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Volume slicing refactoring #630

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merged 6 commits into from
Mar 19, 2024
Merged

Volume slicing refactoring #630

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bacf849
replace ijm macro activity with the metaphase image
AnniekStok Jan 30, 2024
4015ab0
add python-napari version for reslicing and visualizing anisotropy
AnniekStok Mar 14, 2024
c6bb725
remove the jython exercise from the markdown file
AnniekStok Mar 14, 2024
59bc455
Update volume_slicing_act1_imagej-macro.ijm
AnniekStok Mar 18, 2024
4db8a0e
Update volume_slicing_act1_python-napari.py implenting Tischi's comments
AnniekStok Mar 18, 2024
8770c39
Merge branch 'master' into volume_slicing_anniek
AnniekStok Mar 19, 2024
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4 changes: 2 additions & 2 deletions _includes/volume_slicing/volume_slicing_act1_imagej-gui.md
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- Open a 3D image
- Use `Imaeg > Properties` to check for anisotropic voxel sizes
- Use `Image > Stacks > Orthogonal views` to view the data in XY, XZ and YZ planes
- Use `Image > Properties` to check for anisotropic voxel sizes
- Use `Image > Stacks > Orthogonal views` to view the data in XY, XZ and YZ planes
- Understand how the anisotropy is dealt with
- Use `Image > Stacks > Reslice` to resample the data, exploring the below options to deal with anisotropy
- `Output spacing`
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67 changes: 40 additions & 27 deletions _includes/volume_slicing/volume_slicing_act1_imagej-macro.ijm
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// This macro opens the head image stack and reslices it to view it from different angles (side, front, top and diagonal view)
// and then selects slices corresponding to Z=60, X=135, and Y=160.
// Close other open images
run("Close All");
// open image stack
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not sure "stack" is the best word...
maybe "open volumetric image" or "open 3D image" ?

open("https://github.com/NEUBIAS/training-resources/raw/master/image_data/xyz_16bit_calibrated__dna_metaphase.tif");

// open head image stack
open("http://imagej.net/images/t1-head.zip");
rename("Head viewed from the side");
run("Duplicate...", "duplicate range=60");
rename("Head Z=60");
// explore the dimensions
width = getWidth();
height = getHeight();
depth = nSlices();
print("Original Image Dimensions: Width = " + width + ", Height = " + height + ", Depth = " + depth);

// Reslice the head image stack to view the head from the front and from the top, and select the slices for X=135 and Y=160 in the orginal view.
selectWindow("Head viewed from the side");
run("Reslice [/]...", "output=1.500 start=Left rotate"); // view head from the front
rename("Head viewed from the front");
run("Duplicate...", "duplicate range=135-135");
rename("Head X=135");
// reslice YZ with interpolation
run("Reslice [/]...", "output=0.300 start=Left flip rotate");
rename("YZ view");
width = getWidth();
height = getHeight();
depth = nSlices();
print("Image Dimensions YZ view: Width = " + width + ", Height = " + height + ", Depth = " + depth);

selectWindow("Head viewed from the side");
run("Reslice [/]...", "output=1.500 start=Top rotate"); // view head from the top
rename("Head viewed from the top");
run("Duplicate...", "duplicate range=160-160");
rename("Head Y=160");
// reslice XZ with interpolation
selectImage("xyz_16bit_calibrated__dna_metaphase.tif");
run("Reslice [/]...", "output=0.300 start=Top");
rename("XZ view");
width = getWidth();
height = getHeight();
depth = nSlices();
print("Image Dimensions XZ view: Width = " + width + ", Height = " + height + ", Depth = " + depth);

// Rotate the head stack that is viewed from the side and reslice to obtain diagonal view
selectWindow("Head viewed from the side");
run("Duplicate...", "duplicate");
run("Rotate... ", "angle=45 grid=1 interpolation=Bilinear enlarge stack"); // rotate the stack
run("Reslice [/]...", "output=1.500 start=Top rotate");
rename("Head in diagonal view");
// reslice YZ without interpolation
selectImage("xyz_16bit_calibrated__dna_metaphase.tif");
run("Reslice [/]...", "output=0.300 start=Left flip rotate avoid");
rename("YZ view");
width = getWidth();
height = getHeight();
depth = nSlices();
print("Image Dimensions YZ view WITHOUT interpolation: Width = " + width + ", Height = " + height + ", Depth = " + depth);

run("Tile");
// reslice XZ with interpolation
selectImage("xyz_16bit_calibrated__dna_metaphase.tif");
run("Reslice [/]...", "output=0.300 start=Top avoid");
rename("XZ view");
width = getWidth();
height = getHeight();
depth = nSlices();
print("Image Dimensions XZ view WITHOUT interpolation: Width = " + width + ", Height = " + height + ", Depth = " + depth);

run("Tile")
29 changes: 29 additions & 0 deletions _includes/volume_slicing/volume_slicing_act1_python-napari.py
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# !First start Napari and drag and drop the image (https://github.com/NEUBIAS/training-resources/raw/master/image_data/xyz_16bit_calibrated__dna_metaphase.tif) in the Napari viewer.

# Access the 3D array in the image layer
d = viewer.layers[0].data # assuming the image is in the first layer
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maybe better image instead of d?

shape = d.shape
print("Image shape:", shape)

# Compare the non-scaled non-scaled image to the scaled image. You can also use the 3D view to better appreciate the dimensions.
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non-scaled non-scaled :)

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Oops

voxel_dims = (0.300, 0.1377745, 0.1377745) # original voxel dimensions in µm, in order ZYX
scaled_voxel_dims = [1 / voxel_dims[0] * v for v in voxel_dims] # rescaling the voxel dimensions to maintain proportions but display with a z-step of 1.
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We try to avoid fancy python such as list comprehension...

Would that also work?
scaled_voxel_dims = voxel_dims / voxel_dims[0]

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@AnniekStok AnniekStok Mar 18, 2024 •
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This gives an error: "unsupported operand type(s) for /: 'tuple' and 'float'" (also with list), