (or nearly overlap), the function adjusts the treatment periods by either merging adjacent rows or splitting rows to create continuous treatment periods. The process continues until all overlapping treatments are combined into one, creating an updated and accurate treatment history. The function ensures the final output reflects realistic treatment windows by handling special cases where gaps between treatments are smaller than the treatment durations themselves.\n\nIt mainly covers the three cases mentioned in the R-research paper:\n\n### Switch Case:\n\n*Condition*: If the gap between the two treatment events is smaller than the combinationWindow, but the gap is not equal to the duration of either event.\n*Action*: The event_end_date of the previous treatment is set to the event_start_date of the current treatment. This effectively \"shifts\" the previous treatment’s end date to eliminate the gap, merging the treatments into one continuous period.\n*Purpose*: This ensures that treatment gaps that are too small (less than combinationWindow) are treated as part of the same treatment window.\n\n```julia\n#| eval: false \n\n#= ... =#\n\nif -gap_previous < combinationWindow && !(-gap_previous in [duration_era, prev_duration_era])\n treatment_history[i-1, :event_end_date] = treatment_history[i, :event_start_date]\n```\nHere is the pictorial representation for the same:\n![](./case1.png)\n\n### FRFS (First Row, First Shortened):\n\n*Condition*: If the gap is larger than or equal to the combinationWindow, or the gap equals the duration of one of the two treatments, and the first treatment ends before or on the same date as the second treatment.\n*Action*: A new row is created where the second treatment’s event_end_date is set to the end date of the first treatment. This preserves the overlap but ensures that the earlier treatment period stays intact.\n*Purpose*: This prevents unnecessary truncation of the first treatment if it spans the entire overlap window.\n\n```julia\n#| eval: false \n\n#= ... =#\n\nelseif -gap_previous >= combinationWindow || -gap_previous in [duration_era, prev_duration_era]\n if treatment_history[i-1, :event_end_date] <= treatment_history[i, :event_end_date]\n new_row = deepcopy(treatment_history[i, :])\n new_row.event_end_date = treatment_history[i-1, :event_end_date]\n append!(treatment_history, DataFrame(new_row'))\n```\nHere is the pictorial representation for the same:\n![](./case2.png)\n\n### LRFS (Last Row, First Shortened):\n\n*Condition*: If the gap is larger than or equal to the combinationWindow, or the gap equals the duration of one of the treatments, and the first treatment ends after the second treatment.\n*Action*: The current treatment’s event_end_date is adjusted to match the event_end_date of the previous treatment.\n*Purpose*: This handles cases where the second treatment's window should be shortened to prevent overlap with the previous treatment, merging them into a single continuous window.\n\n```julia\n#| eval: false \n\n#= ... =#\n\nelse\n treatment_history[i, :event_end_date] = treatment_history[i-1, :event_end_date]\n```\nHere is the pictorial representation for the same:\n![](./case3.png)\n\n\n> *Note:* However, There are a few things left to cover here, most of which are the documentation and writing the test suite for the same.\n\n# Contributions Beyond Coding\n\n## 1. Organizing Meetings and Communication\n\nThroughout the project, I regularly met with my mentor, [Jacob Zelko], and co-mentor, [Mounika], via weekly Zoom calls to discuss progress and seek guidance. During these meetings, we reviewed my work, identified areas where I needed help, and set clear goals for the upcoming weeks. We used Trello to organize and track these goals, ensuring that nothing was overlooked. My mentors provided detailed insights into specific technical aspects and guided me through the logic behind various functions. Outside of our scheduled meetings, they were always available for quick queries via Slack, ensuring constant support.\n\n## 2. Personal Documentation\n\nIn addition to the notes from our meetings, I maintained personal documentation where I recorded every step I took, including the challenges I faced and the mistakes I made. This helped me reflect on my progress and stay organized throughout the fellowship. Following my selection for GSoC 2024, I also published a blog post on [Medium](https://medium.com/@landgejay124/gsoc-24-the-julia-language-62b809bbec49) to share my journey and experiences with the Julia Language community.\n\n## 3. Contributions To the Rest of the JuliaHealth Repositories\n\nEarlier I have contributed a lot to the [OMOPCDMCohortCreator.jl](https://github.com/JuliaHealth/OMOPCDMCohortCreator.jl) including adding new functionalities writing test suites, adding blogs including - [Patient Pathways within JuliaHealth](https://github.com/JuliaHealth/juliahealth.github.io/pull/124). Apart from that I also initiated 3 new releases of this package.\n\n# Conclusions and Future Development\n\nThis project was a 350-hour large project since there were many goals to accomplish. Here is what we accomplished:\n\n1. Built a new repository in JuliaHealth land dedicated especially to treatment pathways synthesis.\n\n2. CI/CD for the Package and Documentation hosting.\n\n3. All required basic functionalities required to build the pathways.\n\n4. Documentation and test suites added for each.\n\nFuture work would include:\n\n- Finish this [PR](https://github.com/JuliaHealth/OMOPCDMPathways.jl/pull/63) test-suites and documentation are remaining for this PR. \n\n- Apart from that, we would need to add a [function](https://github.com/JuliaHealth/OMOPCDMPathways.jl/issues/9) that sews up all the functionalities of the package so that the user can run the complete pathways analysis by running just one function instead of running each of the functions manually. \n\n- Also, in the future, we would explore what statistical functionalities we would need to further explore pathways. \n\n- Then, we could explore how to compose JuliaHealth with packages from ecosystems like [JuliaStats](https://juliastats.org/) and [JuliaDSP](https://docs.juliadsp.org/stable/contents/) (for time series analysis) that are mentioned in this [PR](https://github.com/JuliaHealth/OMOPCDMPathways.jl/issues/8). \n\n- And finally work on creating novel visualizations for the Pathways. Commonly used visualizations for treatment pathways (such as sunburst or icicle plots) showing which patients fall under what treatment pathways could be developed as plotting recipes to visualize various aspects of a patient’s care pathway rapidly.\n\n# Acknowledgements πŸ™‡β€β™‚οΈ\n\n1. [Jacob S. Zelko](https://jacobzelko.com): aka, [TheCedarPrince](https://github.com/TheCedarPrince)\n\n2. [Mounika Thakkallapally](https://www.linkedin.com/in/mounika-thakkallapally/)\n\nThank you for your continuous help and support throughout the fellowship.\n_Note: This blog post was also written with the assistance of LLM technologies to help with grammar, flow, and spelling!_\n\n\n", + "supporting": [ + "gsoc-2024-fellows_files/figure-html" + ], + "filters": [], + "includes": {} + } +} \ No newline at end of file diff --git a/_freeze/blog/posts/michela-gsoc/Michela_JSoC/execute-results/html.json b/_freeze/blog/posts/michela-gsoc/Michela_JSoC/execute-results/html.json new file mode 100644 index 0000000..e3a48d3 --- /dev/null +++ b/_freeze/blog/posts/michela-gsoc/Michela_JSoC/execute-results/html.json @@ -0,0 +1,12 @@ +{ + "hash": "55d28893470821c6ce2b9a35078cfa9b", + "result": { + "engine": "julia", + "markdown": "---\ntitle: \"GSoC '24: IPUMS.jl Small Project\"\ndescription: \"A summary of my project for Google Summer of Code\"\nauthor: \"Michela Rocchetti\"\ndate: \"8/26/2024\"\ntoc: true\nengine: julia\nimage: false\ncategories:\n - gsoc\n - geospatial \n - census\n---\n\n\n\n\n# Hello! πŸ‘‹\n\nHi! I am Michela, I have a Master's degree in Physics of Complex Systems and I am currently working as a software engineer in Rome, where I am from. \nDuring my studies, I became interested in the use of modeling and AI methods to improve healthcare and how these tools can be used to better understand how cultural and social backgrounds influence the health of individuals. \nI am also interested in the computational modeling of the brain and the human body and its implications for a better understanding of certain pathological conditions. \n\nWith these motivations in mind, I heard about Google Summer of Code. \nSince I had studied Julia in some courses and given that the language is expanding rapidly, I decided to find a project within Julia. \nAs a result, I found the project of [Jacob Zelko (@TheCedarPrince)](https://jacobzelko.com) to start this experience. \n\n> If you want to learn more about me, you can connect with me here: [**LinkedIn**](https://www.linkedin.com/in/michela-rocchetti-261793218/), [**GitHub**](https://github.com/MichelaRocchetti)\n\n# Project Description \n\n*IPUMS* is the \"world's largest available single database of census microdata\", providing survey and census data from around the world. \nIt includes several projects that provide a wide variety of datasets.\nThe information and data collected by *IPUMS* are useful for comparative research, as well as for the analysis of individuals in their life contexts.\nThese data can be used to create a more comprehensive dataset that will facilitate research on the social determinants of health for different types of diseases, social communities, and geographical areas. \n\n![](./IPUMS_grid_logo.png)\n\n> To learn more about IPUMS, visit the [website](https://www.ipums.org) \n\n# Tasks and Goals\nThe primary objectives of this proposal are to:\n\n1. Develop a native Julia package to interact with the APIs available around the datasets *IPUMS* provides.\n\n2. Provide useful utilities within this package for manipulating *IPUMS* datasets.\n\n3. Compose this package with the wider Julia ecosystem to enable novel research in health, economics, and more. \n\nTo achieve this, the work was distributed as follows:\n\n1. Expand some of the functionality developed in `ipumsr` *IPUMS* NHGIS\n - Create a link between OpenAPI documentation and the functions internally used in IPUMS.jl:\n updating already present functions, determining if updating is needed, and testing them\n - Develop functionality similar to the get_metadata_nghis function present in ipumsr\n\n2. Update *IPUMS* documentation\n - Set up and deploy DocumenterVitepress.jl \n - Write a blog post on how IPUMS.jl can be composed within the ecosystem.\n\n\n# How the work was done\n\nThe first task was to migrate documents from Documenter to DocumenterVitepress.This issue aims to support the significant refactoring underway across JuliaHealth, aimed at improving the discoverability and cohesion of the JuliaHealth ecosystem, particularly about documentation. This issue is intended to create a more attractive entry point for new Julia users interested in health research within the Julia community.\nTo accomplish this task, a dependency of DocumenterVitepress was added to the docs directory of the IPUMS.jl repository. \nOnce this was done, the Documenter.jl make.jl file was migrated into a DocumenterVitepress.jl make.jl file. Working on the make.jl file, the pages structure were added to the web page explaining the IPUMS.jl package. With this in mind, those were added:\n 1. Home: to explain the main purpose of the package\n 2. Workflows: to explain the working process\n 3. How to: to give general information \n 4. Tutorials: to show how to use IPUMS.jl \n 5. Examples: some examples of activities\n 6. Mission: to explain why the package is useful for the community\n 7. References: references used to write the pages.\n\nThis first task takes some time, especially setting up GitHub and cloning the repository locally. At this point, my experience with GitHub was really limited and I had to learn how to use the Git environment from scratch, for example how to do continuous integration (to commit code to a shared repository), documentation release and merge, and local testing. I found the support of my mentors and searching for material online was really helpful. \n\nThe second task was to update the documentation of IPUMS.jl by modifying the functionality within the model folder in the IPUMS.jl folder. The main aim of this task was to\na description of the function and its attributes, an example of possible implementation and result, and finally to show how to use it. The documentation to be updated as of several types of functions:\n 1. Data extract\n 2. Data set\n 3. Data Table\n 4. Time series table\n 5. Error\n 6. Shapefile.\n Each of these macro-categories (from 1 to 4) contains a set of functions, each signaling the different expected output and specific purpose.\n Information about what each function does, and the meaning of each specific input variable, has been found on the *IPUMS* website and references have been made in the written documentation.\n\n# How to work with IPUMS\n After writing down the description of the function and the inputs, examples were formulated, starting from the *IPUMS* website: when you register at [IPUMS](https://uma.pop.umn.edu/usa/user/new), an API key is given. \nwhich is used, among other things, to run pre-written code on the website. This code contains examples of these functions, and these examples \nhave been adapted by changing some input values and adapting them to work in the Julia framework. The latter task was done by simply rewriting some structures, such as dictionaries, maps, or lists, in the\nJulia language. \nHere is a small guide on how to set up working with the API:\n1. Create an *IPUMS* account\n2. Log in to your account \n3. Copy the API key, which can be obtained from the [website](https://account.ipums.org/api_keys)\n4. Use the key to run the code that is already available on the [*IPUMS* Developer Portal](https://developer.ipums.org/docs/v2/reference/), where you will also find information about the variables and packages.\n\n# Functions testing\n\n A final task was to test the functions in the 'api_IPUMSAPI.jl' file. In this file, the function to be tested and other functions are defined and the most important ones are extracted to be available in the\n available throughout the framework. Some of the functions to be tested were the following:\n \n 1. `metadata_nhgis_data_tables_get`\n 2. `metadata_nhgis_datasets_dataset_data_tables_data_table_get`\n 3. `metadata_nhgis_datasets_dataset_get`\n 4. `metadata_nhgis_datasets_get`\n\n Before working on the Julia files, testing and understanding the original R function was done using R studio. \n\n![](./rstudio.png)\n\nEach function was then tested using the API key from the *IPUMS* registration as well as other input examples taken from the documentation or the *IPUMS* website. \nor from the *IPUMS* website. All functions were displayed successfully, giving the expected result, so it can be concluded that the translation from R to Julia is successful.\n\n\n\n\n::: {#2 .cell execution_count=0}\n``` {.julia .cell-code}\nusing IPUMS\nusing OpenAPI\n\napi_key = \"insert your key here\"\n\nversion = \"2\"\npage_number = 1\npage_size = 2500\n#media_type = \n\napi = IPUMSAPI(\"https://api.ipums.org\", Dict(\"Authorization\" => api_key));\n\nres1 = metadata_nhgis_data_tables_get(api, version)\n\nres2 = metadata_nhgis_datasets_dataset_get(api, \"2022_ACS1\", \"2\");\n\nres3 = metadata_nhgis_datasets_dataset_data_tables_data_table_get(api, \"2022_ACS1\",\"B01001\", \"2\");\n\nres4 = metadata_nhgis_datasets_get(api, \"2\");\n```\n:::\n\n\n\n\n\n\nAn example of the output is: \n\n\n\n\n```{json}\n. . .\n\n{\n \"name\": \"NT1\",\n \"nhgisCode\": \"AAA\",\n \"description\": \"Total Population\",\n \"universe\": \"Persons\",\n \"sequence\": 1,\n \"datasetName\": \"1790_cPop\",\n \"nVariables\": [\n 1\n ]\n}\n\n. . .\n```\n\n\n\n\n# Accomplished Goals and Future Development\n\nThe project was a 90-hour small project and during this time the documentation was completed and the testing of the metadata function was done, as well as the migration from Documenter.jl to DocumenterVitepress.jl.\nDuring these months some things took longer than I expected because of some problems that occurred, so some things were missing in relation to the original plan. However, this time was useful for learning new things: \n - I saw how to work with a package under development, how to work with large datasets, and how to write documentation \n - I had the opportunity to better understand how to work with Git and GitHub\n - I learned some new things about R, which was a completely unknown language to me. \n - I deepened my knowledge of Julia, a language I had worked with during my time at university.\n - I had the chance to work on a large open-source project, to be part of a large community, and to learn how to communicate with it efficiently. \n\nA special thanks goes to my mentors, Jacob Zelko and Krishna Bhogaonker, for helping me through this process.\n\nFuture developments of this work could include deepening the work that my mentors and I have started, with the possibility of integrating this package with other machine learning packages in Julia and, from there, doing new analyses of the data in terms of social and geographical implications for health.\n\n", + "supporting": [ + "Michela_JSoC_files" + ], + "filters": [], + "includes": {} + } +} \ No newline at end of file diff --git a/_freeze/blog/posts/mounika-gsoc-mentor/index/execute-results/html.json b/_freeze/blog/posts/mounika-gsoc-mentor/index/execute-results/html.json new file mode 100644 index 0000000..1738344 --- /dev/null +++ b/_freeze/blog/posts/mounika-gsoc-mentor/index/execute-results/html.json @@ -0,0 +1,12 @@ +{ + "hash": "aa8ac27b7ea412105115a41b1ab4c229", + "result": { + "engine": "julia", + "markdown": "---\ntitle: \"GSoC Co-Mentoring Experience\"\ndescription: \"My experience as a GSoC co-mentor within JuliaHealth\"\nauthor: \"Mounika Thakkallapally\"\ndate: \"9/12/2024\"\ntoc: true\nengine: julia\nbibliography: ./references.bib\ncsl: ./../../ieee-with-url.csl\ncategories:\n - gsoc\n - mentor\n - experience\n---\n\n\n\n\n# Introduction\n\nHello πŸ‘‹, I am Mounika. I am a Data Engineer at [Brown Center for Biomedical Informatics](https://bcbi.brown.edu/). This summer, I had the privilege of co-mentoring a talented student, [Jay Sanjay](https://www.linkedin.com/in/jay-landge-589439260/) alongside [Jacob Zelko (\\@TheCedarPrince)](https://jacobzelko.com) on a [project](https://summerofcode.withgoogle.com/programs/2024/projects/ZXVIYAXG) for Google Summer of Code (aka [GSoC](https://summerofcode.withgoogle.com/)). Here, I would like to share my experience as a co-mentor, offering insights for future mentors and students alike. \n\nBefore diving into my experience, let me provide some background on how it all started. At JuliaCon 2023, I had the chance to meet Jacob Zelko and have been following his work at [JuliaHealth](https://juliahealth.org/) ever since. One day, I received a message from Jacob asking if I'd be interested in co-mentoring Jay for his GSoC project. Fortunately, I was already working on several projects at BCBI involving Julia programming, [OMOP CDM databases](https://ohdsi.github.io/CommonDataModel/cdm54.html) and [OHDSI](https://ohdsi.org/) tools, all of which were closely aligned with Jay's project.\n\n> Feel free to visit Jay's work on [OMOPCDMPathways.jl](https://github.com/JuliaHealth/OMOPCDMPathways.jl) or read about his [fellowship experience from this post](https://juliahealth.org/JuliaHealthBlog/posts/jay-gsoc/gsoc-2024-fellows.html).\n\n# Mentor-Mentee Relationship\n\nJay, being a proactive student with a strong involvement in JuliaHealth, worked closely with Jacob to build a [proposal for the project](https://summerofcode.withgoogle.com/organizations/the-julia-language/projects/details/ZXVIYAXG) several months before GSoC began this year. His early involvement and familiarity with the community set a solid foundation for the project. Jacob, with his extensive experience mentoring GSoC students over the years, brought invaluable insights not only for Jay but also for me, as I was just beginning my journey as a mentor.\n\nJacob established regular weekly Zoom meetings for the three of us to discuss Jay's progress, review his accomplishments, and plan the next steps. During these meetings, I focused on taking detailed notes to ensure we stayed organized and up to date with all the tasks. We used [Trello](https://trello.com/), a project management tool, to track progress and manage project tasks efficiently. Additionally, we stayed connected thoughout the week via a dedicated slack channel for any ongoing discussions or questions (on the [Julia Slack](https://julialang.org/slack/#the_julia_language_slack)).\n\n# Technical Discussion \n\nJay's project \"Developing Tooling for Observational Health Research in Julia\" was inspired by the [TreatmentPatterns R package](https://www.sciencedirect.com/science/article/pii/S016926072200462X?via%3Dihub) [@markus2022treatmentpatterns]. The main goal of the project was to enhance observational research capabilities within the JuliaHealth ecosystem. To help Jay get started, [Jacob created 10 to 15 GitHub issues](https://github.com/JuliaHealth/OMOPCDMPathways.jl/issues?q=), each linked to a specific function that Jay planned to work on.\n\nDuring our weekly meeting, we discussed the challenges Jay encountered, any roadblacks in his progress, and reviewed the pull requests he submitted on GitHub. These sessions allowed us to provide timely feedback and guide Jay through complex technical issues, ensuring steady progress throughout the project.\n\n# Learnings and Observations\n\nJay's proactive approach, steady progress, thoughtful questions, and clear focus on completing the project are qualities from which every student can benefit. His dedication to learning and problem-solving made a significant impact on the success of the project.\n\n## Tips for Mentees\n\nFrom a mentee's perspective having the following qualities would be helpful \n\n1. **Stick to the proposal:** While it's natural to feel the urge explore new ideas beyond the original proposal, it's essential to remain focused on the original proposal due to time constrains. \n\n2. **Adaptability and open-mindedness:** Be open to feedback and willing to adjust the tasks as you face challenges. \n\n3. **Time Management:** Many students juggle internships, interviews and other commitments during the summer. So it's to manage time effectively and discuss with the mentor about the progress during those times. \n\n4. **Effective communication:** Stay up to date with any updates from GSoC or from the mentor. Keeping your mentor updated about your progress or any challenges helps build a collaborative and supportive mentor relationship. \n\n## Tips for Mentors\n\nOn the other hand, Jacob demonstrated what it means to be an effective mentor. He showed me how to foster a supportive, collaborative relationship with the student. These are the lessons that I will carry forward in future mentorship roles:\n\nFrom a mentor's perspective having the following qualities would be helpful \n\n1. **Clear communication:** Communicating well in advance about the availability to meet or to review the work, having frequent meetings with the mentee would be helpful. \n\n2. **Encouragement:** While offering support, it's important to encourage the mentee to take ownership of the project. \n\n3. **Commitment and time:** Mentoring GSoC is a voluntary role, often taken on in addition to regular professional work. Balancing GSoC with other work commitments requires effective time management and commitment. \n\n4. **Structured Guidance:** Providing a well-organized plan, such as using task management tools like Trello and GitHub issues, ensures that the mentee can follow a clear path towards success completion of the project. \n\n# Conclusion\n\nGoogle Summer of Code offers an incredible opportunity for students to hone their programming skills while contributing to impactful open-source projects. It was a rewarding experience to be part of this journey as a co-mentor, and I am grateful to Jacob for giving me the chance to be involved in such a meaning project with the JuliaHealth community.\n\nThrough this experience, I not only gained insights into effective mentorship but also deepened my understanding of open-source collaboration and its potential to drive innovation in healthcare. I'm excited to explore further ways I can contribute to the JuliaHealth ecosystem and continue supporting the community.\n\n## Let's Keep in Touch!\n\nIf you would like to know more about me, you can connect with me on [Linkedin](https://www.linkedin.com/in/mounika-thakkallapally/).\n\n", + "supporting": [ + "index_files" + ], + "filters": [], + "includes": {} + } +} \ No newline at end of file diff --git a/_freeze/blog/posts/ryan-gsoc/Ryan_GSOC/execute-results/html.json b/_freeze/blog/posts/ryan-gsoc/Ryan_GSOC/execute-results/html.json new file mode 100644 index 0000000..5373921 --- /dev/null +++ b/_freeze/blog/posts/ryan-gsoc/Ryan_GSOC/execute-results/html.json @@ -0,0 +1,12 @@ +{ + "hash": "9b2677886ca73ecdd4ecc1f50f1c298e", + "result": { + "engine": "julia", + "markdown": "---\ntitle: \"GSoC '24: Enhancements to KomaMRI.jl GPU Support\"\ndescription: \"A summary of my project for Google Summer of Code\"\nauthor: \"Ryan Kierulf\"\ndate: \"8/30/2024\"\ntoc: true\nengine: julia\nimage: false\ncategories:\n - gsoc\n - mri\n - gpu\n - hpc\n - simulation\n---\n\n\n\n\n# Hi! πŸ‘‹\n\nI am Ryan, an MS student currently studying computer science at the University of Wisconsin-Madison. Looking for a project to work on this summer, my interest in high-performance computing and affinity for the Julia programming language drew me to Google Summer of Code, where I learned about this project opportunity to work on enhancing GPU support for KomaMRI.jl. \n\nIn this post, I'd like to summarize what I did this summer and everything I learned along the way!\n\n> If you want to learn more about me, you can connect with me here: [**LinkedIn**](https://www.linkedin.com/in/ryan-kierulf-022062201/), [**GitHub**](https://github.com/rkierulf)\n\n# What is KomaMRI?\n\n[KomaMRI](https://github.com/JuliaHealth/KomaMRI.jl) is a Julia package for efficiently simulating Magnetic Resonance Imaging (MRI) acquisitions. MRI simulation is a useful tool for researchers, as it allows testing new pulse sequences to analyze the signal output and image reconstruction quality without needing to actually take an MRI, which may be time or cost-prohibitive.\n\nIn contrast to many other MRI simulators, KomaMRI.jl is open-source, cross-platform, and comes with an intuitive user interface (To learn more about KomaMRI, you can read the paper introducing it [here](https://onlinelibrary.wiley.com/doi/full/10.1002/mrm.29635)). However, being developed fairly recently, there are still new features that can be added and optimization to be done.\n\n# Project Goals\n\nThe goals outlined by Carlos (my project mentor) and I the beginning of this summer were:\n\n1. Extend GPU support beyond CUDA to include AMD, Intel, and Apple Silicon GPUs, through the packages [AMDGPU.jl](https://github.com/JuliaGPU/AMDGPU.jl), [oneAPI.jl](https://github.com/JuliaGPU/oneAPI.jl), and [Metal.jl](https://github.com/JuliaGPU/Metal.jl)\n\n2. Create a CI pipeline to be able to test each of the GPU backends\n\n3. Create a new kernel-based simulation method optimized for the GPU, which we expected would outperform array broadcasting\n\n4. (Stretch Goal) Look into ways to support running distributed simulations across multiple nodes or GPUs\n\n\n# Step 1: Support for Different GPU backends\n\nPreviously, KomaMRI's support for GPU acceleration worked by converting each array used within the simulation to a `CuArray`, the device array type defined in [CUDA.jl](https://github.com/JuliaGPU/CUDA.jl). This was done through a general `gpu` function. The inner simulation code is GPU-agnostic, as the same operations can be performed on a CuArray or a plain CPU Array. This approach is good for extensibility, as it does not require writing different simulation code for the CPU / GPU, or different GPU backends, and would only work in a language like Julia based on runtime dispatch!\n\nTo extend this to multiple GPU backends, all that is needed is to generalize the `gpu` function to convert to either the device types of CUDA.jl, AMDGPU.jl, Metal.jl, or oneAPI.jl, depending on which backend is being used. To give an idea of what the gpu conversion code looked like before, here is a snippet:\n\n```julia\nstruct KomaCUDAAdaptor end\nadapt_storage(to::KomaCUDAAdaptor, x) = CUDA.cu(x)\n\nfunction gpu(x)\n check_use_cuda()\n return use_cuda[] ? fmap(x -> adapt(KomaCUDAAdaptor(), x), x; exclude=_isleaf) : x\nend\n\n#CPU adaptor\nstruct KomaCPUAdaptor end\nadapt_storage(to::KomaCPUAdaptor, x::AbstractArray) = adapt(Array, x)\nadapt_storage(to::KomaCPUAdaptor, x::AbstractRange) = x\n\ncpu(x) = fmap(x -> adapt(KomaCPUAdaptor(), x), x)\n```\n\nThe `fmap` function is from the package `Functors.jl` and can recursively apply a function to a struct tagged with `@functor`. The function being applied is `adapt` from `Adapt.jl`, which will call the lower-level `adapt_storage` function to actually convert to / from the device type. The second parameter to `adapt` is what is being adapted, and the first is what it is being adapted to, which in this case is a custom adapter struct `KomaCUDAAdapter`. \n\nOne possible approach to generalize to different backends would be to define additional adapter structs for each backend and corresponding `adapt_storage` functions. This is what the popular machine learning library [Flux.jl](https://github.com/FluxML/Flux.jl) does. However, there is a simpler way!\n\nEach backend package (CUDA.jl, Metal.jl, etc.) already defines `adapt_storage` functions for converting different types to / from corresponding device type. Reusing these functions is preferable to defining our own since, not only does it save work, but it allows us to rely on the expertise of the developers who wrote those packages! If there is an issue with types being converted incorrectly that is fixed in one of those packages, then we would not need to update our code to get this fix since we are using the definitions they created.\n\nOur final `gpu` and `cpu` functions are very simple. The `backend` parameter is a type derived from the abstract `Backend` type of [`KernelAbstractions.jl`](https://github.com/JuliaGPU/KernelAbstractions.jl), which is extended by each of the backend packages:\n\n```julia\nimport KernelAbstractions as KA\n\nfunction gpu(x, backend::KA.GPU)\n return fmap(x -> adapt(backend, x), x; exclude=_isleaf)\nend\n\ncpu(x) = fmap(x -> adapt(KA.CPU(), x), x, exclude=_isleaf)\n```\n\nThe other work needed to generalize our GPU support involved switching to use [package extensions](https://pkgdocs.julialang.org/v1/creating-packages/#Conditional-loading-of-code-in-packages-(Extensions)) to avoid having each of the backend packages as an explicit dependency, and defining some basic GPU functions for backend selection and printing information about available GPU devices. The pull request for adding support for multiple backends is linked below:\n\n> https://github.com/JuliaHealth/KomaMRI.jl/pull/405\n\n# Step 2: Buildkite CI\n\nAt the time the above pull request was merged, we weren't sure whether the added support for AMD and Intel GPUs actually worked, since we only had access to CUDA and Apple Silicon GPUs. So the next step was to set up a CI to test each GPU backend. To do this, we used [Buildkite](https://github.com/JuliaGPU/KernelAbstractions.jl), which is a CI platform that many other Julia packages also use. Since there were many examples to follow, setting up our testing pipeline was not too difficult. Each step of the pipeline does the required environment setup and then calls `Pkg.test()` for KomaMRICore. As an example, here is what the AMDGPU step of our pipeline looks like:\n\n\n\n\n```{yml}\n - label: \"AMDGPU: Run tests on v{{matrix.version}}\"\n matrix:\n setup:\n version:\n - \"1\"\n plugins:\n - JuliaCI/julia#v1:\n version: \"{{matrix.version}}\"\n - JuliaCI/julia-coverage#v1:\n codecov: true\n dirs:\n - KomaMRICore/src\n - KomaMRICore/ext\n command: |\n julia -e 'println(\"--- :julia: Instantiating project\")\n using Pkg\n Pkg.develop([\n PackageSpec(path=pwd(), subdir=\"KomaMRIBase\"),\n PackageSpec(path=pwd(), subdir=\"KomaMRICore\"),\n ])'\n \n julia --project=KomaMRICore/test -e 'println(\"--- :julia: Add AMDGPU to test environment\")\n using Pkg\n Pkg.add(\"AMDGPU\")'\n \n julia -e 'println(\"--- :julia: Running tests\")\n using Pkg\n Pkg.test(\"KomaMRICore\"; coverage=true, test_args=[\"AMDGPU\"])'\n agents:\n queue: \"juliagpu\"\n rocm: \"*\"\n timeout_in_minutes: 60\n```\n\n\n\n\nWe also decided that in addition to a testing CI, it would also be helpful to have a benchmarking CI to track performance changes resulting from each commit to the main branch of the repository. [Lux.jl](https://github.com/LuxDL/Lux.jl) had a very nice-looking benchmarking page, so I decided to look into their approach. They were using [github-action-benchmark](https://github.com/benchmark-action/github-action-benchmark), a popular benchmarking action that integrates with the Julia package [`BenchmarkTools.jl`](https://github.com/JuliaCI/BenchmarkTools.jl). github-action-benchmark does two very useful things:\n\n1. Collects benchmarking data into a json file and provides a default index.html to display this data. If put inside a relative path in the gh-pages branch of a repository, this results in a public benchmarking page which is automatically updated after each commit!\n\n2. Comments on a pull request with the benchmarking results compared with before the pull request. Example: https://github.com/JuliaHealth/KomaMRI.jl/pull/442#pullrequestreview-2213921334\n\nThe only issue was that since github-action-benchmark is a github action, it is meant to be run within github by one of the available github runners. While this works for CPU benchmarking, only Buildkite has the CI setup for each of the GPU backends we are using, and Lux.jl's benchmarks page only included CPU benchmarks, not GPU benchmarks (Note: we talked with Avik, the repository owner of Lux.jl, and Lux.jl has since adopted the approach outlined below to display GPU and CPU benchmarks together). I was not able to find any examples of other julia packages using github-action-benchmark for GPU benchmarking.\n\nFortunately, there is a tool someone developed to download results from Buildkite into a github action (https://github.com/EnricoMi/download-buildkite-artifact-action). This repository only had 1 star when I found it, but it does exactly what we needed: it identifies the corresponding Buildkite build for a commit, waits for it to finish, and then downloads the artifacts for the build into the github action it is being run from. With this, we were able to download the Buildkite benchmark results from a final aggregation step into our benchmarking action and upload to github-action-benchmark to publish to either the main data.js file for our benchmarking website, or pull request.\n\nOur final benchmarking page looks like this and is [publicly accessible](https://juliahealth.org/KomaMRI.jl/benchmarks/):\n\n![](./Benchmark_Page.png)\n\nOne neat thing about github-action-benchmark is that the default index.html is extensible, so even though by deault it only shows time, the information for memory usage and number of allocations is also collected into the json file, and can be displayed as well.\n\nA successful CI run on Buildkite Looks like [this](https://buildkite.com/julialang/komamri-dot-jl/builds/925):\n\n![](./CI_Run.png)\n\nThe pull requests for creating the CI testing and benchmarking pipeline, and changing the index.html for our benchmark page are listed below:\n\n1. https://github.com/JuliaHealth/KomaMRI.jl/pull/411\n2. https://github.com/JuliaHealth/KomaMRI.jl/pull/418\n3. https://github.com/JuliaHealth/KomaMRI.jl/pull/421\n\n# Step 3: Optimization\n\nWith support for multiple backends enabled, and a robust CI, the next step was to optimize our simulation code as much as possible. Our original idea was to create a new GPU-optimized simulation method, but before doing this we wanted to look more at the existing code and optimize for the CPU. \n\nThe simulation code is solving a differential equation (the [Bloch equations(https://en.wikipedia.org/wiki/Bloch_equations)]) over time. Most differential equation solvers step through time, updating the current state at each time step, but our previous simulation code, more optimized for the GPU, did a lot of computations across all time points in a simulation block, allocating a matrix of size `Nspins by NΞ”t` each time this was done. Although this is beneficial for the GPU, where there are millions of threads available on which to parallelize these computations, for the CPU it is more important to conserve memory, and the aforementioned approach of stepping through time is preferable.\n\nAfter seeing that this approach did help speed up simulation time on the CPU, but was not faster on the GPU (7x slower for Metal!) we decided to separate our simulation code for the GPU and CPU, dispatching based on the `KernelAbstractions.Backend` type depending on if it is `<:KernelAbstractions.CPU` or `<:KernelAbstractions.GPU`. \n\nOther things we were able to do to speed up CPU computation time:\n\n1. Preallocating each array used inside the core simulation code so it can be re-used from one simulation block to the next.\n\n2. [Skipping an expensive computation](https://github.com/JuliaHealth/KomaMRI.jl/blob/master/KomaMRICore/src/simulation/SimMethods/Bloch/BlochCPU.jl#L90) if the magnetization at that time point is not added to the final signal\n\n3. Ensuring that each statement is fully broadcasted. We were surprised to see the difference between the following examples:\n\n```julia\n#Fast\nBz = x .* seq.Gx' .+ y .* seq.Gy' .+ z .* seq.Gz' .+ p.Ξ”w ./ T(2Ο€ .* Ξ³)\n\n#Slow\nBz = x .* seq.Gx' .+ y .* seq.Gy' .+ z .* seq.Gz' .+ p.Ξ”w / T(2Ο€ * Ξ³)\n```\n\n4. Using the `cis` function for complex exponentiation, which is faster than `exp`\n\nWith these changes, the mean improvement in simulation time aggregating across each of our benchmarks for 1, 2, 4, and 8 CPU threads was ~4.28. For 1 thread, the average improvement in memory usage was 90x!\n\nThe next task was optimizing the simulation code for the GPU. Although our original idea was to put everything into one GPU kernel, we found that the existing broadcasting operations were already very fast, and that custom kernels we wrote were not able to outperform the previous implementation. The Julia GPU compiler team deserves a lot of credit for developing such fast broadcasting implementations!\n\nHowever, this does not mean that we were unable to improve the GPU simulation time. Similar to with the CPU, preallocation made a substantial difference. Parallelizing as much work as possible across the time points for a simulation block was also found to beneficial. For the parts that needed to be done sequentially, a [custom GPU kernel](https://github.com/JuliaHealth/KomaMRI.jl/blob/master/KomaMRICore/src/simulation/SimMethods/Bloch/KernelFunctions.jl#L5) was written which used the `KernelAbstractions.@localmem` macro for arrays being updated at each time step to yield faster memory access.\n\nThe mean speedup we saw across the 4 supported GPU backends was 4.16, although this varied accross each backend (for example, CUDA was only 2.66x faster while oneAPI was 28x faster). There is a [remaining bottleneck](https://github.com/JuliaHealth/KomaMRI.jl/blob/master/KomaMRICore/src/simulation/SimMethods/Bloch/BlochGPU.jl#L151) in the `run_spin_preceession!` function having to do with logical indexing that I was not able to resolve, but could be solved in the future to speed up the GPU simulation time even further!\n\nThe pull requests optimizing code for the CPU and GPU are below:\n\n1. https://github.com/JuliaHealth/KomaMRI.jl/pull/443\n\n2. https://github.com/JuliaHealth/KomaMRI.jl/pull/459\n\n3. https://github.com/JuliaHealth/KomaMRI.jl/pull/462\n\n# 4. Step 4: Distributed Support\n\nThis last step was a stretch goal for exploring how to add distributed support to KomaMRI. MRI simulations can become quite large, so it is useful to be able to distribute work across either multiple GPUs or multiple compute nodes.\n\nA nice thing about MRI simulation is the independent spin property: if a phantom object (representing, for example a brain tissue slice) is divided into two parts, and each part is simulated separately, the signal result from simulating the whole phantom will be equal to the sum of the signal results from simulating each subdivision of the original phantom. This makes it quite easy to distribute work, either across more than one GPU or accross multiple compute nodes.\n\nThe following scripts worked, with the only necessary code change to the repository being a new + function to add two RawAcquisitionData structs:\n\n```julia\n#Use multiple GPUs:\nusing Distributed\nusing CUDA\n\n#Add workers based on the number of available devices\naddprocs(length(devices()))\n\n#Define inputs on each worker process\n@everywhere begin\n using KomaMRI, CUDA\n sys = Scanner()\n seq = PulseDesigner.EPI_example()\n obj = brain_phantom2D()\n #Divide phantom\n parts = kfoldperm(length(obj), nworkers())\nend\n\n#Distribute simulation across workers\nraw = Distributed.@distributed (+) for i=1:nworkers()\n KomaMRICore.set_device!(i-1) #Sets device for this worker, note that CUDA devices are indexed from 0\n simulate(obj[parts[i]], seq, sys)\nend\n```\n\n```julia\n#Use multiple compute nodes\nusing Distributed\nusing ClusterManagers\n\n#Add workers based on the specified number of SLURM tasks\naddprocs(SlurmManager(parse(Int, ENV[\"SLURM_NTASKS\"])))\n\n#Define inputs on each worker process\n@everywhere begin\n using KomaMRI\n sys = Scanner()\n seq = PulseDesigner.EPI_example()\n obj = brain_phantom2D()\n parts = kfoldperm(length(obj), nworkers())\nend\n\n#Distribute simulation across workers\nraw = Distributed.@distributed (+) for i=1:nworkers()\n simulate(obj[parts[i]], seq, sys)\nend\n```\n\nPull reqeust for adding these examples to the KomaMRI documentation: https://github.com/JuliaHealth/KomaMRI.jl/pull/468\n\n# Conclusions / Future Work\n\nThis project was a 350-hour large project, since there were many goals to accomplish. To summarize what changed since the beginning of the project:\n\n1. Added support for AMDGPU.jl, Metal.jl, and oneAPI.jl GPU backends\n\n2. CI for automated testing and benchmarking accross each backend + [public benchmarks page](https://juliahealth.org/KomaMRI.jl/benchmarks/)\n\n3. Significantly faster CPU and GPU performance\n\n4. Demonstrated distributed support and examples added in documentation\n\nFuture work could look at ways to further optimize the simulation code, since despite the progress made, I believe there is more work to be done! The aforementioned logical indexing issue is still not resolved, and the kernel used inside the `run_spin_excitation!` function has not been profiled in depth. KomaMRI is also looking into adding support for higher-order ODE methods, which could require more GPU kernels being written.\n\n# Acknowledgements\n\nI would like to thank my mentor, Carlos Castillo, for his help and support on this project. I would also like to thank Jakub Mitura, who attended some of our meetings to help with GPU optimization, Dilum Aluthge who helped set up our BuildKite pipeline, and Tim Besard, who answered many GPU-related questions that Carlos and I had.\n\n", + "supporting": [ + "Ryan_GSOC_files" + ], + "filters": [], + "includes": {} + } +} \ No newline at end of file diff --git a/_freeze/site_libs/clipboard/clipboard.min.js b/_freeze/site_libs/clipboard/clipboard.min.js new file mode 100644 index 0000000..1103f81 --- /dev/null +++ b/_freeze/site_libs/clipboard/clipboard.min.js @@ -0,0 +1,7 @@ +/*! + * clipboard.js v2.0.11 + * https://clipboardjs.com/ + * + * Licensed MIT Β© Zeno Rocha + */ +!function(t,e){"object"==typeof exports&&"object"==typeof module?module.exports=e():"function"==typeof define&&define.amd?define([],e):"object"==typeof exports?exports.ClipboardJS=e():t.ClipboardJS=e()}(this,function(){return n={686:function(t,e,n){"use strict";n.d(e,{default:function(){return b}});var e=n(279),i=n.n(e),e=n(370),u=n.n(e),e=n(817),r=n.n(e);function c(t){try{return document.execCommand(t)}catch(t){return}}var a=function(t){t=r()(t);return c("cut"),t};function o(t,e){var n,o,t=(n=t,o="rtl"===document.documentElement.getAttribute("dir"),(t=document.createElement("textarea")).style.fontSize="12pt",t.style.border="0",t.style.padding="0",t.style.margin="0",t.style.position="absolute",t.style[o?"right":"left"]="-9999px",o=window.pageYOffset||document.documentElement.scrollTop,t.style.top="".concat(o,"px"),t.setAttribute("readonly",""),t.value=n,t);return e.container.appendChild(t),e=r()(t),c("copy"),t.remove(),e}var f=function(t){var e=10?setTimeout((function(){e(r,n,s)}),1):(t.update(),n(s))}}},"./src/filter.js":function(t){t.exports=function(t){return t.handlers.filterStart=t.handlers.filterStart||[],t.handlers.filterComplete=t.handlers.filterComplete||[],function(e){if(t.trigger("filterStart"),t.i=1,t.reset.filter(),void 0===e)t.filtered=!1;else{t.filtered=!0;for(var r=t.items,n=0,s=r.length;nv.page,a=new g(t[s],void 0,n),v.items.push(a),r.push(a)}return v.update(),r}m(t.slice(0),e)}},this.show=function(t,e){return this.i=t,this.page=e,v.update(),v},this.remove=function(t,e,r){for(var n=0,s=0,i=v.items.length;s-1&&r.splice(n,1),v},this.trigger=function(t){for(var e=v.handlers[t].length;e--;)v.handlers[t][e](v);return v},this.reset={filter:function(){for(var t=v.items,e=t.length;e--;)t[e].filtered=!1;return v},search:function(){for(var t=v.items,e=t.length;e--;)t[e].found=!1;return v}},this.update=function(){var t=v.items,e=t.length;v.visibleItems=[],v.matchingItems=[],v.templater.clear();for(var r=0;r=v.i&&v.visibleItems.lengthe},innerWindow:function(t,e,r){return t>=e-r&&t<=e+r},dotted:function(t,e,r,n,s,i,a){return this.dottedLeft(t,e,r,n,s,i)||this.dottedRight(t,e,r,n,s,i,a)},dottedLeft:function(t,e,r,n,s,i){return e==r+1&&!this.innerWindow(e,s,i)&&!this.right(e,n)},dottedRight:function(t,e,r,n,s,i,a){return!t.items[a-1].values().dotted&&(e==n&&!this.innerWindow(e,s,i)&&!this.right(e,n))}};return function(e){var n=new i(t.listContainer.id,{listClass:e.paginationClass||"pagination",item:e.item||"
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listingEl.querySelectorAll( + ".pagination li.page-item:not(.disabled) .page.page-link" + ); + for (const paginationEl of paginationEls) { + paginationEl.onclick = (sender) => { + setPageHash(listingId, sender.target.getAttribute("data-i")); + showPage(listingId, sender.target.getAttribute("data-i")); + return false; + }; + } +} + +function renderVisibleProgressiveImages(list) { + // Run through the visible items and render any progressive images + for (const item of list.visibleItems) { + const itemEl = item.elm; + if (itemEl) { + const progressiveImgs = itemEl.querySelectorAll( + `img[${kProgressiveAttr}]` + ); + for (const progressiveImg of progressiveImgs) { + const srcValue = progressiveImg.getAttribute(kProgressiveAttr); + if (srcValue) { + progressiveImg.setAttribute("src", srcValue); + } + progressiveImg.removeAttribute(kProgressiveAttr); + } + } + } +} + +function getHash() { + // Hashes are of the form + // #name:value|name1:value1|name2:value2 + const currentUrl = new URL(window.location); + const hashRaw = currentUrl.hash ? currentUrl.hash.slice(1) : undefined; + return parseHash(hashRaw); +} + +const kAnd = "&"; +const kEquals = "="; + +function parseHash(hash) { + if (!hash) { + return undefined; + } + const hasValuesStrs = hash.split(kAnd); + const hashValues = hasValuesStrs + .map((hashValueStr) => { + const vals = hashValueStr.split(kEquals); + if (vals.length === 2) { + return { name: vals[0], value: vals[1] }; + } else { + return undefined; + } + }) + .filter((value) => { + return value !== undefined; + }); + + const hashObj = {}; + hashValues.forEach((hashValue) => { + hashObj[hashValue.name] = decodeURIComponent(hashValue.value); + }); + return hashObj; +} + +function makeHash(obj) { + return Object.keys(obj) + .map((key) => { + return `${key}${kEquals}${obj[key]}`; + }) + .join(kAnd); +} + +function setHash(obj) { + const hash = makeHash(obj); + window.history.pushState(null, null, `#${hash}`); +} + +function showPage(listingId, page) { + const list = window["quarto-listings"][listingId]; + if (list) { + list.show((page - 1) * list.page + 1, list.page); + } +} + +function activateCategory(category) { + // Deactivate existing categories + const activeEls = window.document.querySelectorAll( + ".quarto-listing-category .category.active" + ); + for (const activeEl of activeEls) { + activeEl.classList.remove("active"); + } + + // Activate this category + const categoryEl = window.document.querySelector( + `.quarto-listing-category .category[data-category='${btoa( + encodeURIComponent(category) + )}']` + ); + if (categoryEl) { + categoryEl.classList.add("active"); + } + + // Filter the listings to this category + filterListingCategory(category); +} + +function filterListingCategory(category) { + const listingIds = Object.keys(window["quarto-listings"]); + for (const listingId of listingIds) { + const list = window["quarto-listings"][listingId]; + if (list) { + if (category === "") { + // resets the filter + list.filter(); + } else { + // filter to this category + list.filter(function (item) { + const itemValues = item.values(); + if (itemValues.categories !== null) { + const categories = decodeURIComponent( + atob(itemValues.categories) + ).split(","); + return categories.includes(category); + } else { + return false; + } + }); + } + } + } +} diff --git a/_quarto.yml b/_quarto.yml index 9c15758..147cd5d 100644 --- a/_quarto.yml +++ b/_quarto.yml @@ -34,7 +34,5 @@ format: light: - cosmo - brand - dark: - - darkly css: styles.css toc: true diff --git a/assets/logo.png b/assets/images/logo.png similarity index 100% rename from assets/logo.png rename to assets/images/logo.png diff --git a/assets/json/packages.json b/assets/json/packages.json new file mode 100644 index 0000000..0967ef4 --- /dev/null +++ b/assets/json/packages.json @@ -0,0 +1 @@ +{} diff --git a/assets/json/related_orgs.json b/assets/json/related_orgs.json new file mode 100644 index 0000000..0967ef4 --- /dev/null +++ b/assets/json/related_orgs.json @@ -0,0 +1 @@ +{} diff --git a/blog/_metadata.yml b/blog/_metadata.yml index a737943..7f667ec 100644 --- a/blog/_metadata.yml +++ b/blog/_metadata.yml @@ -1,6 +1,28 @@ +# options specified here will apply to all posts in this folder website: sidebar: - title: "Blogs" style: "docked" background: light +# freeze computational output +# (see https://quarto.org/docs/projects/code-execution.html#freeze) +execute: + freeze: auto + eval: true + echo: true + output: true + +comments: + utterances: + repo: JuliaHealth/JuliaHealthBlog + theme: github-dark + issue-term: title + label: Comment + +# bibliography: https://raw.githubusercontent.com/pat-alt/bib/main/bib.bib +citation: true + +crossref: + fig-prefix: Figure + tbl-prefix: Table diff --git a/blog/ieee-with-url.csl b/blog/ieee-with-url.csl new file mode 100644 index 0000000..eae93a5 --- /dev/null +++ b/blog/ieee-with-url.csl @@ -0,0 +1,434 @@ + + diff --git a/blog/posts/JZubik-gsoc/Augmentations.png 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    Description of the emojis used in the title +
      +
    • πŸ“ **Action Plan**: A clear, structured plan that guides each step of the MedPipe3D pipeline.
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    • 🩻 **3D Medical Images**: Medical imaging data, such as MRI scans in Nifti format.
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    • πŸ“Ž **AI Model**: The initial AI model that will be trained and refined within the pipeline.
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    • πŸ“‰ **Loss Function**: A function that measures the model’s performance during training, guiding the optimization process.
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    • πŸ—ƒοΈ **Data Loading**: Preparation and loading of data and metadata into HDF5 format.
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    • πŸ“š **Data Splitting**: Dividing data into training, validation, and test sets.
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    • ♻️ **Data Augmentation**: Increasing data variability through augmentation.
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    • πŸ§‘β€πŸ« **AI Training**: Using Lux.jl framework to train the AI model.
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    • πŸ€– **Model**: The trained AI model that can perform tasks like segmentation on medical images.
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    • πŸ‘οΈ **Data for Visualization**: Output data, such as masks and segmentations.
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    • πŸ“ˆ **Performance Logs**: Logs and metrics documenting the AI's performance.
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    • β€οΈβ€πŸ©Ή **Purpose of MedPipe3D**
      • +
    + + + +
    +In this post, I'd like to summarize what I did this summer and everything I learned along the way, rebuilding the MedPipe3D medical imaging pipeline. I will not start typically, but so that anyone even a novice can visualize what this project has achieved, while the latter part is intended for more experienced readers. It will be easiest to divide it into 4 steps separated by ➑️ in the title above. Each emoji stands for a different piece of pipeliner and will be described below. + +πŸ“πŸ©»πŸ“ŽπŸ“‰ **What we need from the user** + +MedPipe3D requires four essential inputs from the user to get started: a clear action plan πŸ“, 3D medical images like MRI scans 🩻, an AI model πŸ“Ž, and a loss function πŸ“‰. + +πŸ—ƒοΈπŸ“šβ™»οΈπŸ§‘β€πŸ« **The Pipeline essential AI manufacturing line** + +Following the plan πŸ“, MedPipe3D loads data, pre-processes, and organizes it πŸ—ƒοΈ. Allowing data to be easily split πŸ“š, and efficiently augmented ♻️ in many ways for learning AI πŸ§‘β€πŸ« model effectively. In the end, performing testing and post-processing for better determination of AI skills. +It's designed to transform raw medical data into a format that your AI can learn from, segmenting meaningful patterns and structures. + +πŸ€–πŸ‘οΈπŸ“ˆ **Results and Insights** + +MedPipe3D is a tool for researchers and for that, it cannot do without analysis, testing, and evaluation. The result of the pipeline is a model πŸ€– as well as data πŸ‘οΈ and logs πŸ“ˆ needed in MedEval3D that are ready for visualization and further analysis with MedEye3D. In a nutshell, it makes visualizing results easy, tumor locations or other medical features directly as masks on the scans. + +β€οΈβ€πŸ©Ή **Purpose-Driven Technology** + +MedPipe3D's mission goes beyond technology. It's about providing the tools to create AIs that support healthcare professionals in making faster, more accurate decisions, with the ultimate goal of saving lives. + +This four-part journey captures the heart of the MedPipe3D toolkit for advancing medical AI, from raw data to life-saving insight. + +## Introduction + +**MedPipe3D** is a framework created from hundreds of hours over summer vacation, thousands of lines of code, hundreds of mistakes, and most importantly the guidance of my mentor and author of all of these libraries Dr. [Jakub Mitura](https://www.linkedin.com/in/jakub-mitura-7b2013151/). +At its core, MedPipe3D combines sophisticated data handling from **MedImage** thanks to the hard work of [Divyansh Goyal](https://www.linkedin.com/in/divyansh-goyal-34654b200/). Newly developed pipeline for model training, validation, and testing with existing **MedEval3D**, and result visualization with **MedEye3D**. +Unfortunately, not all of the project's goals have been fully achieved, and thereby there is one section ➑️ too many. Hopefully not for long. My name is [Jan Zubik](https://www.linkedin.com/in/janzubik/), and I wrote this entire library from scratch, which is currently my most complex project. + +If you are a data scientist, programmer, or code enthusiast, I invite you to read the next section where I go into detail and present **version 1** of this tool in detail. + +I'm a 3rd-year student of BSc in Data Science and Machine Learning, I know that many things can be done better, expanded, debugged, and optimized. Now it just works, **but don't hesitate to write to me personally** on [LinkedIn](https://www.linkedin.com/in/janzubik/), [Julia's Slack](https://julialang.slack.com/team/U06L685B6TD) or [GitHub](https://github.com/JanZubik)! +With your comments, and direct critique **you will help me** to be a better programmer and one day MedPipe3D will contribute in a tiny way to save someone's life! + +Exact work from the Google Summer of Code project you will find in [GitHub the repository.](https://github.com/JuliaHealth/MedPipe3D.jl/tree/GSoC-'24-MedPipe3D) + + +# Project Goals + +The primary goal was to develop MedPipe3D and enhance MedImage, a Julia package designed to streamline the process of GPU-accelerated medical image segmentation. The project aimed to merge existing librariesβ€”MedEye3D, MedEval3D, and MedImageβ€”into a cohesive pipeline that facilitates advanced data handling, preprocessing, augmentation, model training, validation, testing with post-processing and visualization for medical imaging applications. + + + +# Tasks + +- πŸ†™ - Fully finished, with great potential for further development +- βœ… - Fully completed +- ⚠️ - Partially uncompleted +- ❌ - Unreached + +Full list of all major parts and minor tasks (all tasks set up in the original GSOC plan were completed at least minimum level, and many additional improvements above minimum were implemented) +
    + +1. **Helpful functions to support the MedImage format βœ…** + - Debugging rotations βœ… + - Crop MedImage or 3D array βœ… + - Pad MedImage or 3D array βœ… + - Pad with edge values βœ… + - Calculating the average of the edges of the picture πŸ†™ + +2. **Integrate Augmentations for Medical Data βœ…** + - Brightness transform βœ… + - Contrast augmentation transform βœ… + - Gamma Transform βœ… + - Gaussian noise transform βœ… + - Rician noise transform βœ… + - Mirror transform βœ… + - Scale transform πŸ†™ + - Gaussian blur transform βœ… + - Simulate low-resolution transform πŸ†™ + - Elastic deformation transform πŸ†™ + +3. **Develop a Pipeline ⚠️** + - Structured configuration of all hyperparameters πŸ†™ + - Interactive creation of configuration βœ… + - Creating a structured configuration of hyperparameters in JSON πŸ†™ + - Loading data into HDF5 βœ… + - Cropping and padding to real coordinates of the main picture βœ… + - Calculate Median and Mean Spacing with resampling πŸ†™ + - Cropping and padding to specific or average dimensions βœ… + - Standardization and normalization βœ… + - Managing index groups (channels) for batch requirements in HDF5 βœ… + - Divide into train, validation, test specified as % βœ… + - Divide with a specific division specified in JSON βœ… + - Equal distribution when there are multiple classes βœ… + - Extracting data and creating 5-dimensional tensors for batched learning βœ… + - Hole images data loading βœ… + - Patch-based data loading with probabilistic oversampling βœ… + - Obtaining the necessary elements for learning βœ… + - Get optimizer, loss function, and performance metrics βœ… + - Apply augmentations βœ… + - Train βœ… + - Initializing model βœ… + - The learning epoch βœ… + - Epoch with early stopping functionality βœ… + - Inferring βœ… + - Validation βœ… + - Evaluate metric βœ… + - Evaluate validation loss βœ… + - Validation with largest connected componentβœ… + - Testing βœ… + - Evaluate test set βœ… + - Invertible augmentations evaluation βœ… + - Patch-based invertible augmentations evaluation βœ… + - Logging ⚠️ + - Returning the necessary results ⚠️ + - Logging connection to TensorBoard ❌ + - Logging errors and warnings ❌ + - Visualization ⚠️ + - Returning data in Nifti format βœ… + - Automated visualization in MedEye3D ❌ + +4. **Optimize Performance with GPU Acceleration** + - Augmentations βœ… + - Learning, Validation, Testing βœ… + - Largest connected component βœ… + +5. **Documentation ⚠️** + - Comments in important places in the code ⚠️ + - Documentation of the function ⚠️ + - Read me ⚠️ + - Documentation on juliahealth.org ❌ + +
    + +## Integrate augmentations for medical data πŸ†™ +Augmenting medical data is a crucial step for enhancing model robustness, especially given the variations in imaging conditions and patient anatomy. + +- This pipeline currently supports multiple augmentation techniques: + - Brightness transform βœ… + - Contrast augmentation transform βœ… + - Gamma Transform βœ… + - Gaussian noise transform βœ… + - Rician noise transform βœ… + - Mirror transform βœ… + - Scale transform πŸ†™ + - Gaussian blur transform βœ… + - Simulate low-resolution transform πŸ†™ + - Elastic deformation transform πŸ†™ + +Which have been fully integrated. Each of these methods helps the model generalize better by simulating diverse imaging scenarios. + +![](./Augmentations.png) + +Comments: + +Augmentations such as scaling, and low-resolution simulation use interpolation that is not yet GPU-accelerated. + +Elastic deformation with simulation of different tissue elasticities is a potential development opportunity that would further improve the model's adaptability by mimicking more complex variations found in medical imaging. + +## Invertible augmentations and support test time augmentations πŸ†™ +This section focuses on the ability to apply reversible augmentations to test data, allowing the model to be evaluated with different transformations. Only rotation is available at this time. The function `evaluate_patches` performs this evaluation by applying specified augmentations, dividing the test data into patches, and reconstructing the full image from the patches. During testing, one can choose to use of largest connected component post-processing. Metrics are calculated and results are saved for analysis. + +
    +evaluate_test: + +```julia +# ... +for test_group in test_groups + test_data, test_label, attributes = fetch_and_preprocess_data([test_group], h5, config) + results, test_metrics = evaluate_patches(test_data, test_label, tstate, model, config) + y_pred, metr = process_results(results, test_metrics, config) + save_results(y_pred, attributes, config) + push!(all_test_metrics, metr) +end +# ... +``` + +```julia +function evaluate_patches(test_data, test_label, tstate, model, config, axis, angle) + println("Evaluating patches...") + results = [] + test_metrics = [] + tstates = [tstate] + test_time_augs = [] + + for i in config["learning"]["n_invertible"] + data = rotate_mi(test_data, axis, angle) + for tstate_curr in tstates + patch_results = [] + patch_size = Tuple(config["learning"]["patch_size"]) + idx_and_patches, paded_data_size = divide_into_patches(test_data, patch_size) + coordinates = [patch[1] for patch in idx_and_patches] + patch_data = [patch[2] for patch in idx_and_patches] + for patch in patch_data + y_pred_patch, _ = infer_model(tstate_curr, model, patch) + push!(patch_results, y_pred_patch) + end + idx_and_y_pred_patch = zip(coordinates, patch_results) + y_pred = recreate_image_from_patches(idx_and_y_pred_patch, paded_data_size, patch_size, size(test_data)) + if config["learning"]["largest_connected_component"] + y_pred = largest_connected_component(y_pred, config["learning"]["n_lcc"]) + end + metr = evaluate_metric(y_pred, test_label, config["learning"]["metric"]) + push!(test_metrics, metr) + end + end + return results, test_metrics +end +``` + +```julia +function divide_into_patches(image::AbstractArray{T, 5}, patch_size::Tuple{Int, Int, Int}) where T + println("Dividing image into patches...") + println("Size of the image: ", size(image)) + + # Calculate the required padding for each dimension (W, H, D) + pad_size = ( + (size(image, 1) % patch_size[1]) != 0 ? patch_size[1] - size(image, 1) % patch_size[1] : 0, + (size(image, 2) % patch_size[2]) != 0 ? patch_size[2] - size(image, 2) % patch_size[2] : 0, + (size(image, 3) % patch_size[3]) != 0 ? patch_size[3] - size(image, 3) % patch_size[3] : 0 + ) + + # Pad the image if necessary + padded_image = image + if any(pad_size .> 0) + padded_image = crop_or_pad(image, (size(image, 1) + pad_size[1], size(image, 2) + pad_size[2], size(image, 3) + pad_size[3])) + end + + # Extract patches + patches = [] + for x in 1:patch_size[1]:size(padded_image, 1) + for y in 1:patch_size[2]:size(padded_image, 2) + for z in 1:patch_size[3]:size(padded_image, 3) + patch = view( + padded_image, + x:min(x+patch_size[1]-1, size(padded_image, 1)), + y:min(y+patch_size[2]-1, size(padded_image, 2)), + z:min(z+patch_size[3]-1, size(padded_image, 3)), + :, + : + ) + push!(patches, [(x, y, z), patch]) + end + end + end + println("Size of padded image: ", size(padded_image)) + return patches, size(padded_image) +end + +function recreate_image_from_patches( + coords_with_patches, + padded_size, + patch_size, + original_size +) + println("Recreating image from patches...") + reconstructed_image = zeros(Float32, padded_size...) + + # Place patches back into their original positions + for (coords, patch) in coords_with_patches + x, y, z = coords + reconstructed_image[ + x:x+patch_size[1]-1, + y:y+patch_size[2]-1, + z:z+patch_size[3]-1, + :, + : + ] = patch + end + + # Crop the reconstructed image to remove any padding + final_image = reconstructed_image[ + 1:original_size[1], + 1:original_size[2], + 1:original_size[3], + :, + : + ] + println("Size of the final image: ", size(final_image)) + return final_image +end +``` +
    + +Comment:
    +In this section, there is significant potential to incorporate additional types of invertible augmentations. + +## Patch-based data loading with probabilistic oversampling βœ… +In this section, patches are extracted using `extract_patch` from the medical images for model training, with a probability-based method to decide between a random patch or a patch with non-zero labels. +Helper functions like `get_random_patch` and `get_centered_patch` determine the starting indices and dimensions for the patches based on given configurations, while padding methods ensure consistency even if the patch exceeds the original image dimensions. Probabilistic oversampling, as configured, allows for more balanced and informative data sampling, which improves the model's ability to detect specific medical features. + + +
    +extract_patch: + +```julia +function extract_patch(image, label, patch_size, config) + # Fetch the oversampling probability from the config + println("Extracting patch.") + oversampling_probability = config["learning"]["oversampling_probability"] + # Generate a random number to decide which patch extraction method to use + random_choice = rand() + + if random_choice <= oversampling_probability + return extract_nonzero_patch(image, label, patch_size) + else + + return get_random_patch(image, label, patch_size) + end +end +#Helper function, in case the mask is emptyClick to apply +function extract_nonzero_patch(image, label, patch_size) + println("Extracting a patch centered around a non-zero label value.") + indices = findall(x -> x != 0, label) + if isempty(indices) + # Fallback to random patch if no non-zero points are found + return get_random_patch(image, label, patch_size) + else + # Choose a random non-zero index to center the patch around + center = indices[rand(1:length(indices))] + return get_centered_patch(image, label, center, patch_size) + end +end +# Function to get a patch centered around a specific index +function get_centered_patch(image, label, center, patch_size) + center_coords = Tuple(center) + half_patch = patch_size .Γ· 2 + start_indices = center_coords .- half_patch + end_indices = start_indices .+ patch_size .- 1 + + # Calculate padding needed + pad_beg = ( + max(1 - start_indices[1], 0), + max(1 - start_indices[2], 0), + max(1 - start_indices[3], 0) + ) + pad_end = ( + max(end_indices[1] - size(image, 1), 0), + max(end_indices[2] - size(image, 2), 0), + max(end_indices[3] - size(image, 3), 0) + ) + + # Adjust start_indices and end_indices after padding + start_indices_adj = start_indices .+ pad_beg + end_indices_adj = end_indices .+ pad_beg + + # Convert padding values to integers + pad_beg = Tuple(round.(Int, pad_beg)) + pad_end = Tuple(round.(Int, pad_end)) + + # Pad the image and label using pad_mi + image_padded = pad_mi(image, pad_beg, pad_end, 0) + label_padded = pad_mi(label, pad_beg, pad_end, 0) + + # Extract the patch + image_patch = image_padded[ + start_indices_adj[1]:end_indices_adj[1], + start_indices_adj[2]:end_indices_adj[2], + start_indices_adj[3]:end_indices_adj[3] + ] + label_patch = label_padded[ + start_indices_adj[1]:end_indices_adj[1], + start_indices_adj[2]:end_indices_adj[2], + start_indices_adj[3]:end_indices_adj[3] + ] + + return image_patch, label_patch +end + +function get_random_patch(image, label, patch_size) + println("Extracting a random patch.") + # Check if the patch size is greater than the image dimensions + if any(patch_size .> size(image)) + # Calculate the needed size to fit the patch + needed_size = map(max, size(image), patch_size) + # Use crop_or_pad to ensure the image and label are at least as large as needed_size + image = crop_or_pad(image, needed_size) + label = crop_or_pad(label, needed_size) + end + + # Calculate random start indices within the new allowable range + start_x = rand(1:size(image, 1) - patch_size[1] + 1) + start_y = rand(1:size(image, 2) - patch_size[2] + 1) + start_z = rand(1:size(image, 3) - patch_size[3] + 1) + start_indices = [start_x, start_y, start_z] + end_indices = start_indices .+ patch_size .- 1 + + # Extract the patch directly when within bounds + image_patch = image[start_indices[1]:end_indices[1], start_indices[2]:end_indices[2], start_indices[3]:end_indices[3]] + label_patch = label[start_indices[1]:end_indices[1], start_indices[2]:end_indices[2], start_indices[3]:end_indices[3]] + + return image_patch, label_patch +end + +``` +
    + +## Calculate Median and Mean Spacing with resampling πŸ†™ +This part ensures that all images in the dataset have consistent real coordinates, spacing, and shape. It's a critical factor in medical imaging for accurate analysis. Calculating and applying set values, median or mean across images ensures uniformity. + +#### Resample images to target image πŸ†™ +This step aligns each image to the reference coordinates of the main image, ensuring that all images share a common spatial alignment. The `resample_to_image` function from MedImage.jl is used here, applying interpolation to adjust each image. + + +
    +resample_images_to_target: + +```julia +if resample_images_to_target && !isempty(Med_images) + println("Resampling $channel_type files in channel '$channel_folder' to the first $channel_type in the channel.") + reference_image = Med_images[1] + Med_images = [resample_to_image(reference_image, img, interpolator) for img in Med_images] +end +``` +
    + +Comment:
    +`Resample_to_image` uses interpolation that is not yet GPU-accelerated in this implementation, this step slows down the data preparation phase significantly. + +#### Ensure uniform spacing across the entire dataset πŸ†™ +This step brings all images to a consistent voxel spacing across the dataset using `resample_to_spacing` from MedImage.jl. This uniform spacing is crucial for creating a standardized dataset where each image voxel represents the same physical volume. + + +
    +esample_to_spacing: + +```julia +if resample_images_spacing == "set" + println("Resampling all $channel_type files to target spacing: $target_spacing") + target_spacing = Tuple(Float32(s) for s in target_spacing) + channels_data = [[resample_to_spacing(img, target_spacing, interpolator) for img in channel] for channel in channels_data] +elseif resample_images_spacing == "avg" + println("Calculating average spacing across all $channel_type files and resampling.") + all_spacings = [img.spacing for channel in channels_data for img in channel] + avg_spacing = Tuple(Float32(mean(s)) for s in zip(all_spacings...)) + println("Average spacing calculated: $avg_spacing") + channels_data = [[resample_to_spacing(img, avg_spacing, interpolator) for img in channel] for channel in channels_data] +elseif resample_images_spacing == "median" + println("Calculating median spacing across all $channel_type files and resampling.") + all_spacings = [img.spacing for channel in channels_data for img in channel] + median_spacing = Tuple(Float32(median(s)) for s in all_spacings) + println("Median spacing calculated: $median_spacing") + channels_data = [[resample_to_spacing(img, median_spacing, interpolator) for img in channel] for channel in channels_data] +elseif resample_images_spacing == false + println("Skipping resampling of $channel_type files.") + # No resampling will be applied, channels_data remains unchanged. +end +``` +
    + +Comment:
    +`Resample_to_spacing` uses interpolation that is not yet GPU-accelerated in this implementation, this step slows down the data preparation phase significantly. + +#### Resizing all channel files to average or target size βœ… +To create a cohesive 5D tensor, all images in each channel are resized to a uniform shape, either the average size of all images or a specific target size. This resizing process uses `crop_or_pad`, ensuring that all images match the specified dimensions, making them suitable for model input. + +
    +crop_or_pad: + +```julia +if resample_size == "avg" + sizes = [size(img.voxel_data) for img in channels_data for img in img] # Get sizes from all images + avg_dim = map(mean, zip(sizes...)) + avg_dim = Tuple(Int(round(d)) for d in avg_dim) + println("Resizing all $channel_type files to average dimension: $avg_dim") + channels_data = [[crop_or_pad(img, avg_dim) for img in channel] for channel in channels_data] +elseif resample_size != "avg" + target_dim = Tuple(resample_size) + println("Resizing all $channel_type files to target dimension: $target_dim") + channels_data = [[crop_or_pad(img, target_dim) for img in channel] for channel in channels_data] +end +``` +
    + +## Basic Post-processing operations +Post-processing operations involve the algorithm `largest_connected_components`. It is achieved by label initialization and propagation in the segmented mask. +The `initialize_labels_kernel` function assigns unique labels to different regions. + +
    +initialize_labels_kernel: + +```julia +@kernel function initialize_labels_kernel(mask, labels, width, height, depth) + idx = @index(Global, Cartesian) + i = idx[1] + j = idx[2] + k = idx[3] + + if i >= 1 && i <= width && j >= 1 && j <= height && k >= 1 && k <= depth + if mask[i, j, k] == 1 + labels[i, j, k] = i + (j - 1) * width + (k - 1) * width * height + else + labels[i, j, k] = 0 + end + end +end +``` +
    +Propagate_labels_kernel iteratively updates the labels to maintain connected regions. +propagate_labels_kernel: +
    + +```julia +@kernel function propagate_labels_kernel(mask, labels, width, height, depth) + idx= @index(Global, Cartesian) + i = idx[1] + j = idx[2] + k = idx[3] + + if i >= 1 && i <= width && j >= 1 && j <= height && k >= 1 && k <= depth + if mask[i, j, k] == 1 + current_label = labels[i, j, k] + for di in -1:1 + for dj in -1:1 + for dk in -1:1 + if di == 0 && dj == 0 && dk == 0 + continue + end + ni = i + di + nj = j + dj + nk = k + dk + if ni >= 1 && ni <= width && nj >= 1 && nj <= height && nk >= 1 && nk <= depth + if mask[ni, nj, nk] == 1 && labels[ni, nj, nk] < current_label + labels[i, j, k] = labels[ni, nj, nk] + end + end + end + end + end + end + end +end +``` +
    +This process facilitates the identification of the largest connected components in 3D space, helping to isolate relevant medical structures, such as tumors, in the segmented mask. Allowing determining how many such areas are to be returned. + +
    +largest_connected_components: + +```julia +function largest_connected_components(mask::Array{Int32, 3}, n_lcc::Int) + width, height, depth = size(mask) + mask_gpu = CuArray(mask) + labels_gpu = CUDA.fill(0, size(mask)) + dev = get_backend(labels_gpu) + ndrange = (width, height, depth) + workgroupsize = (3, 3, 3) + + # Initialize labels + initialize_labels_kernel(dev)(mask_gpu, labels_gpu, width, height, depth, ndrange = ndrange) + CUDA.synchronize() + + # Propagate labels iteratively + for _ in 1:10 + propagate_labels_kernel(dev, workgroupsize)(mask_gpu, labels_gpu, width, height, depth, ndrange = ndrange) + CUDA.synchronize() + end + + # Download labels back to CPU + labels_cpu = Array(labels_gpu) + + # Find all unique labels and their sizes + unique_labels = unique(labels_cpu) + label_sizes = [(label, count(labels_cpu .== label)) for label in unique_labels if label != 0] + + # Sort labels by size and get the top n_lcc + sort!(label_sizes, by = x -> x[2], rev = true) + top_labels = label_sizes[1:min(n_lcc, length(label_sizes))] + + # Create a mask for each of the top n_lcc components + components = [labels_cpu .== label[1] for label in top_labels] + return components +end +``` +
    + +## Structured configuration of all hyperparameters πŸ†™ + +Hyperparameters for the entire pipeline are stored in a JSON configuration file, enabling straightforward adjustments for experimentation (just swap values, save and resume the study). This structured setup allows easy modification of key parameters, such as data set preparation, training settings, data augmentation, and resampling options. + + +
    +Example configuration: + +```JSON +{ + "model": { + "patience": 10, + "early_stopping_metric": "val_loss", + "optimizer_name": "Adam", + "loss_function_name": "l1", + "early_stopping": true, + "early_stopping_min_delta": 0.01, + "optimizer_args": "lr=0.001", + "num_epochs": 10 + }, + "data": { + "batch_complete": false, + "resample_size": [200,101,49], + "resample_to_target": false, + "resample_to_spacing": false, + "batch_size": 3, + "standardization": false, + "target_spacing": null, + "channel_size": 1, + "normalization": false, + "has_mask": true + }, + "augmentation": { + "augmentations": { + "Brightness transform": { + "mode": "additive", + "value": 0.2 + } + }, + "p_rand": 0.5, + "processing_unit": "GPU", + "order": [ + "Brightness transform" + ] + }, + "learning": { + "Train_Val_Test_JSON": false, + "largest_connected_component": false, + "n_lcc": 1, + "n_folds": 3, + "invertible_augmentations": false, + "n_invertible": true, + + "class_JSON_path": false, + "additional_JSON_path": false, + "patch_size": [50,50,50], + "metric": "dice", + "n_cross_val": false, + "patch_probabilistic_oversampling": false, + "oversampling_probability": 1.0, + "test_train_validation": [ + 0.6, + 0.2, + 0.2 + ], + "shuffle": false + } +} + +``` +
    + +Comments:
    +The current configuration is loaded as a dictionary, which simplifies access and modification. This setup presents a strong foundation for integrating automated search algorithms for hyperparameter tuning, enabling more efficient model optimization.
    +The configuration structure could be reorganized and re-named to improve readability, making it easier for users to locate and adjust specific parameters. + +## Visualization of algorithm outputs ⚠️ +This module provides basic visualization functionality by saving output masks and images first to MedImage format and then to Nifti format. The `create_nii_from_medimage` function from MedImage.jl generates Nifti files, which can be loaded into MedEye3D for 3D visualization. + +Comments:
    +Integrating this visualization module more fully with the pipeline could eliminate unnecessary steps. By automatically loading output masks and images as raw data into MedEye3D for 3D visualization and supporting a more efficient end-to-end workflow. + +## K-fold cross-validation functionality βœ… +K-fold cross-validation is implemented to evaluate model performance more robustly. The data is split into multiple folds, with each fold serving as a validation set once, while the others form the training set. This functionality provides a better assessment of model performance across different subsets of the data. + +
    +K-fold cross-validation functionality: + +```julia +... + tstate = initialize_train_state(rng, model, optimizer) + if config["learning"]["n_cross_val"] + n_folds = config["learning"]["n_folds"] + all_tstate = [] + combined_indices = [indices_dict["train"]; indices_dict["validation"]] + shuffled_indices = shuffle(rng, combined_indices) + for fold in 1:n_folds + println("Starting fold $fold/$n_folds") + train_groups, validation_groups = k_fold_split(combined_indices, n_folds, fold, rng) + + tstate = initialize_train_state(rng, model, optimizer) + final_tstate = epoch_loop(num_epochs, train_groups, validation_groups, h5, model, tstate, config, loss_function, num_classes) + + push!(all_tstate, final_tstate) + end + else + final_tstate = epoch_loop(num_epochs, train_groups, validation_groups, h5, model, tstate, config, loss_function, num_classes) + end + return final_tstate +... +``` +
    + +The `k_fold_split` function organizes the indices for each fold, ensuring comprehensive coverage of the dataset during training. + +
    +k_fold_split + +```julia +function k_fold_split(data, n_folds, current_fold) + fold_size = length(data) Γ· n_folds + validation_start = (current_fold - 1) * fold_size + 1 + validation_end = validation_start + fold_size - 1 + validation_indices = data[validation_start:validation_end] + train_indices = [data[1:validation_start-1]; data[validation_end+1:end]] + return train_indices, validation_indices +end +``` +
    + +# Conclusions and Future Development +I have successfully established a foundation for a medical imaging pipeline, addressing significant challenges in data handling, model training, and augmentation integration. The integration of dataset-wide functions has significantly enhanced the reproducibility and handling of batched data with GPU support enabling scalability of experiments, making it easier for researchers and practitioners to produce better results. + +# Future Development +As we look to the future, there are several areas where MedPipe3D can be expanded and improved to better serve the medical AI community. These include: + +## Necessary Enhancements + +Comprehensive Logging: Develop detailed logging mechanisms that capture a wide range of events, including system statuses, model performance metrics, and user activities, to facilitate debugging and system optimization. This is currently executed as a simple `println` function. + +TensorBoard Integration: Implement an interface for TensorBoard to allow users to visualize training dynamics in real time, providing insights into model behavior and performance trends. + +Error and Warning Logs: Introduce advanced error and warning logging capabilities to alert users of potential issues before they affect the pipeline's performance, ensuring smoother operations and maintenance. + +Automated Visualization: Integrate MedEye3D directly into MedPipe3D to enable automated visualization of outputs, such as segmentation masks or other relevant medical imaging features. This feature would provide users with real-time visual feedback on model performance and data quality. +Code-Level Documentation: Due to needed changes in the fundamental structure of the pipeline in the final phase of the project, it is necessary to reevaluate all documentation. + +Official JuliaHealth Documentation: Extend the documentation efforts to include official entries on juliahealth.org, providing a centralized and authoritative resource for users seeking to learn more about MedPipe3D and its capabilities with examples shown + +## Potential Enhancements +GPU support for interpolation will allow for significant acceleration of such functions as Scale transform, Simulate, Low-resolution transform, Elastic deformation transform, and Resampling spacing. + +Add more reversible augmentations to test time. + +Calculating the average of the edges of the picture: checking the type of photo and calculating more correctly on this basis + +Elastic deformation transforms with the simulation of different tissue elasticities. + +# Acknowledgments πŸ™‡β€β™‚οΈ + +I would like to express my deepest gratitude to my mentor Dr. [Jakub Mitura](https://www.linkedin.com/in/jakub-mitura-7b2013151/) for his invaluable guidance and support throughout this project. 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Motivation to develop this application was to provide to rapidly growing Julia language scientific community tool for research in three dimensional medical images. Package is based on multiple open source software packages, yet most prominent is utilization of OpenGl specification to enable GPU acceleration. Application was tested both on Linux and Windows platforms and in both cases latency observed by the user in most common interaction like scrolling, annotation and change of displayed plane was very small. Thanks to utilization of many modern packages and methodologies developed package is providing convenient visualization in rapid prototyping with medical image segmentation algorithms. Application also is easily extendable and will be included in medical image segmentation framework that is currently in development.}, + affiliation = {Medical University of Lublin; Chair and Department of Nuclear Medicine, Medical University of Lublin}, + author = {Mitura, Jakub and Chrapko, Beata E.}, + doi = {10.26348/znwwsi.25.57}, + journal = {Zeszyty Naukowe Warszawskiej WyΕΌszej SzkoΕ‚y Informatyki}, + keywords = {OpenGl; Computer Tomography; PET/CT; medical image annotation; medical image visualization}, + language = {English}, + pages = {57--67}, + title = {3D Medical Segmentation Visualization in Julia with MedEye3d}, + volume = {nr 25}, + year = {2021}, +} diff --git a/blog/posts/divyansh-gsoc/supervoxel_rendering_fixed.png b/blog/posts/divyansh-gsoc/supervoxel_rendering_fixed.png new file mode 100644 index 0000000000000000000000000000000000000000..a0df9c743ab6b82060a293c657512ece63d3f6a1 GIT binary patch literal 132685 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[**LinkedIn**](https://www.linkedin.com/in/michela-rocchetti-261793218/), [**GitHub**](https://github.com/MichelaRocchetti) + +# Project Description + +*IPUMS* is the "world's largest available single database of census microdata", providing survey and census data from around the world. +It includes several projects that provide a wide variety of datasets. +The information and data collected by *IPUMS* are useful for comparative research, as well as for the analysis of individuals in their life contexts. +These data can be used to create a more comprehensive dataset that will facilitate research on the social determinants of health for different types of diseases, social communities, and geographical areas. + +![](./IPUMS_grid_logo.png) + +> To learn more about IPUMS, visit the [website](https://www.ipums.org) + +# Tasks and Goals +The primary objectives of this proposal are to: + +1. Develop a native Julia package to interact with the APIs available around the datasets *IPUMS* provides. + +2. Provide useful utilities within this package for manipulating *IPUMS* datasets. + +3. Compose this package with the wider Julia ecosystem to enable novel research in health, economics, and more. + +To achieve this, the work was distributed as follows: + +1. Expand some of the functionality developed in `ipumsr` *IPUMS* NHGIS + - Create a link between OpenAPI documentation and the functions internally used in IPUMS.jl: + updating already present functions, determining if updating is needed, and testing them + - Develop functionality similar to the get_metadata_nghis function present in ipumsr + +2. Update *IPUMS* documentation + - Set up and deploy DocumenterVitepress.jl + - Write a blog post on how IPUMS.jl can be composed within the ecosystem. + + +# How the work was done + +The first task was to migrate documents from Documenter to DocumenterVitepress.This issue aims to support the significant refactoring underway across JuliaHealth, aimed at improving the discoverability and cohesion of the JuliaHealth ecosystem, particularly about documentation. This issue is intended to create a more attractive entry point for new Julia users interested in health research within the Julia community. +To accomplish this task, a dependency of DocumenterVitepress was added to the docs directory of the IPUMS.jl repository. +Once this was done, the Documenter.jl make.jl file was migrated into a DocumenterVitepress.jl make.jl file. Working on the make.jl file, the pages structure were added to the web page explaining the IPUMS.jl package. With this in mind, those were added: + 1. Home: to explain the main purpose of the package + 2. Workflows: to explain the working process + 3. How to: to give general information + 4. Tutorials: to show how to use IPUMS.jl + 5. Examples: some examples of activities + 6. Mission: to explain why the package is useful for the community + 7. References: references used to write the pages. + +This first task takes some time, especially setting up GitHub and cloning the repository locally. At this point, my experience with GitHub was really limited and I had to learn how to use the Git environment from scratch, for example how to do continuous integration (to commit code to a shared repository), documentation release and merge, and local testing. I found the support of my mentors and searching for material online was really helpful. + +The second task was to update the documentation of IPUMS.jl by modifying the functionality within the model folder in the IPUMS.jl folder. The main aim of this task was to +a description of the function and its attributes, an example of possible implementation and result, and finally to show how to use it. The documentation to be updated as of several types of functions: + 1. Data extract + 2. Data set + 3. Data Table + 4. Time series table + 5. Error + 6. Shapefile. + Each of these macro-categories (from 1 to 4) contains a set of functions, each signaling the different expected output and specific purpose. + Information about what each function does, and the meaning of each specific input variable, has been found on the *IPUMS* website and references have been made in the written documentation. + +# How to work with IPUMS + After writing down the description of the function and the inputs, examples were formulated, starting from the *IPUMS* website: when you register at [IPUMS](https://uma.pop.umn.edu/usa/user/new), an API key is given. +which is used, among other things, to run pre-written code on the website. This code contains examples of these functions, and these examples +have been adapted by changing some input values and adapting them to work in the Julia framework. The latter task was done by simply rewriting some structures, such as dictionaries, maps, or lists, in the +Julia language. +Here is a small guide on how to set up working with the API: +1. Create an *IPUMS* account +2. Log in to your account +3. Copy the API key, which can be obtained from the [website](https://account.ipums.org/api_keys) +4. Use the key to run the code that is already available on the [*IPUMS* Developer Portal](https://developer.ipums.org/docs/v2/reference/), where you will also find information about the variables and packages. + +# Functions testing + + A final task was to test the functions in the 'api_IPUMSAPI.jl' file. In this file, the function to be tested and other functions are defined and the most important ones are extracted to be available in the + available throughout the framework. Some of the functions to be tested were the following: + + 1. `metadata_nhgis_data_tables_get` + 2. `metadata_nhgis_datasets_dataset_data_tables_data_table_get` + 3. `metadata_nhgis_datasets_dataset_get` + 4. `metadata_nhgis_datasets_get` + + Before working on the Julia files, testing and understanding the original R function was done using R studio. + +![](./rstudio.png) + +Each function was then tested using the API key from the *IPUMS* registration as well as other input examples taken from the documentation or the *IPUMS* website. +or from the *IPUMS* website. All functions were displayed successfully, giving the expected result, so it can be concluded that the translation from R to Julia is successful. + +```{julia} +#| eval: false + +using IPUMS +using OpenAPI + +api_key = "insert your key here" + +version = "2" +page_number = 1 +page_size = 2500 +#media_type = + +api = IPUMSAPI("https://api.ipums.org", Dict("Authorization" => api_key)); + +res1 = metadata_nhgis_data_tables_get(api, version) + +res2 = metadata_nhgis_datasets_dataset_get(api, "2022_ACS1", "2"); + +res3 = metadata_nhgis_datasets_dataset_data_tables_data_table_get(api, "2022_ACS1","B01001", "2"); + +res4 = metadata_nhgis_datasets_get(api, "2"); +``` + +An example of the output is: + +```{json} +. . . + +{ + "name": "NT1", + "nhgisCode": "AAA", + "description": "Total Population", + "universe": "Persons", + "sequence": 1, + "datasetName": "1790_cPop", + "nVariables": [ + 1 + ] +} + +. . . +``` + +# Accomplished Goals and Future Development + +The project was a 90-hour small project and during this time the documentation was completed and the testing of the metadata function was done, as well as the migration from Documenter.jl to DocumenterVitepress.jl. +During these months some things took longer than I expected because of some problems that occurred, so some things were missing in relation to the original plan. However, this time was useful for learning new things: + - I saw how to work with a package under development, how to work with large datasets, and how to write documentation + - I had the opportunity to better understand how to work with Git and GitHub + - I learned some new things about R, which was a completely unknown language to me. + - I deepened my knowledge of Julia, a language I had worked with during my time at university. + - I had the chance to work on a large open-source project, to be part of a large community, and to learn how to communicate with it efficiently. + +A special thanks goes to my mentors, Jacob Zelko and Krishna Bhogaonker, for helping me through this process. + +Future developments of this work could include deepening the work that my mentors and I have started, with the possibility of integrating this package with other machine learning packages in Julia and, from there, doing new analyses of the data in terms of social and geographical implications for 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+Hello πŸ‘‹, I am Mounika. I am a Data Engineer at [Brown Center for Biomedical Informatics](https://bcbi.brown.edu/). This summer, I had the privilege of co-mentoring a talented student, [Jay Sanjay](https://www.linkedin.com/in/jay-landge-589439260/) alongside [Jacob Zelko (\@TheCedarPrince)](https://jacobzelko.com) on a [project](https://summerofcode.withgoogle.com/programs/2024/projects/ZXVIYAXG) for Google Summer of Code (aka [GSoC](https://summerofcode.withgoogle.com/)). Here, I would like to share my experience as a co-mentor, offering insights for future mentors and students alike. + +Before diving into my experience, let me provide some background on how it all started. At JuliaCon 2023, I had the chance to meet Jacob Zelko and have been following his work at [JuliaHealth](https://juliahealth.org/) ever since. One day, I received a message from Jacob asking if I'd be interested in co-mentoring Jay for his GSoC project. Fortunately, I was already working on several projects at BCBI involving Julia programming, [OMOP CDM databases](https://ohdsi.github.io/CommonDataModel/cdm54.html) and [OHDSI](https://ohdsi.org/) tools, all of which were closely aligned with Jay's project. + +> Feel free to visit Jay's work on [OMOPCDMPathways.jl](https://github.com/JuliaHealth/OMOPCDMPathways.jl) or read about his [fellowship experience from this post](https://juliahealth.org/JuliaHealthBlog/posts/jay-gsoc/gsoc-2024-fellows.html). + +# Mentor-Mentee Relationship + +Jay, being a proactive student with a strong involvement in JuliaHealth, worked closely with Jacob to build a [proposal for the project](https://summerofcode.withgoogle.com/organizations/the-julia-language/projects/details/ZXVIYAXG) several months before GSoC began this year. His early involvement and familiarity with the community set a solid foundation for the project. Jacob, with his extensive experience mentoring GSoC students over the years, brought invaluable insights not only for Jay but also for me, as I was just beginning my journey as a mentor. + +Jacob established regular weekly Zoom meetings for the three of us to discuss Jay's progress, review his accomplishments, and plan the next steps. During these meetings, I focused on taking detailed notes to ensure we stayed organized and up to date with all the tasks. We used [Trello](https://trello.com/), a project management tool, to track progress and manage project tasks efficiently. Additionally, we stayed connected thoughout the week via a dedicated slack channel for any ongoing discussions or questions (on the [Julia Slack](https://julialang.org/slack/#the_julia_language_slack)). + +# Technical Discussion + +Jay's project "Developing Tooling for Observational Health Research in Julia" was inspired by the [TreatmentPatterns R package](https://www.sciencedirect.com/science/article/pii/S016926072200462X?via%3Dihub) [@markus2022treatmentpatterns]. The main goal of the project was to enhance observational research capabilities within the JuliaHealth ecosystem. To help Jay get started, [Jacob created 10 to 15 GitHub issues](https://github.com/JuliaHealth/OMOPCDMPathways.jl/issues?q=), each linked to a specific function that Jay planned to work on. + +During our weekly meeting, we discussed the challenges Jay encountered, any roadblacks in his progress, and reviewed the pull requests he submitted on GitHub. These sessions allowed us to provide timely feedback and guide Jay through complex technical issues, ensuring steady progress throughout the project. + +# Learnings and Observations + +Jay's proactive approach, steady progress, thoughtful questions, and clear focus on completing the project are qualities from which every student can benefit. His dedication to learning and problem-solving made a significant impact on the success of the project. + +## Tips for Mentees + +From a mentee's perspective having the following qualities would be helpful + +1. **Stick to the proposal:** While it's natural to feel the urge explore new ideas beyond the original proposal, it's essential to remain focused on the original proposal due to time constrains. + +2. **Adaptability and open-mindedness:** Be open to feedback and willing to adjust the tasks as you face challenges. + +3. **Time Management:** Many students juggle internships, interviews and other commitments during the summer. So it's to manage time effectively and discuss with the mentor about the progress during those times. + +4. **Effective communication:** Stay up to date with any updates from GSoC or from the mentor. Keeping your mentor updated about your progress or any challenges helps build a collaborative and supportive mentor relationship. + +## Tips for Mentors + +On the other hand, Jacob demonstrated what it means to be an effective mentor. He showed me how to foster a supportive, collaborative relationship with the student. These are the lessons that I will carry forward in future mentorship roles: + +From a mentor's perspective having the following qualities would be helpful + +1. **Clear communication:** Communicating well in advance about the availability to meet or to review the work, having frequent meetings with the mentee would be helpful. + +2. **Encouragement:** While offering support, it's important to encourage the mentee to take ownership of the project. + +3. **Commitment and time:** Mentoring GSoC is a voluntary role, often taken on in addition to regular professional work. Balancing GSoC with other work commitments requires effective time management and commitment. + +4. **Structured Guidance:** Providing a well-organized plan, such as using task management tools like Trello and GitHub issues, ensures that the mentee can follow a clear path towards success completion of the project. + +# Conclusion + +Google Summer of Code offers an incredible opportunity for students to hone their programming skills while contributing to impactful open-source projects. It was a rewarding experience to be part of this journey as a co-mentor, and I am grateful to Jacob for giving me the chance to be involved in such a meaning project with the JuliaHealth community. + +Through this experience, I not only gained insights into effective mentorship but also deepened my understanding of open-source collaboration and its potential to drive innovation in healthcare. I'm excited to explore further ways I can contribute to the JuliaHealth ecosystem and continue supporting the community. + +## Let's Keep in Touch! + +If you would like to know more about me, you can connect with me on [Linkedin](https://www.linkedin.com/in/mounika-thakkallapally/). diff --git a/blog/posts/mounika-gsoc-mentor/references.bib b/blog/posts/mounika-gsoc-mentor/references.bib new file mode 100644 index 0000000..d6d69f1 --- /dev/null +++ b/blog/posts/mounika-gsoc-mentor/references.bib @@ -0,0 +1,9 @@ +@article{markus2022treatmentpatterns, + title={TreatmentPatterns: an R package to facilitate the standardized development and analysis of treatment patterns across disease domains}, + author={Markus, Aniek F and Verhamme, Katia MC and Kors, Jan A and Rijnbeek, Peter R}, + journal={Computer Methods and Programs in Biomedicine}, + volume={225}, + pages={107081}, + year={2022}, + publisher={Elsevier} +} diff --git a/blog/posts/ryan-gsoc/Benchmark_Page.png 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znPW;81+o%*0yUi`(I#u|tytT3p#d9h88@b3+-svv)ji*{K9+^}jkp%K*EW&_32s24 z;cVTj89Q9td|qQWl>w{2zus9tdbr@wtuG;2ZQo z%WSF@5t;DnN~{n5{h)oO$-IG1!GZL`k>`UJoAum2W#qrp&uUYDHeqxtI7G+9ZMZ^Q zoZOjgH3Mdt7Duhv8au$w3p4M`G=?8Bmh_zFOamJ}WWRdI@B0SXNPfF~Z+J(f{~Pvd B If you want to learn more about me, you can connect with me here: [**LinkedIn**](https://www.linkedin.com/in/ryan-kierulf-022062201/), [**GitHub**](https://github.com/rkierulf) + +# What is KomaMRI? + +[KomaMRI](https://github.com/JuliaHealth/KomaMRI.jl) is a Julia package for efficiently simulating Magnetic Resonance Imaging (MRI) acquisitions. MRI simulation is a useful tool for researchers, as it allows testing new pulse sequences to analyze the signal output and image reconstruction quality without needing to actually take an MRI, which may be time or cost-prohibitive. + +In contrast to many other MRI simulators, KomaMRI.jl is open-source, cross-platform, and comes with an intuitive user interface (To learn more about KomaMRI, you can read the paper introducing it [here](https://onlinelibrary.wiley.com/doi/full/10.1002/mrm.29635)). However, being developed fairly recently, there are still new features that can be added and optimization to be done. + +# Project Goals + +The goals outlined by Carlos (my project mentor) and I the beginning of this summer were: + +1. Extend GPU support beyond CUDA to include AMD, Intel, and Apple Silicon GPUs, through the packages [AMDGPU.jl](https://github.com/JuliaGPU/AMDGPU.jl), [oneAPI.jl](https://github.com/JuliaGPU/oneAPI.jl), and [Metal.jl](https://github.com/JuliaGPU/Metal.jl) + +2. Create a CI pipeline to be able to test each of the GPU backends + +3. Create a new kernel-based simulation method optimized for the GPU, which we expected would outperform array broadcasting + +4. (Stretch Goal) Look into ways to support running distributed simulations across multiple nodes or GPUs + + +# Step 1: Support for Different GPU backends + +Previously, KomaMRI's support for GPU acceleration worked by converting each array used within the simulation to a `CuArray`, the device array type defined in [CUDA.jl](https://github.com/JuliaGPU/CUDA.jl). This was done through a general `gpu` function. The inner simulation code is GPU-agnostic, as the same operations can be performed on a CuArray or a plain CPU Array. This approach is good for extensibility, as it does not require writing different simulation code for the CPU / GPU, or different GPU backends, and would only work in a language like Julia based on runtime dispatch! + +To extend this to multiple GPU backends, all that is needed is to generalize the `gpu` function to convert to either the device types of CUDA.jl, AMDGPU.jl, Metal.jl, or oneAPI.jl, depending on which backend is being used. To give an idea of what the gpu conversion code looked like before, here is a snippet: + +```julia +struct KomaCUDAAdaptor end +adapt_storage(to::KomaCUDAAdaptor, x) = CUDA.cu(x) + +function gpu(x) + check_use_cuda() + return use_cuda[] ? fmap(x -> adapt(KomaCUDAAdaptor(), x), x; exclude=_isleaf) : x +end + +#CPU adaptor +struct KomaCPUAdaptor end +adapt_storage(to::KomaCPUAdaptor, x::AbstractArray) = adapt(Array, x) +adapt_storage(to::KomaCPUAdaptor, x::AbstractRange) = x + +cpu(x) = fmap(x -> adapt(KomaCPUAdaptor(), x), x) +``` + +The `fmap` function is from the package `Functors.jl` and can recursively apply a function to a struct tagged with `@functor`. The function being applied is `adapt` from `Adapt.jl`, which will call the lower-level `adapt_storage` function to actually convert to / from the device type. The second parameter to `adapt` is what is being adapted, and the first is what it is being adapted to, which in this case is a custom adapter struct `KomaCUDAAdapter`. + +One possible approach to generalize to different backends would be to define additional adapter structs for each backend and corresponding `adapt_storage` functions. This is what the popular machine learning library [Flux.jl](https://github.com/FluxML/Flux.jl) does. However, there is a simpler way! + +Each backend package (CUDA.jl, Metal.jl, etc.) already defines `adapt_storage` functions for converting different types to / from corresponding device type. Reusing these functions is preferable to defining our own since, not only does it save work, but it allows us to rely on the expertise of the developers who wrote those packages! If there is an issue with types being converted incorrectly that is fixed in one of those packages, then we would not need to update our code to get this fix since we are using the definitions they created. + +Our final `gpu` and `cpu` functions are very simple. The `backend` parameter is a type derived from the abstract `Backend` type of [`KernelAbstractions.jl`](https://github.com/JuliaGPU/KernelAbstractions.jl), which is extended by each of the backend packages: + +```julia +import KernelAbstractions as KA + +function gpu(x, backend::KA.GPU) + return fmap(x -> adapt(backend, x), x; exclude=_isleaf) +end + +cpu(x) = fmap(x -> adapt(KA.CPU(), x), x, exclude=_isleaf) +``` + +The other work needed to generalize our GPU support involved switching to use [package extensions](https://pkgdocs.julialang.org/v1/creating-packages/#Conditional-loading-of-code-in-packages-(Extensions)) to avoid having each of the backend packages as an explicit dependency, and defining some basic GPU functions for backend selection and printing information about available GPU devices. The pull request for adding support for multiple backends is linked below: + +> https://github.com/JuliaHealth/KomaMRI.jl/pull/405 + +# Step 2: Buildkite CI + +At the time the above pull request was merged, we weren't sure whether the added support for AMD and Intel GPUs actually worked, since we only had access to CUDA and Apple Silicon GPUs. So the next step was to set up a CI to test each GPU backend. To do this, we used [Buildkite](https://github.com/JuliaGPU/KernelAbstractions.jl), which is a CI platform that many other Julia packages also use. Since there were many examples to follow, setting up our testing pipeline was not too difficult. Each step of the pipeline does the required environment setup and then calls `Pkg.test()` for KomaMRICore. As an example, here is what the AMDGPU step of our pipeline looks like: + +```{yml} + - label: "AMDGPU: Run tests on v{{matrix.version}}" + matrix: + setup: + version: + - "1" + plugins: + - JuliaCI/julia#v1: + version: "{{matrix.version}}" + - JuliaCI/julia-coverage#v1: + codecov: true + dirs: + - KomaMRICore/src + - KomaMRICore/ext + command: | + julia -e 'println("--- :julia: Instantiating project") + using Pkg + Pkg.develop([ + PackageSpec(path=pwd(), subdir="KomaMRIBase"), + PackageSpec(path=pwd(), subdir="KomaMRICore"), + ])' + + julia --project=KomaMRICore/test -e 'println("--- :julia: Add AMDGPU to test environment") + using Pkg + Pkg.add("AMDGPU")' + + julia -e 'println("--- :julia: Running tests") + using Pkg + Pkg.test("KomaMRICore"; coverage=true, test_args=["AMDGPU"])' + agents: + queue: "juliagpu" + rocm: "*" + timeout_in_minutes: 60 +``` + +We also decided that in addition to a testing CI, it would also be helpful to have a benchmarking CI to track performance changes resulting from each commit to the main branch of the repository. [Lux.jl](https://github.com/LuxDL/Lux.jl) had a very nice-looking benchmarking page, so I decided to look into their approach. They were using [github-action-benchmark](https://github.com/benchmark-action/github-action-benchmark), a popular benchmarking action that integrates with the Julia package [`BenchmarkTools.jl`](https://github.com/JuliaCI/BenchmarkTools.jl). github-action-benchmark does two very useful things: + +1. Collects benchmarking data into a json file and provides a default index.html to display this data. If put inside a relative path in the gh-pages branch of a repository, this results in a public benchmarking page which is automatically updated after each commit! + +2. Comments on a pull request with the benchmarking results compared with before the pull request. Example: https://github.com/JuliaHealth/KomaMRI.jl/pull/442#pullrequestreview-2213921334 + +The only issue was that since github-action-benchmark is a github action, it is meant to be run within github by one of the available github runners. While this works for CPU benchmarking, only Buildkite has the CI setup for each of the GPU backends we are using, and Lux.jl's benchmarks page only included CPU benchmarks, not GPU benchmarks (Note: we talked with Avik, the repository owner of Lux.jl, and Lux.jl has since adopted the approach outlined below to display GPU and CPU benchmarks together). I was not able to find any examples of other julia packages using github-action-benchmark for GPU benchmarking. + +Fortunately, there is a tool someone developed to download results from Buildkite into a github action (https://github.com/EnricoMi/download-buildkite-artifact-action). This repository only had 1 star when I found it, but it does exactly what we needed: it identifies the corresponding Buildkite build for a commit, waits for it to finish, and then downloads the artifacts for the build into the github action it is being run from. With this, we were able to download the Buildkite benchmark results from a final aggregation step into our benchmarking action and upload to github-action-benchmark to publish to either the main data.js file for our benchmarking website, or pull request. + +Our final benchmarking page looks like this and is [publicly accessible](https://juliahealth.org/KomaMRI.jl/benchmarks/): + +![](./Benchmark_Page.png) + +One neat thing about github-action-benchmark is that the default index.html is extensible, so even though by deault it only shows time, the information for memory usage and number of allocations is also collected into the json file, and can be displayed as well. + +A successful CI run on Buildkite Looks like [this](https://buildkite.com/julialang/komamri-dot-jl/builds/925): + +![](./CI_Run.png) + +The pull requests for creating the CI testing and benchmarking pipeline, and changing the index.html for our benchmark page are listed below: + +1. https://github.com/JuliaHealth/KomaMRI.jl/pull/411 +2. https://github.com/JuliaHealth/KomaMRI.jl/pull/418 +3. https://github.com/JuliaHealth/KomaMRI.jl/pull/421 + +# Step 3: Optimization + +With support for multiple backends enabled, and a robust CI, the next step was to optimize our simulation code as much as possible. Our original idea was to create a new GPU-optimized simulation method, but before doing this we wanted to look more at the existing code and optimize for the CPU. + +The simulation code is solving a differential equation (the [Bloch equations(https://en.wikipedia.org/wiki/Bloch_equations)]) over time. Most differential equation solvers step through time, updating the current state at each time step, but our previous simulation code, more optimized for the GPU, did a lot of computations across all time points in a simulation block, allocating a matrix of size `Nspins by NΞ”t` each time this was done. Although this is beneficial for the GPU, where there are millions of threads available on which to parallelize these computations, for the CPU it is more important to conserve memory, and the aforementioned approach of stepping through time is preferable. + +After seeing that this approach did help speed up simulation time on the CPU, but was not faster on the GPU (7x slower for Metal!) we decided to separate our simulation code for the GPU and CPU, dispatching based on the `KernelAbstractions.Backend` type depending on if it is `<:KernelAbstractions.CPU` or `<:KernelAbstractions.GPU`. + +Other things we were able to do to speed up CPU computation time: + +1. Preallocating each array used inside the core simulation code so it can be re-used from one simulation block to the next. + +2. [Skipping an expensive computation](https://github.com/JuliaHealth/KomaMRI.jl/blob/master/KomaMRICore/src/simulation/SimMethods/Bloch/BlochCPU.jl#L90) if the magnetization at that time point is not added to the final signal + +3. Ensuring that each statement is fully broadcasted. We were surprised to see the difference between the following examples: + +```julia +#Fast +Bz = x .* seq.Gx' .+ y .* seq.Gy' .+ z .* seq.Gz' .+ p.Ξ”w ./ T(2Ο€ .* Ξ³) + +#Slow +Bz = x .* seq.Gx' .+ y .* seq.Gy' .+ z .* seq.Gz' .+ p.Ξ”w / T(2Ο€ * Ξ³) +``` + +4. Using the `cis` function for complex exponentiation, which is faster than `exp` + +With these changes, the mean improvement in simulation time aggregating across each of our benchmarks for 1, 2, 4, and 8 CPU threads was ~4.28. For 1 thread, the average improvement in memory usage was 90x! + +The next task was optimizing the simulation code for the GPU. Although our original idea was to put everything into one GPU kernel, we found that the existing broadcasting operations were already very fast, and that custom kernels we wrote were not able to outperform the previous implementation. The Julia GPU compiler team deserves a lot of credit for developing such fast broadcasting implementations! + +However, this does not mean that we were unable to improve the GPU simulation time. Similar to with the CPU, preallocation made a substantial difference. Parallelizing as much work as possible across the time points for a simulation block was also found to beneficial. For the parts that needed to be done sequentially, a [custom GPU kernel](https://github.com/JuliaHealth/KomaMRI.jl/blob/master/KomaMRICore/src/simulation/SimMethods/Bloch/KernelFunctions.jl#L5) was written which used the `KernelAbstractions.@localmem` macro for arrays being updated at each time step to yield faster memory access. + +The mean speedup we saw across the 4 supported GPU backends was 4.16, although this varied accross each backend (for example, CUDA was only 2.66x faster while oneAPI was 28x faster). There is a [remaining bottleneck](https://github.com/JuliaHealth/KomaMRI.jl/blob/master/KomaMRICore/src/simulation/SimMethods/Bloch/BlochGPU.jl#L151) in the `run_spin_preceession!` function having to do with logical indexing that I was not able to resolve, but could be solved in the future to speed up the GPU simulation time even further! + +The pull requests optimizing code for the CPU and GPU are below: + +1. https://github.com/JuliaHealth/KomaMRI.jl/pull/443 + +2. https://github.com/JuliaHealth/KomaMRI.jl/pull/459 + +3. https://github.com/JuliaHealth/KomaMRI.jl/pull/462 + +# 4. Step 4: Distributed Support + +This last step was a stretch goal for exploring how to add distributed support to KomaMRI. MRI simulations can become quite large, so it is useful to be able to distribute work across either multiple GPUs or multiple compute nodes. + +A nice thing about MRI simulation is the independent spin property: if a phantom object (representing, for example a brain tissue slice) is divided into two parts, and each part is simulated separately, the signal result from simulating the whole phantom will be equal to the sum of the signal results from simulating each subdivision of the original phantom. This makes it quite easy to distribute work, either across more than one GPU or accross multiple compute nodes. + +The following scripts worked, with the only necessary code change to the repository being a new + function to add two RawAcquisitionData structs: + +```julia +#Use multiple GPUs: +using Distributed +using CUDA + +#Add workers based on the number of available devices +addprocs(length(devices())) + +#Define inputs on each worker process +@everywhere begin + using KomaMRI, CUDA + sys = Scanner() + seq = PulseDesigner.EPI_example() + obj = brain_phantom2D() + #Divide phantom + parts = kfoldperm(length(obj), nworkers()) +end + +#Distribute simulation across workers +raw = Distributed.@distributed (+) for i=1:nworkers() + KomaMRICore.set_device!(i-1) #Sets device for this worker, note that CUDA devices are indexed from 0 + simulate(obj[parts[i]], seq, sys) +end +``` + +```julia +#Use multiple compute nodes +using Distributed +using ClusterManagers + +#Add workers based on the specified number of SLURM tasks +addprocs(SlurmManager(parse(Int, ENV["SLURM_NTASKS"]))) + +#Define inputs on each worker process +@everywhere begin + using KomaMRI + sys = Scanner() + seq = PulseDesigner.EPI_example() + obj = brain_phantom2D() + parts = kfoldperm(length(obj), nworkers()) +end + +#Distribute simulation across workers +raw = Distributed.@distributed (+) for i=1:nworkers() + simulate(obj[parts[i]], seq, sys) +end +``` + +Pull reqeust for adding these examples to the KomaMRI documentation: https://github.com/JuliaHealth/KomaMRI.jl/pull/468 + +# Conclusions / Future Work + +This project was a 350-hour large project, since there were many goals to accomplish. To summarize what changed since the beginning of the project: + +1. Added support for AMDGPU.jl, Metal.jl, and oneAPI.jl GPU backends + +2. CI for automated testing and benchmarking accross each backend + [public benchmarks page](https://juliahealth.org/KomaMRI.jl/benchmarks/) + +3. Significantly faster CPU and GPU performance + +4. Demonstrated distributed support and examples added in documentation + +Future work could look at ways to further optimize the simulation code, since despite the progress made, I believe there is more work to be done! The aforementioned logical indexing issue is still not resolved, and the kernel used inside the `run_spin_excitation!` function has not been profiled in depth. KomaMRI is also looking into adding support for higher-order ODE methods, which could require more GPU kernels being written. + +# Acknowledgements + +I would like to thank my mentor, Carlos Castillo, for his help and support on this project. I would also like to thank Jakub Mitura, who attended some of our meetings to help with GPU optimization, Dilum Aluthge who helped set up our BuildKite pipeline, and Tim Besard, who answered many GPU-related questions that Carlos and I had. diff --git a/docs/assets/logo.png b/docs/assets/images/logo.png similarity index 100% rename from docs/assets/logo.png rename to docs/assets/images/logo.png diff --git a/docs/blog/index.html b/docs/blog/index.html index 43cf7cf..64cfb54 100644 --- a/docs/blog/index.html +++ b/docs/blog/index.html @@ -38,12 +38,10 @@ - - + - - + +

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ziOU^kno+}FVCeM8KM2%9Ko|fL83w@zg+W1{TvqkZ)vnF~IQK{+FlzJg<~zN-&_&5v z!q{Ktk)HQQ#pUl>9O;W1_!Qbd_x8{8<6e#%@FJQ#+|QzT7nvX;+_FpL)xljA~vvwUar$q4_8!m8r z&Vr(8d&8kRq;DuJkCMjopog+%&(kSJdLS_WuJz#q{Etq&CLJ9+uvjQr_# literal 0 HcmV?d00001 diff --git a/blog/posts/jay-gsoc/gsoc-2024-fellows.qmd b/blog/posts/jay-gsoc/gsoc-2024-fellows.qmd new file mode 100644 index 0000000..85602d5 --- /dev/null +++ b/blog/posts/jay-gsoc/gsoc-2024-fellows.qmd @@ -0,0 +1,333 @@ +--- +title: "GSoC '24: Developing Tooling for Observational Health Research in Julia" +description: "A summary of my project for Google Summer of Code - 2024" +author: "Jay Sanjay Landge" +date: "9/7/2024" +bibliography: ./references.bib +csl: ./../../ieee-with-url.csl +toc: true +engine: julia +image: false +categories: + - gsoc + - sql + - observational health + - analysis +--- + + +# Hi Everyone! πŸ‘‹ + +I am Jay Sanjay, and I am pursuing a Bachelor's degree in Computational Sciences and Engineering at the Indian Institute of Technology (IIT) in Hyderabad, India. Coming from a mathematics and data analysis background, I was initially introduced to Julia at my university lectures. Later, I delved more into the language and the JuliaHealth community - an intersection of Julia, Health Research, Data Sciences, and Informatics. Here, I met some of the great folks in JuliaHealth and I decided to take it on as a full-fledged summer project. +In this blog, I will briefly describe what my project is and what I did as a part of it. + + +1. You can find my [**GSoC project archive link**](https://summerofcode.withgoogle.com/archive/2024/projects/ZXVIYAXG) + +2. You can also find the related publication of my work on [**Zenodo**](https://zenodo.org/records/14674051) + +3. If you want to know more about me, you can connect with me on [**LinkedIn**](https://www.linkedin.com/in/jay-landge-589439260/) and follow me on [**GitHub**](https://github.com/Jay-sanjay) + + +# Background + +## What Is Observational Health Research? + +Observational Health Research refers to studies that analyze real-world data (such as patient medical claims, electronic health records, etc.) to understand patient health. These studies often encompass a vast amount of data concerning patient care. An outstanding challenge here is that these datasets can become very complex and grow large enough to require advanced computing methods to process this information. + +## What Are Patient Pathways? + +Patient pathways refer to the journey that patients with specific medical conditions undergo in terms of their treatment. This concept goes beyond simple drug uptake statistics and looks at the sequence of treatments patients receive over time, including first-line treatments and subsequent therapies. Understanding patient pathways is essential for analyzing treatment patterns, adherence to clinical guidelines, and the disbursement of drugs. +To analyze patient pathways, one would typically use real-world data from sources such as electronic health records, claims data, and registries. However, barriers such as data interoperability and resource requirements have hindered the full utilization of real-world data for this purpose. + +So to address these challenges we (the JuliaHealth organization and I) want to develop a set of tools to extract and analyze these patient pathways. These sets of tools are based on the Observational Medical Outcomes Partnership (OMOP) Common Data Model, which standardizes healthcare data to promote interoperability. + + +# Project Description + +As part of this project with JuliaHealth, I developed a new package called [**OMOPCDMPathways.jl**](https://github.com/JuliaHealth/OMOPCDMPathways.jl). This package is designed for deployment in research projects, particularly those related to health and medical data analysis. This project takes much inspiration from the paper [_TreatmentPatterns: An r package to facilitate the standardized development and analysis of treatment patterns across disease domains_](https://www.sciencedirect.com/science/article/pii/S016926072200462X?via%3Dihub) [@markus2022treatmentpatterns] and explores the implementation of some of those ideas to develop new tools within the JuliaHealth Observational Health Subecosystem for exploring patient pathways. Additional new features and approaches were added and explored within this project. Additionally, I have authored a developer guide for the package, providing instructions on its use and contribution. This project provided me with hands-on experience in developing production-level code and exposed me to open-source software development practices. I had the opportunity to work in a team, under my mentors, and ensured the integration of the package with the rest of JuliaHealth, facilitating its adoption and usability within the community. + +# Project Goals + +As a part of the development, I was majorly engaged in crafting the following functionalities: + +1. Selecting treatments of interest: The first decision that was made was to decide the time from which the desired treatments of interest should be included in the treatment pathway study. Here the [periodPriorToIndex](https://github.com/JuliaHealth/OMOPCDMPathways.jl/issues/1) specifies the period (i.e. number of days) before the index date from which treatments should be included. + +2. Find Treatment History of Patients: Create the [treatment history](https://github.com/JuliaHealth/OMOPCDMPathways.jl/issues/4) of a patient based on target, event, and exit cohorts. Then filter patient events based on the start and end dates of the target cohort. Third, Calculate the duration of treatment eras and the gap between treatments. + +3. Filters: Filter the treatment history based on the [minEraDuration](https://github.com/JuliaHealth/OMOPCDMPathways.jl/issues/5) parameter and [EraCollapse](https://github.com/JuliaHealth/OMOPCDMPathways.jl/issues/2) parameter. + +4. Create a Continuous Integration and Continuous Development pipeline for the package. + +5. Implement the combinationWindow function, which combines treatments with various overlapping strategies. + +Additionally, we had a few stretch goals which were: + +1. Composing with JuliaStats Ecosystem + +2. Novel Visualizations for Pathways + +# Tasks + +## 1. Setting Up the Package in JuliaHealth Channel + +Initially, there was no package as such for generating pathways, so I had to build it from scratch. First, I created the repository with the name [OMOPCDMPathways.jl](https://github.com/JuliaHealth/OMOPCDMPathways.jl). Once the repository was created, we needed to have a skeleton for a standard Julia repository. For this, we used the [PkgTemplates.jl](https://juliaci.github.io/PkgTemplates.jl/stable/user/) this provided a basic skeleton for the repository that included - folders for test suites, documentation, src code files, GitHub files, README and LICENSE file, TOML and citation files. All this we can further edit and modify as per our work. By default, PkgTemplate.jl uses [Documenter.jl](https://documenter.juliadocs.org/stable/) for the documentation part but as suggested and discussed with my mentor we decided to shift to [DocumenterVitepress.jl](https://luxdl.github.io/DocumenterVitepress.jl/dev/) for the documentation part. However, we still faced some deployment issues in the new documentation due to a few mistakes in the `make.jl` file, thanks to [Anshul Singhvi](https://github.com/asinghvi17) for helping fix the [Deployment issues with DocumenterVitepress](https://github.com/JuliaHealth/OMOPCDMPathways.jl/issues/15). With this, we were ready with the documentation set up and fully functional. After we had shifted to DocumenterVitepress the main task now was to host the documentation, this was done using Github-Actions, detailed steps for hosting are provided at [this](https://documenter.juliadocs.org/stable/man/hosting/#Hosting-Documentation) page. Then we added the CodeCov to our package by triggering it via a dummy function and a corresponding test case for it. Also, the CI for the package was set up with it. And, now finally the repository was ready with test coverage, CI, and documentation fully functional repository ready. Here's some snapshots of the documentation set-up: + +![](./image.png) + +> Initial documentation with Documenter.jl + +![](./img2.png) + +> New documentation using DocumenterVitepress.jl + +So, as a part of it, I created this [documentation](https://luxdl.github.io/DocumenterVitepress.jl/dev/documenter_to_vitepress_docs_example) which provides detailed steps for converting docs from Documenter to DocumenterVitepress. + +## 2. Loading the PostgreSQL Database + +The main database we worked on/built analysis was the freely available OMOPCDM Database. The Database was formatted within a PostgreSQL database with [installation instructions here](https://www.devart.com/dbforge/postgresql/how-to-install-postgresql-on-linux/) are some instructions on how to set up Postgres in a Linux machine. However, I was provided with some more extra synthetic data from my mentor for further testing of the functionalities. Being a very large database we had to strategically download it further, my mentor helped me in setting up the Postgres on my local machine. Once, the database was set up proper testing was performed on it to check if things were as expected. With this, we were done with the database setup as well and could finally dive into the actual code logic for the Pathways synthesis. + +## 3. Testing and Development setup on my local computer + +To get a proper environment for functionality creation and concurrent testing we required a proper testing setup so that we could test the new functions made at the same time. This was done using [Revise.jl](https://timholy.github.io/Revise.jl/stable/), which helps to keep Julia sessions running without frequent restarts when making changes to code. It allowed me to edit my code, update packages, or switch git branches during a session, with changes applied immediately in the next command. My mentor helped me set it up, added Revise.jl to the global Julia environment, also [PackageCompatUI](https://github.com/GunnarFarneback/PackageCompatUI.jl) that provides a terminal text interface to the [compat] section of a Julia Project.toml file, and finally made a Julia script by the name β€œstartup.jl” out of it. This script was then added to `/home/jay-sanjay/.julia/config/` path in my local computer. + +Here is the sample for the startup.jl file: + +```julia +using PackageCompatUI +using PkgTemplates +using Revise + +################################### +# HELPER FUNCTIONS +################################### +function template() + Template(; + user="jay-sanjay", + dir="~/FOSS", + authors="jaysanjay and contributors", + julia=v"1.6", + plugins=[ + ProjectFile(; version=v"0.0.1"), + Git(), + Readme(), + License(; name="MIT"), + GitHubActions(; extra_versions=["1.6", "1", "nightly"]), + TagBot(), + Codecov(), + Documenter{GitHubActions}(), + Citation(; readme = true), + RegisterAction(), + BlueStyleBadge(), + Formatter(;style = "blue") + ], + ) +end + +``` + + +## 4. Selecting Treatments of Interest + +So, as a part of this, we used the previously mentioned research paper and discussion with the mentors we came up with logic for it. The first thing to do was to determine the moment in time from which selected treatments of interest should be included in the treatment pathway. The default is all treatments starting after the index date of the target cohort. For example, for a target cohort consisting of newly diagnosed patients, treatments after the moment of first diagnosis are included. However, it would also be desirable to include (some) treatments before the index date, for instance in case a specific disease diagnosis is only confirmed after initiating treatment. Therefore, periodPriorToIndex specifies the period (i.e. number of days) before the index date from which treatments should be included. We have created two dispatches for this function. +After that proper testing and documentation are also added. + +A basic implementation for it is: + +1. Construct a SQL query to select cohort_definition_id, subject_id, and cohort_start_date from a specified table, filtering by cohort_id. + +2. The SQL query construction and execution was done using the [FunSQL.jl](https://mechanicalrabbit.github.io/FunSQL.jl/stable/) library, in the below-shown manner: + +```julia +sql = From(tab) |> + Where(Fun.in(Get.cohort_definition_id, cohort_id...)) |> + Select(Get.cohort_definition_id, Get.subject_id, Get.cohort_start_date) |> + q -> render(q, dialect=dialect) +``` +3. Executes the constructed SQL query using a database connection, fetching the results into a data frame. + +4. If the DataFrame is not empty, convert cohort_start_date to DateTime and subtract date_prior from each date, then return the modified DataFrame. + +This was then be called this: +```julia +period_prior_to_index( + cohort_id = [1, 1, 1, 1, 1], + conn; + date_prior = Day(100), + tab=cohort + ) +``` + + +## 5. Filters Applied + +After this, we where needed to get the patient's database filtered more finely so that there are minimal variations that can be ignored. The duration of the above extracted event eras may vary a lot and it can be preferable to limit to only treatments exceeding a minimum duration. Hence, minEraDuration specifies the minimum time an event era should last to be included in the analysis. All these implementations were more of Dataframe manipulation where I used [DataFrames.jl](https://dataframes.juliadata.org/stable/) package. + +After that proper testing and documentation are also added. + +A basic implementation for the minEraDuration is: +It filters the treatment history `DataFrame` to retain only those rows where the duration between `drug_exposure_end` and `drug_exposure_start` is at least `minEraDuration`. +This function can be used as follows: +```julia +#| eval: false + +calculate_era_duration(test_df, 920000) + +#= ... =# + +4Γ—3 DataFrame + Row β”‚ person_id drug_exposure_start drug_exposure_end + β”‚ Int64 Float64 Int64 +─────┼─────────────────────────────────────────────────── + 1 β”‚ 1 -3.7273e8 -364953600 + 2 β”‚ 1 2.90304e7 31449600 + 3 β”‚ 1 -8.18208e7 -80006400 + 4 β”‚ 1 1.32918e9 1330387200 +``` + + +Another filter we worked on is the EraCollapse. So, let's suppose a case where an individual receives the same treatment for a long period +of time (e.g. need for chronic treatment). Then it's highly likely that the person would require refills. Now as patients are not 100% adherent, there might be a gap between two subsequent event eras. Usually, these eras are still considered as one treatment episode, and the eraCollapseSize deals with the maximum gap within which two eras of the same event cohort would be collapsed into one era (i.e. seen as a continuous treatment instead of a stop and re-initiation of the same treatment). +After that proper testing and documentation are also added. + +A basic implementation for the eraCollapseSize is: +(a) Sorts the data frame by event_start_date and event_end_date. +(b) Calculates the gap between each era and the previous era. +(c) Filters out rows with gap_same > eraCollapseSize. + +These functions can be used as follows: +```julia +#| eval: false + +#= ... =# + +EraCollapse(treatment_history = test_df, eraCollapseSize = 400000000) +4Γ—4 DataFrame + Row β”‚ person_id drug_exposure_start drug_exposure_end gap_same + β”‚ Int64 Float64 Int64 Float64 +─────┼─────────────────────────────────────────────────────────────── + 1 β”‚ 1 -5.33347e8 -532483200 -1.86373e9 + 2 β”‚ 1 -3.7273e8 -364953600 1.59754e8 + 3 β”‚ 1 -8.18208e7 -80006400 2.83133e8 + 4 β”‚ 1 2.90304e7 31449600 1.09037e8 +``` + + +## 6. Treatment History of the Patients + +The `create_treatment_history` function constructs a detailed treatment history for patients in a target cohort by processing and filtering event cohort data from a given DataFrame. It begins by isolating the target cohort based on its `cohort_id`, adding a new column for the `index_year` derived from the cohort’s start date. Then, it selects relevant event cohorts based on a provided list of cohort IDs and merges them with the target cohort on the `subject_id` to associate events with individuals in the target group. The function applies different filtering criteria depending on whether the user is interested in treatments starting or ending within a specified period before the target cohort's start date (defined by `periodPriorToIndex`). It keeps only the event cohorts that match the filtering condition, ensuring that only relevant treatments are considered. After filtering, the function calculates time gaps between consecutive cohort events for each patient, adding these gaps to the DataFrame. The final DataFrame provides a history of treatments, including the dates of events and the time intervals between them, offering a clear timeline of treatment for each patient. After that proper testing and documentation are also added. + + +## 7. CombinationWindow Functionality To Combine Overlapping Treatments + +Now once we have the filtering of the treatments done, we need to combine the overlapping treatments based on some set of rules. The combinationWindow specifies the time that two event eras need to overlap to be considered a combination treatment. If there are more than two overlapping event eras, we sequentially combine treatments, starting from the first two overlapping event eras. + +The `combination_Window` function processes a patient's treatment history by identifying overlapping treatment events and combining them into continuous treatment periods based on certain rules. It first converts `event_cohort_id` into strings and sorts the treatment data by `person_id`, `event_start_date`, and `event_end_date`. The helper function `selectRowsCombinationWindow` calculates gaps between consecutive treatments, marking rows where treatments overlap or occur too closely. In the main loop, the function checks these overlaps and gaps against a specified `combinationWindow`. If treatments overlap (or nearly overlap), the function adjusts the treatment periods by either merging adjacent rows or splitting rows to create continuous treatment periods. The process continues until all overlapping treatments are combined into one, creating an updated and accurate treatment history. The function ensures the final output reflects realistic treatment windows by handling special cases where gaps between treatments are smaller than the treatment durations themselves. + +It mainly covers the three cases mentioned in the R-research paper: + +### Switch Case: + +*Condition*: If the gap between the two treatment events is smaller than the combinationWindow, but the gap is not equal to the duration of either event. +*Action*: The event_end_date of the previous treatment is set to the event_start_date of the current treatment. This effectively "shifts" the previous treatment’s end date to eliminate the gap, merging the treatments into one continuous period. +*Purpose*: This ensures that treatment gaps that are too small (less than combinationWindow) are treated as part of the same treatment window. + +```julia +#| eval: false + +#= ... =# + +if -gap_previous < combinationWindow && !(-gap_previous in [duration_era, prev_duration_era]) + treatment_history[i-1, :event_end_date] = treatment_history[i, :event_start_date] +``` +Here is the pictorial representation for the same: +![](./case1.png) + +### FRFS (First Row, First Shortened): + +*Condition*: If the gap is larger than or equal to the combinationWindow, or the gap equals the duration of one of the two treatments, and the first treatment ends before or on the same date as the second treatment. +*Action*: A new row is created where the second treatment’s event_end_date is set to the end date of the first treatment. This preserves the overlap but ensures that the earlier treatment period stays intact. +*Purpose*: This prevents unnecessary truncation of the first treatment if it spans the entire overlap window. + +```julia +#| eval: false + +#= ... =# + +elseif -gap_previous >= combinationWindow || -gap_previous in [duration_era, prev_duration_era] + if treatment_history[i-1, :event_end_date] <= treatment_history[i, :event_end_date] + new_row = deepcopy(treatment_history[i, :]) + new_row.event_end_date = treatment_history[i-1, :event_end_date] + append!(treatment_history, DataFrame(new_row')) +``` +Here is the pictorial representation for the same: +![](./case2.png) + +### LRFS (Last Row, First Shortened): + +*Condition*: If the gap is larger than or equal to the combinationWindow, or the gap equals the duration of one of the treatments, and the first treatment ends after the second treatment. +*Action*: The current treatment’s event_end_date is adjusted to match the event_end_date of the previous treatment. +*Purpose*: This handles cases where the second treatment's window should be shortened to prevent overlap with the previous treatment, merging them into a single continuous window. + +```julia +#| eval: false + +#= ... =# + +else + treatment_history[i, :event_end_date] = treatment_history[i-1, :event_end_date] +``` +Here is the pictorial representation for the same: +![](./case3.png) + + +> *Note:* However, There are a few things left to cover here, most of which are the documentation and writing the test suite for the same. + +# Contributions Beyond Coding + +## 1. Organizing Meetings and Communication + +Throughout the project, I regularly met with my mentor, [Jacob Zelko], and co-mentor, [Mounika], via weekly Zoom calls to discuss progress and seek guidance. During these meetings, we reviewed my work, identified areas where I needed help, and set clear goals for the upcoming weeks. We used Trello to organize and track these goals, ensuring that nothing was overlooked. My mentors provided detailed insights into specific technical aspects and guided me through the logic behind various functions. Outside of our scheduled meetings, they were always available for quick queries via Slack, ensuring constant support. + +## 2. Personal Documentation + +In addition to the notes from our meetings, I maintained personal documentation where I recorded every step I took, including the challenges I faced and the mistakes I made. This helped me reflect on my progress and stay organized throughout the fellowship. Following my selection for GSoC 2024, I also published a blog post on [Medium](https://medium.com/@landgejay124/gsoc-24-the-julia-language-62b809bbec49) to share my journey and experiences with the Julia Language community. + +## 3. Contributions To the Rest of the JuliaHealth Repositories + +Earlier I have contributed a lot to the [OMOPCDMCohortCreator.jl](https://github.com/JuliaHealth/OMOPCDMCohortCreator.jl) including adding new functionalities writing test suites, adding blogs including - [Patient Pathways within JuliaHealth](https://github.com/JuliaHealth/juliahealth.github.io/pull/124). Apart from that I also initiated 3 new releases of this package. + +# Conclusions and Future Development + +This project was a 350-hour large project since there were many goals to accomplish. Here is what we accomplished: + +1. Built a new repository in JuliaHealth land dedicated especially to treatment pathways synthesis. + +2. CI/CD for the Package and Documentation hosting. + +3. All required basic functionalities required to build the pathways. + +4. Documentation and test suites added for each. + +Future work would include: + +- Finish this [PR](https://github.com/JuliaHealth/OMOPCDMPathways.jl/pull/63) test-suites and documentation are remaining for this PR. + +- Apart from that, we would need to add a [function](https://github.com/JuliaHealth/OMOPCDMPathways.jl/issues/9) that sews up all the functionalities of the package so that the user can run the complete pathways analysis by running just one function instead of running each of the functions manually. + +- Also, in the future, we would explore what statistical functionalities we would need to further explore pathways. + +- Then, we could explore how to compose JuliaHealth with packages from ecosystems like [JuliaStats](https://juliastats.org/) and [JuliaDSP](https://docs.juliadsp.org/stable/contents/) (for time series analysis) that are mentioned in this [PR](https://github.com/JuliaHealth/OMOPCDMPathways.jl/issues/8). + +- And finally work on creating novel visualizations for the Pathways. Commonly used visualizations for treatment pathways (such as sunburst or icicle plots) showing which patients fall under what treatment pathways could be developed as plotting recipes to visualize various aspects of a patient’s care pathway rapidly. + +# Acknowledgements πŸ™‡β€β™‚οΈ + +1. [Jacob S. Zelko](https://jacobzelko.com): aka, [TheCedarPrince](https://github.com/TheCedarPrince) + +2. [Mounika Thakkallapally](https://www.linkedin.com/in/mounika-thakkallapally/) + +Thank you for your continuous help and support throughout the fellowship. +_Note: This blog post was also written with the assistance of LLM technologies to help with grammar, flow, and spelling!_ + + diff --git a/blog/posts/jay-gsoc/image.png b/blog/posts/jay-gsoc/image.png new file mode 100644 index 0000000000000000000000000000000000000000..044588b1a1e398b8ecfa5822076b807a4eee2c29 GIT binary patch literal 198691 zcmce8gKN=*;!ZGyB8> zXSRZ3iZ8$)w-@?;;5DwDu&SMcm7$%Zu8jelk)@S|0j=#<8v_GNTVpG`gQtyr;3C?` zi-c?pbnQ&6EZ-@bSQx-58r#2PV11`x@a-KF0~5rT!x1V+K}qQ83KZs&F6n!|9YJsKH`G#T-gp;&`&(aezRzIuDm3@^`8&; ziOUpj!To<9Z~wg(_CGfxBkv~u_kF=7e&UkhLjU=mU$3KLNqzt4gC7{hP?7$7siIyu z0s4P0&1oa$B>nHDa{m8ob?6cQ*Izig5E9|!>OA~c;3*F;ub70yYFn-XDJd!D>_l_( zkDG%U#Ejvt0WIifDJj~rhiF=L*$Q#2_ZjINm~Z6F@rC7bAVktpL$a8{#N^~YUpqs; zvcv^gK+p*!6QKL04QvVhYik?m*w~#V7!+N-z0|Da+S=N96UCuWD0gmdE&-=Q-@bZ0 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zhtIA$Ur9;w`eYyi!}dOMlBP|4v@xS79-xcZrhD07g;xs*PTU`O5BqMwv^!^?ycDGw zf!%`8YUh9r% zBKOAF5D0|v%mOuGrY*-8^FSdJ*J?7G0^r{%GGkxx-!RWCb3@hwv&C>-_EDjO0OvlB zJpNmO=Bjw&6#l#Co_l=WjqELd|7YPy;n%V{`#|_L`~Nt6q3#zK7yC{C58rh`w(ra% zk390oF4ng-+Sy9BzmBfqKh$m8<|_Tm55e|hAQM;>|Pk;i`yvYqvx)qi>9k;mtY!R$kS zzap}a|H&3H48N8us`%d&ZOlhj^T^|igZzHXBab}t$m9P4c?UO-FABEWYAgSdYhU!p zetyq{6k&$-*^2p=U;j?u1 z^ZW6Oja>7S%drmwuLaINFc{}P7m>^p%>Q2G=RS`-^2p<}Mc%Hijyx&NBab}(8xgjB z|Gz}`^Z0bgb>`)A>cik`gRa>p@QZ`}l05!9z%}t%|CdJ|dF1imiflIVzx}^F^2j5P oFEX;(t If you want to learn more about me, you can connect with me on [**LinkedIn**](https://www.linkedin.com/in/divyansh-goyal-34654b200/) and follow me on [**GitHub**](https://github.com/divital-coder) + +# Background + +## What is MedEye3d.jl? + +[MedEye3D.jl](https::/github.com/Juliahealth/MedEye3d.jl) is a package under the Julia language ecosystem designed to facilitate the visualization and annotation of medical images. Tailored specifically for medical applications, it offers a range of functionalities to enhance the interpretation and analysis of medical images. MedEye3D aims to provide an essential tool for 3D medical imaging workflow within Julia. The underlying combination of [Rocket.jl](https://github.com/ReactiveBayes/Rocket.jl) and [ModernGL.jl](https://github.com/JuliaGL/ModernGL.jl) ensures the high-performance robust visualizations that the package has to offer. + +MedEye3d.jl is open-source and comes with an intuitive user interface (To learn more about MedEye3d, you can read the paper introducing it [here](https://doi.org/10.26348/znwwsi.25.57) [@Mitura2021]). + +## What features does this project encompass? + +This project covers implementation of several tasks that will enable the establishment of additional important functionalities within the MedEye3D package, facilitating enhancements within the visualization’s windowing for MRI and PET data, support for super voxels (sv), improved load times, high-level functionality implementation and robust viewing for multiple images. + +# Project Goals + +The goals outlined by Dr. Jakub Mitura (my project mentor) and I, beginning of this summer were: + +1. Migration of package reliance from [Rocket.jl](https://github.com/reactivebayes/Rocket.jl) to base Julia channel and macros: The first decision that was made was to fix the issue of screen tearing and flicker, resulting from the Rocket.jl's actor-subscription mechanism present at the core of MedEye3d.jl's event-driven programming. Here, Julia's threadsafe and asynchronous [channels](https://docs.julialang.org/en/v1/manual/asynchronous-programming/) provided a way to introduce reactive programming and state management within MedEye3d without the tradeoffs resulting from external packages such as Rocket + +2. Implementation of high level functions with simplified basic usage: Prior to this, MedEye3d involved initialization of data, texture specifications and text display for a final visualization. To reduce complexity, methods to abstract such chores were devised and implemented which resulted in the exposure of functions for loading images, accessing display data and modification of display data. This also encompassed the loading of images via [MedImages.jl](https://github.com/juliahealth/MedImages.jl) which required prior work for the integration of C++ [ITK](https://github.com/InsightSoftwareConsortium/ITK) backend for image I/O. + +3. Improved precompilation with decreased outputs to reduce start time + +4. Automatic windowing for most common MRI and PET modalities: This task is a step in the direction of maintaining consistent visualizations across MRI and PET’s most common modalities, to mimic images similar to what is displayed within [3dSlicer](https://www.slicer.org/) for the same. + +5. Adding support for multi-image viewing with crosshair marker for image registration + +6. Adding support for the display of [SuperVoxels](https://doi.org/10.1016/j.cagd.2022.102080) sv with borders within the image slices to better understand anatomical regions within slices: Supervoxels, described either through indicator masks or meshes, encapsulate regions of interest with distinct image characteristics. + +Additionally, we had a few stretch goals which are going to be a work in progress: + +1. Visualization of structures by 3D rendering using OpenGL, + +2. Support for MedVoxelHD visualization by voxel-based Hausdorff distance computation. + +3. Support for OSX users + +# Tasks + +## 1. Migration of package from Rocket to Julia's Base.Channel + +Initially, there was significant screen-tearing evident from the pixelated display of the rendered text and main image which, furthermore exhibited flickering upon scrolling through the slices in the relevant displayed image's planar views i.e (Transversal, Coronal and Saggital). Troubleshooting along the way, we narrowed down the issue within the Rocket's actor-subscription mechanism and decided to integrate Julia's Base.Channel within [MedEye3d.jl](https://github.com/Juliahealth/MedEye3d.jl) for handling the event and state management routine. Julia has asynchronous, threadsafe [channels](https://docs.julialang.org/en/v1/manual/asynchronous-programming/#Communicating-with-Channels) which facilitate in asynchronous programming with the help of a producer-consumer mechanism. An example usage of Base.Channel is as follows: + +```julia +function consumer(channel::Base.Channel) + while(true) + channelData::String = take!(channel) + println("Channel got " * channelData) + end +end + +newChannel = Base.Channel(100) + +@async consumer(newChannel) +put!(newChannel, "apples") +``` + +Julia’s multiple dispatch made for the architectural setup of MedEye3d, facilitated fixing the issue of screen tearing. Below is how the `on_next!` function, invokes different reactive components based on the types of arguments it is dealing with. + +> Dump data in channel -> fetch data from the channel in an event loop -> invoke `on_next!(state, channelData)` -> invoke relevant functionality based on the type of arguments passed + +![](./multiple_dispatch_code.png) + +The end result was a visualizer with a seamless display of a CT image without any pixelating artifacts. + +![](./fixed_screen_tear.png) + +## 2. Implementation of high level functions with simplified basic usage + +Implementing a bare-bones image visualization required a lot of function calls and definitions, in order to execute the following phases: + +1. Rendering an image-plane with OpenGL + +2. Loading data slices from the image + +3. Creating texture specifications for modalities + +4. Producing the final segmentation display + +In order to simplify basic usage, high-level abstractions were put in place with the help of [MedImages.jl](https://github.com/MedImages.jl) (under ongoing development) library to load images in the form of MedImage objects to formulate a single display function for the user. Further simplifications were made to accommodate options for the user to manipulate the imaging data that is displayed currently in the visualizer i.e retrieval of voxel arrays and their modification. Taking this in mind, the following relevant functions were exposed: + +```julia +MedEye3d.SegmentationDisplay.displayImage() +``` + +```julia +MedEye3d.DisplayDataManag.getDisplayedData() +``` + +```julia +MedEye3d.DisplayDataManag.setDisplayedData() +``` + +Putting all of the above functions to use together, we can launch the visualizer, retrieve the displayed voxel data and modify it to our liking. A sample script to achieve the former, is highlighted below: + +```julia +using MedEye3d +ctNiftiImage = "/home/hurtbadly/Downloads/ct_soft_study.nii.gz" +medEyeStruct = MedEye3d.SegmentationDisplay.displayImage(ctNiftiImage) +displayData = MedEye3d.DisplayDataManag.getDisplayedData(medEyeStruct, [Int32(1), Int32(2)]) #passing the active texture number + +# We need to check if the return type of the displayData is a single Array{Float32,3} or a vector{Array{Float32,3}} +# Now in this case we are setting Gaussian noise over the manualModif Texture voxel layer, and the manualModif texture defaults to 2 for active number + +displayData[2][:, :, :] = randn(Float32, size(displayData[2])) +MedEye3d.DisplayDataManag.setDisplayedData(medEyeStruct, displayData) +``` + +The result of this [Gaussian noise](https://www.sfu.ca/sonic-studio-webdav/handbook/Gaussian_Noise.html) within the annotation layer, made for an outcome like the following: + +![](./gaussian_noise_annotation.png) + +## 3. Improved precompilation with decreased outputs to reduce start time + +Previously, the package's precompilation was failing in Julia v1.9 and v1.10 due to pattern matching errors arising after the usage of match macros from the [Match.jl](https://github.com/JuliaServices/Match.jl) pkg in MedEye3d's keymapping workflow between GLFW callbacks from mouse and keyboard. The relevant equivalent native conditional (if-else) statements, resolved the issue and facilitated in successful precompilation of the package. Further, only following minimal outputs were produced during precompilation: + +![](./precompilation_outputs.png) + +Changes highlighted within the following pull-request: + +[https://github.com/JuliaHealth/MedEye3d.jl/pull/12](https://github.com/JuliaHealth/MedEye3d.jl/pull/12) + +## 4. Automatic [windowing](https://youtu.be/HaL-G43kwKA) for most common MRI and PET modalities + +Windowing is a crucial aspect of medical imaging, particularly in MRI (Magnetic Resonance Imaging) and PET (Positron Emission Tomography) modalities. It enables radiologists to enhance the contrast of images, highlighting specific features and improving the overall diagnostic accuracy. Windowing involves controlling the display range of pixel values to optimize the contrast between different tissues or structures. The display range is defined by two values: the minimum (min) and maximum (max) values that contribute to the final range of pixels that are displayed. By adjusting these values, radiologists can enhance or suppress specific features in the image, facilitating a more accurate diagnosis. + +The `setTextureWindow` function utilizes a set of predefined keymap controls to simplify the windowing process. The F1-F7 keys are designated for controlling windowing in MRI and PET modalities. The keymap controls are as follows: + +* F1: Display wide window for bone (CT) or increase minimum value for PET + +* F2: Display window for soft tissues (CT) or increase minimum value for PET + +* F3: Display wide window for lung viewing (CT) or increase minimum value for PET + +* F4: Decrease minimum value for display + +* F5: Increase minimum value for display + +* F6: Decrease maximum value for display + +* F7: Increase maximum value for display + +Implementation of `setTextureWindow` Function + +The `setTextureWindow` function is designed to update the texture window settings based on the input keymap control. The function takes three arguments: + +* `activeTextur`: The current texture specification +* `stateObject`: The state data fields +* `windowControlStruct`: The window control structure containing the letter code for the keymap control + +The function performs the following steps: + +1. Checks the letter code of the keymap control and updates the minimum and maximum values of the texture specification accordingly. +2. Updates the uniforms for the texture specification using the `controlMinMaxUniformVals` function. + +```julia +function setTextureWindow(activeTextur::TextureSpec, stateObject::StateDataFields, windowControlStruct::WindowControlStruct) + activeTexturName = activeTextur.name + displayRange = activeTextur.minAndMaxValue[2] - activeTextur.minAndMaxValue[1] + activeTexturStudyType = activeTextur.studyType + if windowControlStruct.letterCode == "F1" + if activeTexturStudyType == "CT" + #Bone windowing in CT + activeTextur.minAndMaxValue = Float32.([400, 1000]) + elseif activeTexturStudyType == "PET" + activeTextur.minAndMaxValue[1] += 0.10 * displayRange #windowing for pet, in the case of PET simply increase the minimum by 20% , doing the same in f1,f2 and f3 + end + elseif windowControlStruct.letterCode == "F2" + if activeTexturStudyType == "CT" + activeTextur.minAndMaxValue = Float32.([-40, 350]) + elseif activeTexturStudyType == "PET" + activeTextur.minAndMaxValue[1] += 0.10 * displayRange + end + elseif windowControlStruct.letterCode == "F3" + if activeTexturStudyType == "CT" + activeTextur.minAndMaxValue = Float32.([-426, 1000]) + elseif activeTexturStudyType == "PET" + activeTextur.minAndMaxValue[1] += 0.10 * displayRange + end + elseif windowControlStruct.letterCode == "F4" + activeTextur.minAndMaxValue[1] -= 0.20 * displayRange + elseif windowControlStruct.letterCode == "F5" + activeTextur.minAndMaxValue[1] += 0.20 * displayRange + elseif windowControlStruct.letterCode == "F6" + activeTextur.minAndMaxValue[2] -= 0.20 * displayRange + elseif windowControlStruct.letterCode == "F7" + activeTextur.minAndMaxValue[2] += 0.20 * displayRange + elseif windowControlStruct.letterCode == "F8" + activeTextur.uniforms.maskContribution -= 0.10 + elseif windowControlStruct.letterCode == "F9" + activeTextur.uniforms.maskContribution += 0.10 + end + + stateObject.mainForDisplayObjects.listOfTextSpecifications = map(texture -> texture.name == activeTexturName ? activeTextur : texture, stateObject.mainForDisplayObjects.listOfTextSpecifications) + coontrolMinMaxUniformVals(activeTextur) +end +``` +> Bone windowing in CT + +![](./ct_windowing.png) + +> Bone windowing in PET + +![](./pet_windowing.png) + +## 5. Adding support for multi-image viewing with crosshair marker for image registration + +Following the mid-term evaluation, MedEye3d.jl underwent a significant enhancement, whereby a multi-image display capability was implemented through a series of refinements. Specifically, a novel approach was adopted, whereby separate OpenGL [fragment shaders](https://www.khronos.org/opengl/wiki/Fragment_Shader) were introduced to concurrently render images on either side of the visualizer, namely the left and right views. Prior to integrating voxel data into the fragment shaders, an initial series of tests involved evaluating individual colors to validate the integrity of the double image display. A screenshot from one of these critical testing phases is presented below: +![](./multi_fragment_shader.png) + +The shaders were further manipulated to automatically initialize for each of the images separately. Further, the reactive aspect of the visualizer in multi-image display mode was iterated upon and now, instead of a single state management struct, a vector of states was being passed around, facilitating the user to scroll each of the images separately just by simply hovering their mouse over either of the image, activating its relevant associated state struct. + +Down below, is the struct for state that handles all of the things currently related with an image: + +```julia +@with_kw mutable struct StateDataFields + currentDisplayedSlice::Int = 1 # stores information what slice number we are currently displaying + mainForDisplayObjects::forDisplayObjects = forDisplayObjects() # stores objects needed to display using OpenGL and GLFW + onScrollData::FullScrollableDat = FullScrollableDat() + textureToModifyVec::Vector{TextureSpec} = [] # texture that we want currently to modify - if list is empty it means that we do not intend to modify any texture + isSliceChanged::Bool = false # set to true when slice is changed set to false when we start interacting with this slice - thanks to this we know that when we start drawing on one slice and change the slice the line would star a new on new slice + textDispObj::ForWordsDispStruct = ForWordsDispStruct()# set of objects and constants needed for text diplay + currentlyDispDat::SingleSliceDat = SingleSliceDat() # holds the data displayed or in case of scrollable data view for accessing it + calcDimsStruct::CalcDimsStruct = CalcDimsStruct() #data for calculations of necessary constants needed to calculate window size , mouse position ... + valueForMasToSet::valueForMasToSetStruct = valueForMasToSetStruct() # value that will be used to set pixels where we would interact with mouse + lastRecordedMousePosition::CartesianIndex{3} = CartesianIndex(1, 1, 1) # last position of the mouse related to right click - usefull to know onto which slice to change when dimensions of scroll change + forUndoVector::AbstractArray = [] # holds lambda functions that when invoked will undo last operations + maxLengthOfForUndoVector::Int64 = 15 # number controls how many step at maximum we can get back + fieldKeyboardStruct::KeyboardStruct = KeyboardStruct() + displayMode::DisplayMode = SingleImage + imagePosition::Int64 = 1 + switchIndex::Int = 1 + mainRectFields::GlShaderAndBufferFields = GlShaderAndBufferFields() + crosshairFields::GlShaderAndBufferFields = GlShaderAndBufferFields() + textFields::GlShaderAndBufferFields = GlShaderAndBufferFields() + spacingsValue::Union{Vector{Tuple{Float64,Float64,Float64}},Tuple{Float64,Float64,Float64}} = [(1.0, 1.0, 1.0)] + originValue::Union{Vector{Tuple{Float64,Float64,Float64}},Tuple{Float64,Float64,Float64}} = [(1.0, 1.0, 1.0)] + supervoxelFields::GlShaderAndBufferFields = GlShaderAndBufferFields() +end +``` + +After the integrity of the fragment shaders was verified in multi-image, voxel data for the images was integrated and further modifications to the high-level functions were made and eventually the following script produced a rather appealing result. + +Script for loading the same NIFTI image twice in the visualizer for side-by-side display: + +```julia +using MedEye3d +ctNiftiImage = "/home/hurtbadly/Downloads/ct_soft_study.nii.gz" +MedEye3d.SegmentationDisplay.displayImage([[ctNiftiImage],[ctNifitImage]]) +``` +>Results in : + +![](./multi_image_ct.png) + +Crosshair marker for image registration are displayed in the relevant passive image to hightlight the same anatomical regions based on the spatial meta-data of the images i.e spacing, origin and direction. In order to achive the crosshair rendering in the passive image, the following action items were devised: + +(a) Retrieval of GLFW Mouse Callbacks for x and y position of the cursor in window coordinates (0 to window-width) from the active image + +(b) Conversion of these x and y window coordinates into their relevant active image x and y texture coordinates + +(c) Conversion of these texture coordinates into real space point with the help of spatial metadata + +(d) Conversion of the real space point into the texture coordinates of the passive image + +(e) Conversion of the passive image texture coordinates into their relevant OpenGL coordinate system values (-1 to 1) + +(f) Rendering of crosshair on OpenGL coordinate in passive image + +Conversion between different coordinate systems and accounting for the image's spatial metadata during calculating proved to be challenging at first, but with multiple revisions, a final solution was achieved with seemingly no noticeable amount of lag or delay. One such frame of [CT] images with crosshair display in multi-image is depicted below: + +![](./multi_image_ct_crosshair.png) + +>Another frame from the openGL rendering cycle, highlighting PET images with crosshair display in multi-image mode: + +![](./pet_multi_image.png) + +## 6. Adding support for the display of [SuperVoxels](https://doi.org/10.1016/j.cagd.2022.102080) sv with borders within the image slices to better understand anatomical regions within slices + +In enhancing MedEye3d's functionality, supporting super voxels (sv) with boundaries becomes paramount. The sv rendering, effectively capturing gradients, serves as the cornerstone for detecting these boundaries within both MRI and PET volumes. Supervoxels, described either through indicator masks or meshes, encapsulate regions of interest with distinct image characteristics. +By integrating boundary detection for super-voxels, MedEye3d can offer enhanced segmentation capabilities, enabling more precise delineation and analysis of anatomical structures and pathological regions within medical imaging data. + +[Supervoxels](https://www.sciencedirect.com/topics/computer-science/superpixel) are basically a collection of voxels that share similar image properties. For example: in MRI scans of the brain cortex, super voxels could represent clusters of voxels corresponding to specific anatomical regions or functional areas. The main objective of this task was to add support for the display of super voxel-based segmentation of images, followed by some janitorial tasks: + +1. Display of the borders of super-voxels (sv), extracted using the machine learning algorithms. + +2. Checking image gradient agreement with super-voxel borders. + +This initial workflow involved, the initialization of relevant buffers in OpenGL for dynamic rendering of lines over the image display, namely vertex array buffers (vao), vertex buffers (vbo) and edge buffers (ebo). Further, these buffers are updated on a scroll event, where the information from the currently displayed slice is passed to the event handler, which invokes a function that updates the vertex buffer (vbo) with new vertices pertaining to the relevant slice number and planar view, precalculated from an [HDF5](https://www.neonscience.org/resources/learning-hub/tutorials/about-hdf5) file during initialization of the visualizer. For instance, if the user is scrolling in the 3rd axis (transversal plane) and is currently on slice 40, the supervoxel display will pertain to edges specifically calculated for that specific slice in that plane. + +Eventually, with ever so increasing number of attempts and a few hurdles along the way, one of which particularly stood out since it marked our first step towards a good direction: + +> Challenges in rendering + +![](./supervoxel_rendering_issue.png) + +At last, an appealing result hit our sight. + +> Final result + +> *Note:* The image borders are intentional to emphasize the size of the visualizer which is currently defaulted to a certain width and height. + +![](./supervoxel_rendering_fixed.png) + +> *Note:* However, There are a few things left to cover here, most of which revolve around MedImages.jl and documentation for the same. List of PRs that facilitated the completion of the tasks highlighted above: + +(a) [https://github.com/JuliaHealth/MedEye3d.jl/pull/21](https://github.com/JuliaHealth/MedEye3d.jl/pull/21) + +(b) [https://github.com/JuliaHealth/MedEye3d.jl/pull/20](https://github.com/JuliaHealth/MedEye3d.jl/pull/20) + +(c) [https://github.com/JuliaHealth/MedEye3d.jl/pull/16](https://github.com/JuliaHealth/MedEye3d.jl/pull/16) + +(d) [https://github.com/JuliaHealth/MedEye3d.jl/pull/14](https://github.com/JuliaHealth/MedEye3d.jl/pull/14) + +(e) [https://github.com/JuliaHealth/MedEye3d.jl/pull/13](https://github.com/JuliaHealth/MedEye3d.jl/pull/13) + +(f) [https://github.com/JuliaHealth/MedEye3d.jl/pull/12](https://github.com/JuliaHealth/MedEye3d.jl/pull/12) + +# Contributions Beyond Coding + +## 1. Mentoring and Guidance + +I regularly organized meetings with my mentor to seek guidance on project direction and troubleshooting issues in the visualizer. This ensured that I stayed on track, received timely feedback, and addressed any challenges that arose. + +## 2. Package Documentation and Community Contribution + +I contributed to other medical imaging sub-ecosystem packages in JuliaHealth, including [MedImages.jl](https://github.com/Juliahealth/MedImages.jl) and [MedEval3D.jl](https://github.com/Juliahealth/MedEval3D.jl). Specifically, I set up documentation for these packages using DocuementerVitepress.jl. This not only enhanced the functionality of these packages but also helped maintain a coherent and organized package ecosystem. + +## 3. Multirepo Management and Collaboration + +In addition to my work on the MedEye3d visualizer, I made significant contributions to other JuliaHealth repositories, including [MedImages.jl](https://github.com/JuliaHealth/MedImages.jl) and worked over an [Insight Toolkit](https://github.com/InsightSoftwareConsortium/ITK) wrapper library [ITKIOWrapper.jl](https://github.com/JuliaHealth/ITKIOWrapper.jl) for support in image I/O down the road in MedImages.jl. I also maintained relevant documentation and ensured continuous collaboration and synchronization across these packages. + +# Conclusions and Future Development + +Within the scope of this 350-hour project, a comprehensive range of objectives were successfully addressed. Noteworthy achievements include: + +1. Fixed screen tear and flicker within the visualizer. Integration of threadsafe Julia channels. + +2. Achieved multi-image display over CT and PET modalities with crosshair rendering (Although, only one modality can be visualize at a time, i.e either CT | CT or PET | PET). + +3. Achieved supervoxel display in single image display mode. + +4. Achieved automatic windowing of MRI and PET most common modalities. + +Future work would include: + +- Support for the users on Darwin (Apple-based platforms). + +- Apart from that, we would need to add a function that dynamically allocates the texture number to the manual modification mask, regardless of the number of images passed for display, which is currently defaulted to 2. + +- Also, in the future, we would explore the stretch goals a bit more rigorously, particularly the implementation of [MedVoxelHD](https://doi.org/10.1016/j.softx.2024.101744) within MedEye3d. + +# Acknowledgements πŸ™‡β€β™‚οΈ + +1. [Jakub Mitura](https://orcid.org/0000-0003-1823-6823): aka, [Dr. Jakub Mitura](https://github.com/jakubMitura14) + +2. [Carlos Castillo Passi](https://scholar.google.com/citations?user=WzleS8YAAAAJ&hl=en): aka, [cncastillo](https://github.com/cncastillo) + +I would like to thank my mentor Dr. Jakub Mitura, for his help through out every phase of this project. The troubleshooting routines around problems would have rendered the project unsuccessful, if not for the support and guidance of my mentor throughout each part of this project. I would also like to thank Jacob Zelko, for leading the Juliahealth community with such vast expertise and leading efforts for engagement amongst the members through monthly meetings. 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znPW;81+o%*0yUi`(I#u|tytT3p#d9h88@b3+-svv)ji*{K9+^}jkp%K*EW&_32s24 z;cVTj89Q9td|qQWl>w{2zus9tdbr@wtuG;2ZQo z%WSF@5t;DnN~{n5{h)oO$-IG1!GZL`k>`UJoAum2W#qrp&uUYDHeqxtI7G+9ZMZ^Q zoZOjgH3Mdt7Duhv8au$w3p4M`G=?8Bmh_zFOamJ}WWRdI@B0SXNPfF~Z+J(f{~Pvd B If you want to learn more about me, you can connect with me here: [**LinkedIn**](https://www.linkedin.com/in/ryan-kierulf-022062201/), [**GitHub**](https://github.com/rkierulf) - -# What is KomaMRI? - -[KomaMRI](https://github.com/JuliaHealth/KomaMRI.jl) is a Julia package for efficiently simulating Magnetic Resonance Imaging (MRI) acquisitions. MRI simulation is a useful tool for researchers, as it allows testing new pulse sequences to analyze the signal output and image reconstruction quality without needing to actually take an MRI, which may be time or cost-prohibitive. - -In contrast to many other MRI simulators, KomaMRI.jl is open-source, cross-platform, and comes with an intuitive user interface (To learn more about KomaMRI, you can read the paper introducing it [here](https://onlinelibrary.wiley.com/doi/full/10.1002/mrm.29635)). However, being developed fairly recently, there are still new features that can be added and optimization to be done. - -# Project Goals - -The goals outlined by Carlos (my project mentor) and I the beginning of this summer were: - -1. Extend GPU support beyond CUDA to include AMD, Intel, and Apple Silicon GPUs, through the packages [AMDGPU.jl](https://github.com/JuliaGPU/AMDGPU.jl), [oneAPI.jl](https://github.com/JuliaGPU/oneAPI.jl), and [Metal.jl](https://github.com/JuliaGPU/Metal.jl) - -2. Create a CI pipeline to be able to test each of the GPU backends - -3. Create a new kernel-based simulation method optimized for the GPU, which we expected would outperform array broadcasting - -4. (Stretch Goal) Look into ways to support running distributed simulations across multiple nodes or GPUs - - -# Step 1: Support for Different GPU backends - -Previously, KomaMRI's support for GPU acceleration worked by converting each array used within the simulation to a `CuArray`, the device array type defined in [CUDA.jl](https://github.com/JuliaGPU/CUDA.jl). This was done through a general `gpu` function. The inner simulation code is GPU-agnostic, as the same operations can be performed on a CuArray or a plain CPU Array. This approach is good for extensibility, as it does not require writing different simulation code for the CPU / GPU, or different GPU backends, and would only work in a language like Julia based on runtime dispatch! - -To extend this to multiple GPU backends, all that is needed is to generalize the `gpu` function to convert to either the device types of CUDA.jl, AMDGPU.jl, Metal.jl, or oneAPI.jl, depending on which backend is being used. To give an idea of what the gpu conversion code looked like before, here is a snippet: - -```julia -struct KomaCUDAAdaptor end -adapt_storage(to::KomaCUDAAdaptor, x) = CUDA.cu(x) - -function gpu(x) - check_use_cuda() - return use_cuda[] ? fmap(x -> adapt(KomaCUDAAdaptor(), x), x; exclude=_isleaf) : x -end - -#CPU adaptor -struct KomaCPUAdaptor end -adapt_storage(to::KomaCPUAdaptor, x::AbstractArray) = adapt(Array, x) -adapt_storage(to::KomaCPUAdaptor, x::AbstractRange) = x - -cpu(x) = fmap(x -> adapt(KomaCPUAdaptor(), x), x) -``` - -The `fmap` function is from the package `Functors.jl` and can recursively apply a function to a struct tagged with `@functor`. The function being applied is `adapt` from `Adapt.jl`, which will call the lower-level `adapt_storage` function to actually convert to / from the device type. The second parameter to `adapt` is what is being adapted, and the first is what it is being adapted to, which in this case is a custom adapter struct `KomaCUDAAdapter`. - -One possible approach to generalize to different backends would be to define additional adapter structs for each backend and corresponding `adapt_storage` functions. This is what the popular machine learning library [Flux.jl](https://github.com/FluxML/Flux.jl) does. However, there is a simpler way! - -Each backend package (CUDA.jl, Metal.jl, etc.) already defines `adapt_storage` functions for converting different types to / from corresponding device type. Reusing these functions is preferable to defining our own since, not only does it save work, but it allows us to rely on the expertise of the developers who wrote those packages! If there is an issue with types being converted incorrectly that is fixed in one of those packages, then we would not need to update our code to get this fix since we are using the definitions they created. - -Our final `gpu` and `cpu` functions are very simple. The `backend` parameter is a type derived from the abstract `Backend` type of [`KernelAbstractions.jl`](https://github.com/JuliaGPU/KernelAbstractions.jl), which is extended by each of the backend packages: - -```julia -import KernelAbstractions as KA - -function gpu(x, backend::KA.GPU) - return fmap(x -> adapt(backend, x), x; exclude=_isleaf) -end - -cpu(x) = fmap(x -> adapt(KA.CPU(), x), x, exclude=_isleaf) -``` - -The other work needed to generalize our GPU support involved switching to use [package extensions](https://pkgdocs.julialang.org/v1/creating-packages/#Conditional-loading-of-code-in-packages-(Extensions)) to avoid having each of the backend packages as an explicit dependency, and defining some basic GPU functions for backend selection and printing information about available GPU devices. The pull request for adding support for multiple backends is linked below: - -> https://github.com/JuliaHealth/KomaMRI.jl/pull/405 - -# Step 2: Buildkite CI - -At the time the above pull request was merged, we weren't sure whether the added support for AMD and Intel GPUs actually worked, since we only had access to CUDA and Apple Silicon GPUs. So the next step was to set up a CI to test each GPU backend. To do this, we used [Buildkite](https://github.com/JuliaGPU/KernelAbstractions.jl), which is a CI platform that many other Julia packages also use. Since there were many examples to follow, setting up our testing pipeline was not too difficult. Each step of the pipeline does the required environment setup and then calls `Pkg.test()` for KomaMRICore. As an example, here is what the AMDGPU step of our pipeline looks like: - -```{yml} - - label: "AMDGPU: Run tests on v{{matrix.version}}" - matrix: - setup: - version: - - "1" - plugins: - - JuliaCI/julia#v1: - version: "{{matrix.version}}" - - JuliaCI/julia-coverage#v1: - codecov: true - dirs: - - KomaMRICore/src - - KomaMRICore/ext - command: | - julia -e 'println("--- :julia: Instantiating project") - using Pkg - Pkg.develop([ - PackageSpec(path=pwd(), subdir="KomaMRIBase"), - PackageSpec(path=pwd(), subdir="KomaMRICore"), - ])' - - julia --project=KomaMRICore/test -e 'println("--- :julia: Add AMDGPU to test environment") - using Pkg - Pkg.add("AMDGPU")' - - julia -e 'println("--- :julia: Running tests") - using Pkg - Pkg.test("KomaMRICore"; coverage=true, test_args=["AMDGPU"])' - agents: - queue: "juliagpu" - rocm: "*" - timeout_in_minutes: 60 -``` - -We also decided that in addition to a testing CI, it would also be helpful to have a benchmarking CI to track performance changes resulting from each commit to the main branch of the repository. [Lux.jl](https://github.com/LuxDL/Lux.jl) had a very nice-looking benchmarking page, so I decided to look into their approach. They were using [github-action-benchmark](https://github.com/benchmark-action/github-action-benchmark), a popular benchmarking action that integrates with the Julia package [`BenchmarkTools.jl`](https://github.com/JuliaCI/BenchmarkTools.jl). github-action-benchmark does two very useful things: - -1. Collects benchmarking data into a json file and provides a default index.html to display this data. If put inside a relative path in the gh-pages branch of a repository, this results in a public benchmarking page which is automatically updated after each commit! - -2. Comments on a pull request with the benchmarking results compared with before the pull request. Example: https://github.com/JuliaHealth/KomaMRI.jl/pull/442#pullrequestreview-2213921334 - -The only issue was that since github-action-benchmark is a github action, it is meant to be run within github by one of the available github runners. While this works for CPU benchmarking, only Buildkite has the CI setup for each of the GPU backends we are using, and Lux.jl's benchmarks page only included CPU benchmarks, not GPU benchmarks (Note: we talked with Avik, the repository owner of Lux.jl, and Lux.jl has since adopted the approach outlined below to display GPU and CPU benchmarks together). I was not able to find any examples of other julia packages using github-action-benchmark for GPU benchmarking. - -Fortunately, there is a tool someone developed to download results from Buildkite into a github action (https://github.com/EnricoMi/download-buildkite-artifact-action). This repository only had 1 star when I found it, but it does exactly what we needed: it identifies the corresponding Buildkite build for a commit, waits for it to finish, and then downloads the artifacts for the build into the github action it is being run from. With this, we were able to download the Buildkite benchmark results from a final aggregation step into our benchmarking action and upload to github-action-benchmark to publish to either the main data.js file for our benchmarking website, or pull request. - -Our final benchmarking page looks like this and is [publicly accessible](https://juliahealth.org/KomaMRI.jl/benchmarks/): - -![](./Benchmark_Page.png) - -One neat thing about github-action-benchmark is that the default index.html is extensible, so even though by deault it only shows time, the information for memory usage and number of allocations is also collected into the json file, and can be displayed as well. - -A successful CI run on Buildkite Looks like [this](https://buildkite.com/julialang/komamri-dot-jl/builds/925): - -![](./CI_Run.png) - -The pull requests for creating the CI testing and benchmarking pipeline, and changing the index.html for our benchmark page are listed below: - -1. https://github.com/JuliaHealth/KomaMRI.jl/pull/411 -2. https://github.com/JuliaHealth/KomaMRI.jl/pull/418 -3. https://github.com/JuliaHealth/KomaMRI.jl/pull/421 - -# Step 3: Optimization - -With support for multiple backends enabled, and a robust CI, the next step was to optimize our simulation code as much as possible. Our original idea was to create a new GPU-optimized simulation method, but before doing this we wanted to look more at the existing code and optimize for the CPU. - -The simulation code is solving a differential equation (the [Bloch equations(https://en.wikipedia.org/wiki/Bloch_equations)]) over time. Most differential equation solvers step through time, updating the current state at each time step, but our previous simulation code, more optimized for the GPU, did a lot of computations across all time points in a simulation block, allocating a matrix of size `Nspins by NΞ”t` each time this was done. Although this is beneficial for the GPU, where there are millions of threads available on which to parallelize these computations, for the CPU it is more important to conserve memory, and the aforementioned approach of stepping through time is preferable. - -After seeing that this approach did help speed up simulation time on the CPU, but was not faster on the GPU (7x slower for Metal!) we decided to separate our simulation code for the GPU and CPU, dispatching based on the `KernelAbstractions.Backend` type depending on if it is `<:KernelAbstractions.CPU` or `<:KernelAbstractions.GPU`. - -Other things we were able to do to speed up CPU computation time: - -1. Preallocating each array used inside the core simulation code so it can be re-used from one simulation block to the next. - -2. [Skipping an expensive computation](https://github.com/JuliaHealth/KomaMRI.jl/blob/master/KomaMRICore/src/simulation/SimMethods/Bloch/BlochCPU.jl#L90) if the magnetization at that time point is not added to the final signal - -3. Ensuring that each statement is fully broadcasted. We were surprised to see the difference between the following examples: - -```julia -#Fast -Bz = x .* seq.Gx' .+ y .* seq.Gy' .+ z .* seq.Gz' .+ p.Ξ”w ./ T(2Ο€ .* Ξ³) - -#Slow -Bz = x .* seq.Gx' .+ y .* seq.Gy' .+ z .* seq.Gz' .+ p.Ξ”w / T(2Ο€ * Ξ³) -``` - -4. Using the `cis` function for complex exponentiation, which is faster than `exp` - -With these changes, the mean improvement in simulation time aggregating across each of our benchmarks for 1, 2, 4, and 8 CPU threads was ~4.28. For 1 thread, the average improvement in memory usage was 90x! - -The next task was optimizing the simulation code for the GPU. Although our original idea was to put everything into one GPU kernel, we found that the existing broadcasting operations were already very fast, and that custom kernels we wrote were not able to outperform the previous implementation. The Julia GPU compiler team deserves a lot of credit for developing such fast broadcasting implementations! - -However, this does not mean that we were unable to improve the GPU simulation time. Similar to with the CPU, preallocation made a substantial difference. Parallelizing as much work as possible across the time points for a simulation block was also found to beneficial. For the parts that needed to be done sequentially, a [custom GPU kernel](https://github.com/JuliaHealth/KomaMRI.jl/blob/master/KomaMRICore/src/simulation/SimMethods/Bloch/KernelFunctions.jl#L5) was written which used the `KernelAbstractions.@localmem` macro for arrays being updated at each time step to yield faster memory access. - -The mean speedup we saw across the 4 supported GPU backends was 4.16, although this varied accross each backend (for example, CUDA was only 2.66x faster while oneAPI was 28x faster). There is a [remaining bottleneck](https://github.com/JuliaHealth/KomaMRI.jl/blob/master/KomaMRICore/src/simulation/SimMethods/Bloch/BlochGPU.jl#L151) in the `run_spin_preceession!` function having to do with logical indexing that I was not able to resolve, but could be solved in the future to speed up the GPU simulation time even further! - -The pull requests optimizing code for the CPU and GPU are below: - -1. https://github.com/JuliaHealth/KomaMRI.jl/pull/443 - -2. https://github.com/JuliaHealth/KomaMRI.jl/pull/459 - -3. https://github.com/JuliaHealth/KomaMRI.jl/pull/462 - -# 4. Step 4: Distributed Support - -This last step was a stretch goal for exploring how to add distributed support to KomaMRI. MRI simulations can become quite large, so it is useful to be able to distribute work across either multiple GPUs or multiple compute nodes. - -A nice thing about MRI simulation is the independent spin property: if a phantom object (representing, for example a brain tissue slice) is divided into two parts, and each part is simulated separately, the signal result from simulating the whole phantom will be equal to the sum of the signal results from simulating each subdivision of the original phantom. This makes it quite easy to distribute work, either across more than one GPU or accross multiple compute nodes. - -The following scripts worked, with the only necessary code change to the repository being a new + function to add two RawAcquisitionData structs: - -```julia -#Use multiple GPUs: -using Distributed -using CUDA - -#Add workers based on the number of available devices -addprocs(length(devices())) - -#Define inputs on each worker process -@everywhere begin - using KomaMRI, CUDA - sys = Scanner() - seq = PulseDesigner.EPI_example() - obj = brain_phantom2D() - #Divide phantom - parts = kfoldperm(length(obj), nworkers()) -end - -#Distribute simulation across workers -raw = Distributed.@distributed (+) for i=1:nworkers() - KomaMRICore.set_device!(i-1) #Sets device for this worker, note that CUDA devices are indexed from 0 - simulate(obj[parts[i]], seq, sys) -end -``` - -```julia -#Use multiple compute nodes -using Distributed -using ClusterManagers - -#Add workers based on the specified number of SLURM tasks -addprocs(SlurmManager(parse(Int, ENV["SLURM_NTASKS"]))) - -#Define inputs on each worker process -@everywhere begin - using KomaMRI - sys = Scanner() - seq = PulseDesigner.EPI_example() - obj = brain_phantom2D() - parts = kfoldperm(length(obj), nworkers()) -end - -#Distribute simulation across workers -raw = Distributed.@distributed (+) for i=1:nworkers() - simulate(obj[parts[i]], seq, sys) -end -``` - -Pull reqeust for adding these examples to the KomaMRI documentation: https://github.com/JuliaHealth/KomaMRI.jl/pull/468 - -# Conclusions / Future Work - -This project was a 350-hour large project, since there were many goals to accomplish. To summarize what changed since the beginning of the project: - -1. Added support for AMDGPU.jl, Metal.jl, and oneAPI.jl GPU backends - -2. CI for automated testing and benchmarking accross each backend + [public benchmarks page](https://juliahealth.org/KomaMRI.jl/benchmarks/) - -3. Significantly faster CPU and GPU performance - -4. Demonstrated distributed support and examples added in documentation - -Future work could look at ways to further optimize the simulation code, since despite the progress made, I believe there is more work to be done! The aforementioned logical indexing issue is still not resolved, and the kernel used inside the `run_spin_excitation!` function has not been profiled in depth. KomaMRI is also looking into adding support for higher-order ODE methods, which could require more GPU kernels being written. - -# Acknowledgements - -I would like to thank my mentor, Carlos Castillo, for his help and support on this project. I would also like to thank Jakub Mitura, who attended some of our meetings to help with GPU optimization, Dilum Aluthge who helped set up our BuildKite pipeline, and Tim Besard, who answered many GPU-related questions that Carlos and I had. diff --git a/styles.css b/styles.css index 2ddf50c..c6737b1 100644 --- a/styles.css +++ b/styles.css @@ -1 +1,9 @@ /* css styles */ + +.home-page-package-card:hover { + transition: all 300ms linear; +} + +.home-page-package-card:hover { + border: 1px solid var(--bs-primary); +} \ No newline at end of file diff --git a/theme-light.scss b/theme-light.scss deleted file mode 100644 index 30f00f3..0000000 --- a/theme-light.scss +++ /dev/null @@ -1,12 +0,0 @@ -/*-- scss:defaults --*/ -// Base document colors -$julia-blue: #4063D8; -$primary: $julia_blue; -$body-bg: lighten($primary, 40%); -$body-color: darken($primary, 40%); - -// Navbar -$navbar-bg: lighten($primary, 30%); - -// Footer -$footer-bg: lighten($primary, 30%); \ No newline at end of file From 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Coming from a mathematics and data analysis background, I was initially introduced to Julia at my university lectures. Later, I delved more into the language and the JuliaHealth community - an intersection of Julia, Health Research, Data Sciences, and Informatics. Here, I met some of the great folks in JuliaHealth and I decided to take it on as a full-fledged summer project. -In this blog, I will briefly describe what my project is and what I did as a part of it. - - -1. You can find my [**GSoC project archive link**](https://summerofcode.withgoogle.com/archive/2024/projects/ZXVIYAXG) - -2. You can also find the related publication of my work on [**Zenodo**](https://zenodo.org/records/14674051) - -3. If you want to know more about me, you can connect with me on [**LinkedIn**](https://www.linkedin.com/in/jay-landge-589439260/) and follow me on [**GitHub**](https://github.com/Jay-sanjay) - - -# Background - -## What Is Observational Health Research? - -Observational Health Research refers to studies that analyze real-world data (such as patient medical claims, electronic health records, etc.) to understand patient health. These studies often encompass a vast amount of data concerning patient care. An outstanding challenge here is that these datasets can become very complex and grow large enough to require advanced computing methods to process this information. - -## What Are Patient Pathways? - -Patient pathways refer to the journey that patients with specific medical conditions undergo in terms of their treatment. This concept goes beyond simple drug uptake statistics and looks at the sequence of treatments patients receive over time, including first-line treatments and subsequent therapies. Understanding patient pathways is essential for analyzing treatment patterns, adherence to clinical guidelines, and the disbursement of drugs. -To analyze patient pathways, one would typically use real-world data from sources such as electronic health records, claims data, and registries. However, barriers such as data interoperability and resource requirements have hindered the full utilization of real-world data for this purpose. - -So to address these challenges we (the JuliaHealth organization and I) want to develop a set of tools to extract and analyze these patient pathways. These sets of tools are based on the Observational Medical Outcomes Partnership (OMOP) Common Data Model, which standardizes healthcare data to promote interoperability. - - -# Project Description - -As part of this project with JuliaHealth, I developed a new package called [**OMOPCDMPathways.jl**](https://github.com/JuliaHealth/OMOPCDMPathways.jl). This package is designed for deployment in research projects, particularly those related to health and medical data analysis. This project takes much inspiration from the paper [_TreatmentPatterns: An r package to facilitate the standardized development and analysis of treatment patterns across disease domains_](https://www.sciencedirect.com/science/article/pii/S016926072200462X?via%3Dihub) [@markus2022treatmentpatterns] and explores the implementation of some of those ideas to develop new tools within the JuliaHealth Observational Health Subecosystem for exploring patient pathways. Additional new features and approaches were added and explored within this project. Additionally, I have authored a developer guide for the package, providing instructions on its use and contribution. This project provided me with hands-on experience in developing production-level code and exposed me to open-source software development practices. I had the opportunity to work in a team, under my mentors, and ensured the integration of the package with the rest of JuliaHealth, facilitating its adoption and usability within the community. - -# Project Goals - -As a part of the development, I was majorly engaged in crafting the following functionalities: - -1. Selecting treatments of interest: The first decision that was made was to decide the time from which the desired treatments of interest should be included in the treatment pathway study. Here the [periodPriorToIndex](https://github.com/JuliaHealth/OMOPCDMPathways.jl/issues/1) specifies the period (i.e. number of days) before the index date from which treatments should be included. - -2. Find Treatment History of Patients: Create the [treatment history](https://github.com/JuliaHealth/OMOPCDMPathways.jl/issues/4) of a patient based on target, event, and exit cohorts. Then filter patient events based on the start and end dates of the target cohort. Third, Calculate the duration of treatment eras and the gap between treatments. - -3. Filters: Filter the treatment history based on the [minEraDuration](https://github.com/JuliaHealth/OMOPCDMPathways.jl/issues/5) parameter and [EraCollapse](https://github.com/JuliaHealth/OMOPCDMPathways.jl/issues/2) parameter. - -4. Create a Continuous Integration and Continuous Development pipeline for the package. - -5. Implement the combinationWindow function, which combines treatments with various overlapping strategies. - -Additionally, we had a few stretch goals which were: - -1. Composing with JuliaStats Ecosystem - -2. Novel Visualizations for Pathways - -# Tasks - -## 1. Setting Up the Package in JuliaHealth Channel - -Initially, there was no package as such for generating pathways, so I had to build it from scratch. First, I created the repository with the name [OMOPCDMPathways.jl](https://github.com/JuliaHealth/OMOPCDMPathways.jl). Once the repository was created, we needed to have a skeleton for a standard Julia repository. For this, we used the [PkgTemplates.jl](https://juliaci.github.io/PkgTemplates.jl/stable/user/) this provided a basic skeleton for the repository that included - folders for test suites, documentation, src code files, GitHub files, README and LICENSE file, TOML and citation files. All this we can further edit and modify as per our work. By default, PkgTemplate.jl uses [Documenter.jl](https://documenter.juliadocs.org/stable/) for the documentation part but as suggested and discussed with my mentor we decided to shift to [DocumenterVitepress.jl](https://luxdl.github.io/DocumenterVitepress.jl/dev/) for the documentation part. However, we still faced some deployment issues in the new documentation due to a few mistakes in the `make.jl` file, thanks to [Anshul Singhvi](https://github.com/asinghvi17) for helping fix the [Deployment issues with DocumenterVitepress](https://github.com/JuliaHealth/OMOPCDMPathways.jl/issues/15). With this, we were ready with the documentation set up and fully functional. After we had shifted to DocumenterVitepress the main task now was to host the documentation, this was done using Github-Actions, detailed steps for hosting are provided at [this](https://documenter.juliadocs.org/stable/man/hosting/#Hosting-Documentation) page. Then we added the CodeCov to our package by triggering it via a dummy function and a corresponding test case for it. Also, the CI for the package was set up with it. And, now finally the repository was ready with test coverage, CI, and documentation fully functional repository ready. Here's some snapshots of the documentation set-up: - -![](./image.png) - -> Initial documentation with Documenter.jl - -![](./img2.png) - -> New documentation using DocumenterVitepress.jl - -So, as a part of it, I created this [documentation](https://luxdl.github.io/DocumenterVitepress.jl/dev/documenter_to_vitepress_docs_example) which provides detailed steps for converting docs from Documenter to DocumenterVitepress. - -## 2. Loading the PostgreSQL Database - -The main database we worked on/built analysis was the freely available OMOPCDM Database. The Database was formatted within a PostgreSQL database with [installation instructions here](https://www.devart.com/dbforge/postgresql/how-to-install-postgresql-on-linux/) are some instructions on how to set up Postgres in a Linux machine. However, I was provided with some more extra synthetic data from my mentor for further testing of the functionalities. Being a very large database we had to strategically download it further, my mentor helped me in setting up the Postgres on my local machine. Once, the database was set up proper testing was performed on it to check if things were as expected. With this, we were done with the database setup as well and could finally dive into the actual code logic for the Pathways synthesis. - -## 3. Testing and Development setup on my local computer - -To get a proper environment for functionality creation and concurrent testing we required a proper testing setup so that we could test the new functions made at the same time. This was done using [Revise.jl](https://timholy.github.io/Revise.jl/stable/), which helps to keep Julia sessions running without frequent restarts when making changes to code. It allowed me to edit my code, update packages, or switch git branches during a session, with changes applied immediately in the next command. My mentor helped me set it up, added Revise.jl to the global Julia environment, also [PackageCompatUI](https://github.com/GunnarFarneback/PackageCompatUI.jl) that provides a terminal text interface to the [compat] section of a Julia Project.toml file, and finally made a Julia script by the name β€œstartup.jl” out of it. This script was then added to `/home/jay-sanjay/.julia/config/` path in my local computer. - -Here is the sample for the startup.jl file: - -```julia -using PackageCompatUI -using PkgTemplates -using Revise - -################################### -# HELPER FUNCTIONS -################################### -function template() - Template(; - user="jay-sanjay", - dir="~/FOSS", - authors="jaysanjay and contributors", - julia=v"1.6", - plugins=[ - ProjectFile(; version=v"0.0.1"), - Git(), - Readme(), - License(; name="MIT"), - GitHubActions(; extra_versions=["1.6", "1", "nightly"]), - TagBot(), - Codecov(), - Documenter{GitHubActions}(), - Citation(; readme = true), - RegisterAction(), - BlueStyleBadge(), - Formatter(;style = "blue") - ], - ) -end - -``` - - -## 4. Selecting Treatments of Interest - -So, as a part of this, we used the previously mentioned research paper and discussion with the mentors we came up with logic for it. The first thing to do was to determine the moment in time from which selected treatments of interest should be included in the treatment pathway. The default is all treatments starting after the index date of the target cohort. For example, for a target cohort consisting of newly diagnosed patients, treatments after the moment of first diagnosis are included. However, it would also be desirable to include (some) treatments before the index date, for instance in case a specific disease diagnosis is only confirmed after initiating treatment. Therefore, periodPriorToIndex specifies the period (i.e. number of days) before the index date from which treatments should be included. We have created two dispatches for this function. -After that proper testing and documentation are also added. - -A basic implementation for it is: - -1. Construct a SQL query to select cohort_definition_id, subject_id, and cohort_start_date from a specified table, filtering by cohort_id. - -2. The SQL query construction and execution was done using the [FunSQL.jl](https://mechanicalrabbit.github.io/FunSQL.jl/stable/) library, in the below-shown manner: - -```julia -sql = From(tab) |> - Where(Fun.in(Get.cohort_definition_id, cohort_id...)) |> - Select(Get.cohort_definition_id, Get.subject_id, Get.cohort_start_date) |> - q -> render(q, dialect=dialect) -``` -3. Executes the constructed SQL query using a database connection, fetching the results into a data frame. - -4. If the DataFrame is not empty, convert cohort_start_date to DateTime and subtract date_prior from each date, then return the modified DataFrame. - -This was then be called this: -```julia -period_prior_to_index( - cohort_id = [1, 1, 1, 1, 1], - conn; - date_prior = Day(100), - tab=cohort - ) -``` - - -## 5. Filters Applied - -After this, we where needed to get the patient's database filtered more finely so that there are minimal variations that can be ignored. The duration of the above extracted event eras may vary a lot and it can be preferable to limit to only treatments exceeding a minimum duration. Hence, minEraDuration specifies the minimum time an event era should last to be included in the analysis. All these implementations were more of Dataframe manipulation where I used [DataFrames.jl](https://dataframes.juliadata.org/stable/) package. - -After that proper testing and documentation are also added. - -A basic implementation for the minEraDuration is: -It filters the treatment history `DataFrame` to retain only those rows where the duration between `drug_exposure_end` and `drug_exposure_start` is at least `minEraDuration`. -This function can be used as follows: -```julia -#| eval: false - -calculate_era_duration(test_df, 920000) - -#= ... =# - -4Γ—3 DataFrame - Row β”‚ person_id drug_exposure_start drug_exposure_end - β”‚ Int64 Float64 Int64 -─────┼─────────────────────────────────────────────────── - 1 β”‚ 1 -3.7273e8 -364953600 - 2 β”‚ 1 2.90304e7 31449600 - 3 β”‚ 1 -8.18208e7 -80006400 - 4 β”‚ 1 1.32918e9 1330387200 -``` - - -Another filter we worked on is the EraCollapse. So, let's suppose a case where an individual receives the same treatment for a long period -of time (e.g. need for chronic treatment). Then it's highly likely that the person would require refills. Now as patients are not 100% adherent, there might be a gap between two subsequent event eras. Usually, these eras are still considered as one treatment episode, and the eraCollapseSize deals with the maximum gap within which two eras of the same event cohort would be collapsed into one era (i.e. seen as a continuous treatment instead of a stop and re-initiation of the same treatment). -After that proper testing and documentation are also added. - -A basic implementation for the eraCollapseSize is: -(a) Sorts the data frame by event_start_date and event_end_date. -(b) Calculates the gap between each era and the previous era. -(c) Filters out rows with gap_same > eraCollapseSize. - -These functions can be used as follows: -```julia -#| eval: false - -#= ... =# - -EraCollapse(treatment_history = test_df, eraCollapseSize = 400000000) -4Γ—4 DataFrame - Row β”‚ person_id drug_exposure_start drug_exposure_end gap_same - β”‚ Int64 Float64 Int64 Float64 -─────┼─────────────────────────────────────────────────────────────── - 1 β”‚ 1 -5.33347e8 -532483200 -1.86373e9 - 2 β”‚ 1 -3.7273e8 -364953600 1.59754e8 - 3 β”‚ 1 -8.18208e7 -80006400 2.83133e8 - 4 β”‚ 1 2.90304e7 31449600 1.09037e8 -``` - - -## 6. Treatment History of the Patients - -The `create_treatment_history` function constructs a detailed treatment history for patients in a target cohort by processing and filtering event cohort data from a given DataFrame. It begins by isolating the target cohort based on its `cohort_id`, adding a new column for the `index_year` derived from the cohort’s start date. Then, it selects relevant event cohorts based on a provided list of cohort IDs and merges them with the target cohort on the `subject_id` to associate events with individuals in the target group. The function applies different filtering criteria depending on whether the user is interested in treatments starting or ending within a specified period before the target cohort's start date (defined by `periodPriorToIndex`). It keeps only the event cohorts that match the filtering condition, ensuring that only relevant treatments are considered. After filtering, the function calculates time gaps between consecutive cohort events for each patient, adding these gaps to the DataFrame. The final DataFrame provides a history of treatments, including the dates of events and the time intervals between them, offering a clear timeline of treatment for each patient. After that proper testing and documentation are also added. - - -## 7. CombinationWindow Functionality To Combine Overlapping Treatments - -Now once we have the filtering of the treatments done, we need to combine the overlapping treatments based on some set of rules. The combinationWindow specifies the time that two event eras need to overlap to be considered a combination treatment. If there are more than two overlapping event eras, we sequentially combine treatments, starting from the first two overlapping event eras. - -The `combination_Window` function processes a patient's treatment history by identifying overlapping treatment events and combining them into continuous treatment periods based on certain rules. It first converts `event_cohort_id` into strings and sorts the treatment data by `person_id`, `event_start_date`, and `event_end_date`. The helper function `selectRowsCombinationWindow` calculates gaps between consecutive treatments, marking rows where treatments overlap or occur too closely. In the main loop, the function checks these overlaps and gaps against a specified `combinationWindow`. If treatments overlap (or nearly overlap), the function adjusts the treatment periods by either merging adjacent rows or splitting rows to create continuous treatment periods. The process continues until all overlapping treatments are combined into one, creating an updated and accurate treatment history. The function ensures the final output reflects realistic treatment windows by handling special cases where gaps between treatments are smaller than the treatment durations themselves. - -It mainly covers the three cases mentioned in the R-research paper: - -### Switch Case: - -*Condition*: If the gap between the two treatment events is smaller than the combinationWindow, but the gap is not equal to the duration of either event. -*Action*: The event_end_date of the previous treatment is set to the event_start_date of the current treatment. This effectively "shifts" the previous treatment’s end date to eliminate the gap, merging the treatments into one continuous period. -*Purpose*: This ensures that treatment gaps that are too small (less than combinationWindow) are treated as part of the same treatment window. - -```julia -#| eval: false - -#= ... =# - -if -gap_previous < combinationWindow && !(-gap_previous in [duration_era, prev_duration_era]) - treatment_history[i-1, :event_end_date] = treatment_history[i, :event_start_date] -``` -Here is the pictorial representation for the same: -![](./case1.png) - -### FRFS (First Row, First Shortened): - -*Condition*: If the gap is larger than or equal to the combinationWindow, or the gap equals the duration of one of the two treatments, and the first treatment ends before or on the same date as the second treatment. -*Action*: A new row is created where the second treatment’s event_end_date is set to the end date of the first treatment. This preserves the overlap but ensures that the earlier treatment period stays intact. -*Purpose*: This prevents unnecessary truncation of the first treatment if it spans the entire overlap window. - -```julia -#| eval: false - -#= ... =# - -elseif -gap_previous >= combinationWindow || -gap_previous in [duration_era, prev_duration_era] - if treatment_history[i-1, :event_end_date] <= treatment_history[i, :event_end_date] - new_row = deepcopy(treatment_history[i, :]) - new_row.event_end_date = treatment_history[i-1, :event_end_date] - append!(treatment_history, DataFrame(new_row')) -``` -Here is the pictorial representation for the same: -![](./case2.png) - -### LRFS (Last Row, First Shortened): - -*Condition*: If the gap is larger than or equal to the combinationWindow, or the gap equals the duration of one of the treatments, and the first treatment ends after the second treatment. -*Action*: The current treatment’s event_end_date is adjusted to match the event_end_date of the previous treatment. -*Purpose*: This handles cases where the second treatment's window should be shortened to prevent overlap with the previous treatment, merging them into a single continuous window. - -```julia -#| eval: false - -#= ... =# - -else - treatment_history[i, :event_end_date] = treatment_history[i-1, :event_end_date] -``` -Here is the pictorial representation for the same: -![](./case3.png) - - -> *Note:* However, There are a few things left to cover here, most of which are the documentation and writing the test suite for the same. - -# Contributions Beyond Coding - -## 1. Organizing Meetings and Communication - -Throughout the project, I regularly met with my mentor, [Jacob Zelko], and co-mentor, [Mounika], via weekly Zoom calls to discuss progress and seek guidance. During these meetings, we reviewed my work, identified areas where I needed help, and set clear goals for the upcoming weeks. We used Trello to organize and track these goals, ensuring that nothing was overlooked. My mentors provided detailed insights into specific technical aspects and guided me through the logic behind various functions. Outside of our scheduled meetings, they were always available for quick queries via Slack, ensuring constant support. - -## 2. Personal Documentation - -In addition to the notes from our meetings, I maintained personal documentation where I recorded every step I took, including the challenges I faced and the mistakes I made. This helped me reflect on my progress and stay organized throughout the fellowship. Following my selection for GSoC 2024, I also published a blog post on [Medium](https://medium.com/@landgejay124/gsoc-24-the-julia-language-62b809bbec49) to share my journey and experiences with the Julia Language community. - -## 3. Contributions To the Rest of the JuliaHealth Repositories - -Earlier I have contributed a lot to the [OMOPCDMCohortCreator.jl](https://github.com/JuliaHealth/OMOPCDMCohortCreator.jl) including adding new functionalities writing test suites, adding blogs including - [Patient Pathways within JuliaHealth](https://github.com/JuliaHealth/juliahealth.github.io/pull/124). Apart from that I also initiated 3 new releases of this package. - -# Conclusions and Future Development - -This project was a 350-hour large project since there were many goals to accomplish. Here is what we accomplished: - -1. Built a new repository in JuliaHealth land dedicated especially to treatment pathways synthesis. - -2. CI/CD for the Package and Documentation hosting. - -3. All required basic functionalities required to build the pathways. - -4. Documentation and test suites added for each. - -Future work would include: - -- Finish this [PR](https://github.com/JuliaHealth/OMOPCDMPathways.jl/pull/63) test-suites and documentation are remaining for this PR. - -- Apart from that, we would need to add a [function](https://github.com/JuliaHealth/OMOPCDMPathways.jl/issues/9) that sews up all the functionalities of the package so that the user can run the complete pathways analysis by running just one function instead of running each of the functions manually. - -- Also, in the future, we would explore what statistical functionalities we would need to further explore pathways. - -- Then, we could explore how to compose JuliaHealth with packages from ecosystems like [JuliaStats](https://juliastats.org/) and [JuliaDSP](https://docs.juliadsp.org/stable/contents/) (for time series analysis) that are mentioned in this [PR](https://github.com/JuliaHealth/OMOPCDMPathways.jl/issues/8). - -- And finally work on creating novel visualizations for the Pathways. Commonly used visualizations for treatment pathways (such as sunburst or icicle plots) showing which patients fall under what treatment pathways could be developed as plotting recipes to visualize various aspects of a patient’s care pathway rapidly. - -# Acknowledgements πŸ™‡β€β™‚οΈ - -1. [Jacob S. Zelko](https://jacobzelko.com): aka, [TheCedarPrince](https://github.com/TheCedarPrince) - -2. [Mounika Thakkallapally](https://www.linkedin.com/in/mounika-thakkallapally/) - -Thank you for your continuous help and support throughout the fellowship. -_Note: This blog post was also written with the assistance of LLM technologies to help with grammar, flow, and spelling!_ - - diff --git a/posts/jay-gsoc/image.png b/posts/jay-gsoc/image.png deleted file mode 100644 index 044588b1a1e398b8ecfa5822076b807a4eee2c29..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 198691 zcmce8gKN=*;!ZGyB8> zXSRZ3iZ8$)w-@?;;5DwDu&SMcm7$%Zu8jelk)@S|0j=#<8v_GNTVpG`gQtyr;3C?` zi-c?pbnQ&6EZ-@bSQx-58r#2PV11`x@a-KF0~5rT!x1V+K}qQ83KZs&F6n!|9YJsKH`G#T-gp;&`&(aezRzIuDm3@^`8&; ziOUpj!To<9Z~wg(_CGfxBkv~u_kF=7e&UkhLjU=mU$3KLNqzt4gC7{hP?7$7siIyu z0s4P0&1oa$B>nHDa{m8ob?6cQ*Izig5E9|!>OA~c;3*F;ub70yYFn-XDJd!D>_l_( zkDG%U#Ejvt0WIifDJj~rhiF=L*$Q#2_ZjINm~Z6F@rC7bAVktpL$a8{#N^~YUpqs; zvcv^gK+p*!6QKL04QvVhYik?m*w~#V7!+N-z0|Da+S=N96UCuWD0gmdE&-=Q-@bZ0 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zhtIA$Ur9;w`eYyi!}dOMlBP|4v@xS79-xcZrhD07g;xs*PTU`O5BqMwv^!^?ycDGw zf!%`8YUh9r% zBKOAF5D0|v%mOuGrY*-8^FSdJ*J?7G0^r{%GGkxx-!RWCb3@hwv&C>-_EDjO0OvlB zJpNmO=Bjw&6#l#Co_l=WjqELd|7YPy;n%V{`#|_L`~Nt6q3#zK7yC{C58rh`w(ra% zk390oF4ng-+Sy9BzmBfqKh$m8<|_Tm55e|hAQM;>|Pk;i`yvYqvx)qi>9k;mtY!R$kS zzap}a|H&3H48N8us`%d&ZOlhj^T^|igZzHXBab}t$m9P4c?UO-FABEWYAgSdYhU!p zetyq{6k&$-*^2p=U;j?u1 z^ZW6Oja>7S%drmwuLaINFc{}P7m>^p%>Q2G=RS`-^2p<}Mc%Hijyx&NBab}(8xgjB z|Gz}`^Z0bgb>`)A>cik`gRa>p@QZ`}l05!9z%}t%|CdJ|dF1imiflIVzx}^F^2j5P oFEX;(t If you want to learn more about me, you can connect with me on [**LinkedIn**](https://www.linkedin.com/in/divyansh-goyal-34654b200/) and follow me on [**GitHub**](https://github.com/divital-coder) - -# Background - -## What is MedEye3d.jl? - -[MedEye3D.jl](https::/github.com/Juliahealth/MedEye3d.jl) is a package under the Julia language ecosystem designed to facilitate the visualization and annotation of medical images. Tailored specifically for medical applications, it offers a range of functionalities to enhance the interpretation and analysis of medical images. MedEye3D aims to provide an essential tool for 3D medical imaging workflow within Julia. The underlying combination of [Rocket.jl](https://github.com/ReactiveBayes/Rocket.jl) and [ModernGL.jl](https://github.com/JuliaGL/ModernGL.jl) ensures the high-performance robust visualizations that the package has to offer. - -MedEye3d.jl is open-source and comes with an intuitive user interface (To learn more about MedEye3d, you can read the paper introducing it [here](https://doi.org/10.26348/znwwsi.25.57) [@Mitura2021]). - -## What features does this project encompass? - -This project covers implementation of several tasks that will enable the establishment of additional important functionalities within the MedEye3D package, facilitating enhancements within the visualization’s windowing for MRI and PET data, support for super voxels (sv), improved load times, high-level functionality implementation and robust viewing for multiple images. - -# Project Goals - -The goals outlined by Dr. Jakub Mitura (my project mentor) and I, beginning of this summer were: - -1. Migration of package reliance from [Rocket.jl](https://github.com/reactivebayes/Rocket.jl) to base Julia channel and macros: The first decision that was made was to fix the issue of screen tearing and flicker, resulting from the Rocket.jl's actor-subscription mechanism present at the core of MedEye3d.jl's event-driven programming. Here, Julia's threadsafe and asynchronous [channels](https://docs.julialang.org/en/v1/manual/asynchronous-programming/) provided a way to introduce reactive programming and state management within MedEye3d without the tradeoffs resulting from external packages such as Rocket - -2. Implementation of high level functions with simplified basic usage: Prior to this, MedEye3d involved initialization of data, texture specifications and text display for a final visualization. To reduce complexity, methods to abstract such chores were devised and implemented which resulted in the exposure of functions for loading images, accessing display data and modification of display data. This also encompassed the loading of images via [MedImages.jl](https://github.com/juliahealth/MedImages.jl) which required prior work for the integration of C++ [ITK](https://github.com/InsightSoftwareConsortium/ITK) backend for image I/O. - -3. Improved precompilation with decreased outputs to reduce start time - -4. Automatic windowing for most common MRI and PET modalities: This task is a step in the direction of maintaining consistent visualizations across MRI and PET’s most common modalities, to mimic images similar to what is displayed within [3dSlicer](https://www.slicer.org/) for the same. - -5. Adding support for multi-image viewing with crosshair marker for image registration - -6. Adding support for the display of [SuperVoxels](https://doi.org/10.1016/j.cagd.2022.102080) sv with borders within the image slices to better understand anatomical regions within slices: Supervoxels, described either through indicator masks or meshes, encapsulate regions of interest with distinct image characteristics. - -Additionally, we had a few stretch goals which are going to be a work in progress: - -1. Visualization of structures by 3D rendering using OpenGL, - -2. Support for MedVoxelHD visualization by voxel-based Hausdorff distance computation. - -3. Support for OSX users - -# Tasks - -## 1. Migration of package from Rocket to Julia's Base.Channel - -Initially, there was significant screen-tearing evident from the pixelated display of the rendered text and main image which, furthermore exhibited flickering upon scrolling through the slices in the relevant displayed image's planar views i.e (Transversal, Coronal and Saggital). Troubleshooting along the way, we narrowed down the issue within the Rocket's actor-subscription mechanism and decided to integrate Julia's Base.Channel within [MedEye3d.jl](https://github.com/Juliahealth/MedEye3d.jl) for handling the event and state management routine. Julia has asynchronous, threadsafe [channels](https://docs.julialang.org/en/v1/manual/asynchronous-programming/#Communicating-with-Channels) which facilitate in asynchronous programming with the help of a producer-consumer mechanism. An example usage of Base.Channel is as follows: - -```julia -function consumer(channel::Base.Channel) - while(true) - channelData::String = take!(channel) - println("Channel got " * channelData) - end -end - -newChannel = Base.Channel(100) - -@async consumer(newChannel) -put!(newChannel, "apples") -``` - -Julia’s multiple dispatch made for the architectural setup of MedEye3d, facilitated fixing the issue of screen tearing. Below is how the `on_next!` function, invokes different reactive components based on the types of arguments it is dealing with. - -> Dump data in channel -> fetch data from the channel in an event loop -> invoke `on_next!(state, channelData)` -> invoke relevant functionality based on the type of arguments passed - -![](./multiple_dispatch_code.png) - -The end result was a visualizer with a seamless display of a CT image without any pixelating artifacts. - -![](./fixed_screen_tear.png) - -## 2. Implementation of high level functions with simplified basic usage - -Implementing a bare-bones image visualization required a lot of function calls and definitions, in order to execute the following phases: - -1. Rendering an image-plane with OpenGL - -2. Loading data slices from the image - -3. Creating texture specifications for modalities - -4. Producing the final segmentation display - -In order to simplify basic usage, high-level abstractions were put in place with the help of [MedImages.jl](https://github.com/MedImages.jl) (under ongoing development) library to load images in the form of MedImage objects to formulate a single display function for the user. Further simplifications were made to accommodate options for the user to manipulate the imaging data that is displayed currently in the visualizer i.e retrieval of voxel arrays and their modification. Taking this in mind, the following relevant functions were exposed: - -```julia -MedEye3d.SegmentationDisplay.displayImage() -``` - -```julia -MedEye3d.DisplayDataManag.getDisplayedData() -``` - -```julia -MedEye3d.DisplayDataManag.setDisplayedData() -``` - -Putting all of the above functions to use together, we can launch the visualizer, retrieve the displayed voxel data and modify it to our liking. A sample script to achieve the former, is highlighted below: - -```julia -using MedEye3d -ctNiftiImage = "/home/hurtbadly/Downloads/ct_soft_study.nii.gz" -medEyeStruct = MedEye3d.SegmentationDisplay.displayImage(ctNiftiImage) -displayData = MedEye3d.DisplayDataManag.getDisplayedData(medEyeStruct, [Int32(1), Int32(2)]) #passing the active texture number - -# We need to check if the return type of the displayData is a single Array{Float32,3} or a vector{Array{Float32,3}} -# Now in this case we are setting Gaussian noise over the manualModif Texture voxel layer, and the manualModif texture defaults to 2 for active number - -displayData[2][:, :, :] = randn(Float32, size(displayData[2])) -MedEye3d.DisplayDataManag.setDisplayedData(medEyeStruct, displayData) -``` - -The result of this [Gaussian noise](https://www.sfu.ca/sonic-studio-webdav/handbook/Gaussian_Noise.html) within the annotation layer, made for an outcome like the following: - -![](./gaussian_noise_annotation.png) - -## 3. Improved precompilation with decreased outputs to reduce start time - -Previously, the package's precompilation was failing in Julia v1.9 and v1.10 due to pattern matching errors arising after the usage of match macros from the [Match.jl](https://github.com/JuliaServices/Match.jl) pkg in MedEye3d's keymapping workflow between GLFW callbacks from mouse and keyboard. The relevant equivalent native conditional (if-else) statements, resolved the issue and facilitated in successful precompilation of the package. Further, only following minimal outputs were produced during precompilation: - -![](./precompilation_outputs.png) - -Changes highlighted within the following pull-request: - -[https://github.com/JuliaHealth/MedEye3d.jl/pull/12](https://github.com/JuliaHealth/MedEye3d.jl/pull/12) - -## 4. Automatic [windowing](https://youtu.be/HaL-G43kwKA) for most common MRI and PET modalities - -Windowing is a crucial aspect of medical imaging, particularly in MRI (Magnetic Resonance Imaging) and PET (Positron Emission Tomography) modalities. It enables radiologists to enhance the contrast of images, highlighting specific features and improving the overall diagnostic accuracy. Windowing involves controlling the display range of pixel values to optimize the contrast between different tissues or structures. The display range is defined by two values: the minimum (min) and maximum (max) values that contribute to the final range of pixels that are displayed. By adjusting these values, radiologists can enhance or suppress specific features in the image, facilitating a more accurate diagnosis. - -The `setTextureWindow` function utilizes a set of predefined keymap controls to simplify the windowing process. The F1-F7 keys are designated for controlling windowing in MRI and PET modalities. The keymap controls are as follows: - -* F1: Display wide window for bone (CT) or increase minimum value for PET - -* F2: Display window for soft tissues (CT) or increase minimum value for PET - -* F3: Display wide window for lung viewing (CT) or increase minimum value for PET - -* F4: Decrease minimum value for display - -* F5: Increase minimum value for display - -* F6: Decrease maximum value for display - -* F7: Increase maximum value for display - -Implementation of `setTextureWindow` Function - -The `setTextureWindow` function is designed to update the texture window settings based on the input keymap control. The function takes three arguments: - -* `activeTextur`: The current texture specification -* `stateObject`: The state data fields -* `windowControlStruct`: The window control structure containing the letter code for the keymap control - -The function performs the following steps: - -1. Checks the letter code of the keymap control and updates the minimum and maximum values of the texture specification accordingly. -2. Updates the uniforms for the texture specification using the `controlMinMaxUniformVals` function. - -```julia -function setTextureWindow(activeTextur::TextureSpec, stateObject::StateDataFields, windowControlStruct::WindowControlStruct) - activeTexturName = activeTextur.name - displayRange = activeTextur.minAndMaxValue[2] - activeTextur.minAndMaxValue[1] - activeTexturStudyType = activeTextur.studyType - if windowControlStruct.letterCode == "F1" - if activeTexturStudyType == "CT" - #Bone windowing in CT - activeTextur.minAndMaxValue = Float32.([400, 1000]) - elseif activeTexturStudyType == "PET" - activeTextur.minAndMaxValue[1] += 0.10 * displayRange #windowing for pet, in the case of PET simply increase the minimum by 20% , doing the same in f1,f2 and f3 - end - elseif windowControlStruct.letterCode == "F2" - if activeTexturStudyType == "CT" - activeTextur.minAndMaxValue = Float32.([-40, 350]) - elseif activeTexturStudyType == "PET" - activeTextur.minAndMaxValue[1] += 0.10 * displayRange - end - elseif windowControlStruct.letterCode == "F3" - if activeTexturStudyType == "CT" - activeTextur.minAndMaxValue = Float32.([-426, 1000]) - elseif activeTexturStudyType == "PET" - activeTextur.minAndMaxValue[1] += 0.10 * displayRange - end - elseif windowControlStruct.letterCode == "F4" - activeTextur.minAndMaxValue[1] -= 0.20 * displayRange - elseif windowControlStruct.letterCode == "F5" - activeTextur.minAndMaxValue[1] += 0.20 * displayRange - elseif windowControlStruct.letterCode == "F6" - activeTextur.minAndMaxValue[2] -= 0.20 * displayRange - elseif windowControlStruct.letterCode == "F7" - activeTextur.minAndMaxValue[2] += 0.20 * displayRange - elseif windowControlStruct.letterCode == "F8" - activeTextur.uniforms.maskContribution -= 0.10 - elseif windowControlStruct.letterCode == "F9" - activeTextur.uniforms.maskContribution += 0.10 - end - - stateObject.mainForDisplayObjects.listOfTextSpecifications = map(texture -> texture.name == activeTexturName ? activeTextur : texture, stateObject.mainForDisplayObjects.listOfTextSpecifications) - coontrolMinMaxUniformVals(activeTextur) -end -``` -> Bone windowing in CT - -![](./ct_windowing.png) - -> Bone windowing in PET - -![](./pet_windowing.png) - -## 5. Adding support for multi-image viewing with crosshair marker for image registration - -Following the mid-term evaluation, MedEye3d.jl underwent a significant enhancement, whereby a multi-image display capability was implemented through a series of refinements. Specifically, a novel approach was adopted, whereby separate OpenGL [fragment shaders](https://www.khronos.org/opengl/wiki/Fragment_Shader) were introduced to concurrently render images on either side of the visualizer, namely the left and right views. Prior to integrating voxel data into the fragment shaders, an initial series of tests involved evaluating individual colors to validate the integrity of the double image display. A screenshot from one of these critical testing phases is presented below: -![](./multi_fragment_shader.png) - -The shaders were further manipulated to automatically initialize for each of the images separately. Further, the reactive aspect of the visualizer in multi-image display mode was iterated upon and now, instead of a single state management struct, a vector of states was being passed around, facilitating the user to scroll each of the images separately just by simply hovering their mouse over either of the image, activating its relevant associated state struct. - -Down below, is the struct for state that handles all of the things currently related with an image: - -```julia -@with_kw mutable struct StateDataFields - currentDisplayedSlice::Int = 1 # stores information what slice number we are currently displaying - mainForDisplayObjects::forDisplayObjects = forDisplayObjects() # stores objects needed to display using OpenGL and GLFW - onScrollData::FullScrollableDat = FullScrollableDat() - textureToModifyVec::Vector{TextureSpec} = [] # texture that we want currently to modify - if list is empty it means that we do not intend to modify any texture - isSliceChanged::Bool = false # set to true when slice is changed set to false when we start interacting with this slice - thanks to this we know that when we start drawing on one slice and change the slice the line would star a new on new slice - textDispObj::ForWordsDispStruct = ForWordsDispStruct()# set of objects and constants needed for text diplay - currentlyDispDat::SingleSliceDat = SingleSliceDat() # holds the data displayed or in case of scrollable data view for accessing it - calcDimsStruct::CalcDimsStruct = CalcDimsStruct() #data for calculations of necessary constants needed to calculate window size , mouse position ... - valueForMasToSet::valueForMasToSetStruct = valueForMasToSetStruct() # value that will be used to set pixels where we would interact with mouse - lastRecordedMousePosition::CartesianIndex{3} = CartesianIndex(1, 1, 1) # last position of the mouse related to right click - usefull to know onto which slice to change when dimensions of scroll change - forUndoVector::AbstractArray = [] # holds lambda functions that when invoked will undo last operations - maxLengthOfForUndoVector::Int64 = 15 # number controls how many step at maximum we can get back - fieldKeyboardStruct::KeyboardStruct = KeyboardStruct() - displayMode::DisplayMode = SingleImage - imagePosition::Int64 = 1 - switchIndex::Int = 1 - mainRectFields::GlShaderAndBufferFields = GlShaderAndBufferFields() - crosshairFields::GlShaderAndBufferFields = GlShaderAndBufferFields() - textFields::GlShaderAndBufferFields = GlShaderAndBufferFields() - spacingsValue::Union{Vector{Tuple{Float64,Float64,Float64}},Tuple{Float64,Float64,Float64}} = [(1.0, 1.0, 1.0)] - originValue::Union{Vector{Tuple{Float64,Float64,Float64}},Tuple{Float64,Float64,Float64}} = [(1.0, 1.0, 1.0)] - supervoxelFields::GlShaderAndBufferFields = GlShaderAndBufferFields() -end -``` - -After the integrity of the fragment shaders was verified in multi-image, voxel data for the images was integrated and further modifications to the high-level functions were made and eventually the following script produced a rather appealing result. - -Script for loading the same NIFTI image twice in the visualizer for side-by-side display: - -```julia -using MedEye3d -ctNiftiImage = "/home/hurtbadly/Downloads/ct_soft_study.nii.gz" -MedEye3d.SegmentationDisplay.displayImage([[ctNiftiImage],[ctNifitImage]]) -``` ->Results in : - -![](./multi_image_ct.png) - -Crosshair marker for image registration are displayed in the relevant passive image to hightlight the same anatomical regions based on the spatial meta-data of the images i.e spacing, origin and direction. In order to achive the crosshair rendering in the passive image, the following action items were devised: - -(a) Retrieval of GLFW Mouse Callbacks for x and y position of the cursor in window coordinates (0 to window-width) from the active image - -(b) Conversion of these x and y window coordinates into their relevant active image x and y texture coordinates - -(c) Conversion of these texture coordinates into real space point with the help of spatial metadata - -(d) Conversion of the real space point into the texture coordinates of the passive image - -(e) Conversion of the passive image texture coordinates into their relevant OpenGL coordinate system values (-1 to 1) - -(f) Rendering of crosshair on OpenGL coordinate in passive image - -Conversion between different coordinate systems and accounting for the image's spatial metadata during calculating proved to be challenging at first, but with multiple revisions, a final solution was achieved with seemingly no noticeable amount of lag or delay. One such frame of [CT] images with crosshair display in multi-image is depicted below: - -![](./multi_image_ct_crosshair.png) - ->Another frame from the openGL rendering cycle, highlighting PET images with crosshair display in multi-image mode: - -![](./pet_multi_image.png) - -## 6. Adding support for the display of [SuperVoxels](https://doi.org/10.1016/j.cagd.2022.102080) sv with borders within the image slices to better understand anatomical regions within slices - -In enhancing MedEye3d's functionality, supporting super voxels (sv) with boundaries becomes paramount. The sv rendering, effectively capturing gradients, serves as the cornerstone for detecting these boundaries within both MRI and PET volumes. Supervoxels, described either through indicator masks or meshes, encapsulate regions of interest with distinct image characteristics. -By integrating boundary detection for super-voxels, MedEye3d can offer enhanced segmentation capabilities, enabling more precise delineation and analysis of anatomical structures and pathological regions within medical imaging data. - -[Supervoxels](https://www.sciencedirect.com/topics/computer-science/superpixel) are basically a collection of voxels that share similar image properties. For example: in MRI scans of the brain cortex, super voxels could represent clusters of voxels corresponding to specific anatomical regions or functional areas. The main objective of this task was to add support for the display of super voxel-based segmentation of images, followed by some janitorial tasks: - -1. Display of the borders of super-voxels (sv), extracted using the machine learning algorithms. - -2. Checking image gradient agreement with super-voxel borders. - -This initial workflow involved, the initialization of relevant buffers in OpenGL for dynamic rendering of lines over the image display, namely vertex array buffers (vao), vertex buffers (vbo) and edge buffers (ebo). Further, these buffers are updated on a scroll event, where the information from the currently displayed slice is passed to the event handler, which invokes a function that updates the vertex buffer (vbo) with new vertices pertaining to the relevant slice number and planar view, precalculated from an [HDF5](https://www.neonscience.org/resources/learning-hub/tutorials/about-hdf5) file during initialization of the visualizer. For instance, if the user is scrolling in the 3rd axis (transversal plane) and is currently on slice 40, the supervoxel display will pertain to edges specifically calculated for that specific slice in that plane. - -Eventually, with ever so increasing number of attempts and a few hurdles along the way, one of which particularly stood out since it marked our first step towards a good direction: - -> Challenges in rendering - -![](./supervoxel_rendering_issue.png) - -At last, an appealing result hit our sight. - -> Final result - -> *Note:* The image borders are intentional to emphasize the size of the visualizer which is currently defaulted to a certain width and height. - -![](./supervoxel_rendering_fixed.png) - -> *Note:* However, There are a few things left to cover here, most of which revolve around MedImages.jl and documentation for the same. List of PRs that facilitated the completion of the tasks highlighted above: - -(a) [https://github.com/JuliaHealth/MedEye3d.jl/pull/21](https://github.com/JuliaHealth/MedEye3d.jl/pull/21) - -(b) [https://github.com/JuliaHealth/MedEye3d.jl/pull/20](https://github.com/JuliaHealth/MedEye3d.jl/pull/20) - -(c) [https://github.com/JuliaHealth/MedEye3d.jl/pull/16](https://github.com/JuliaHealth/MedEye3d.jl/pull/16) - -(d) [https://github.com/JuliaHealth/MedEye3d.jl/pull/14](https://github.com/JuliaHealth/MedEye3d.jl/pull/14) - -(e) [https://github.com/JuliaHealth/MedEye3d.jl/pull/13](https://github.com/JuliaHealth/MedEye3d.jl/pull/13) - -(f) [https://github.com/JuliaHealth/MedEye3d.jl/pull/12](https://github.com/JuliaHealth/MedEye3d.jl/pull/12) - -# Contributions Beyond Coding - -## 1. Mentoring and Guidance - -I regularly organized meetings with my mentor to seek guidance on project direction and troubleshooting issues in the visualizer. This ensured that I stayed on track, received timely feedback, and addressed any challenges that arose. - -## 2. Package Documentation and Community Contribution - -I contributed to other medical imaging sub-ecosystem packages in JuliaHealth, including [MedImages.jl](https://github.com/Juliahealth/MedImages.jl) and [MedEval3D.jl](https://github.com/Juliahealth/MedEval3D.jl). Specifically, I set up documentation for these packages using DocuementerVitepress.jl. This not only enhanced the functionality of these packages but also helped maintain a coherent and organized package ecosystem. - -## 3. Multirepo Management and Collaboration - -In addition to my work on the MedEye3d visualizer, I made significant contributions to other JuliaHealth repositories, including [MedImages.jl](https://github.com/JuliaHealth/MedImages.jl) and worked over an [Insight Toolkit](https://github.com/InsightSoftwareConsortium/ITK) wrapper library [ITKIOWrapper.jl](https://github.com/JuliaHealth/ITKIOWrapper.jl) for support in image I/O down the road in MedImages.jl. I also maintained relevant documentation and ensured continuous collaboration and synchronization across these packages. - -# Conclusions and Future Development - -Within the scope of this 350-hour project, a comprehensive range of objectives were successfully addressed. Noteworthy achievements include: - -1. Fixed screen tear and flicker within the visualizer. Integration of threadsafe Julia channels. - -2. Achieved multi-image display over CT and PET modalities with crosshair rendering (Although, only one modality can be visualize at a time, i.e either CT | CT or PET | PET). - -3. Achieved supervoxel display in single image display mode. - -4. Achieved automatic windowing of MRI and PET most common modalities. - -Future work would include: - -- Support for the users on Darwin (Apple-based platforms). - -- Apart from that, we would need to add a function that dynamically allocates the texture number to the manual modification mask, regardless of the number of images passed for display, which is currently defaulted to 2. - -- Also, in the future, we would explore the stretch goals a bit more rigorously, particularly the implementation of [MedVoxelHD](https://doi.org/10.1016/j.softx.2024.101744) within MedEye3d. - -# Acknowledgements πŸ™‡β€β™‚οΈ - -1. [Jakub Mitura](https://orcid.org/0000-0003-1823-6823): aka, [Dr. Jakub Mitura](https://github.com/jakubMitura14) - -2. [Carlos Castillo Passi](https://scholar.google.com/citations?user=WzleS8YAAAAJ&hl=en): aka, [cncastillo](https://github.com/cncastillo) - -I would like to thank my mentor Dr. Jakub Mitura, for his help through out every phase of this project. The troubleshooting routines around problems would have rendered the project unsuccessful, if not for the support and guidance of my mentor throughout each part of this project. I would also like to thank Jacob Zelko, for leading the Juliahealth community with such vast expertise and leading efforts for engagement amongst the members through monthly meetings. 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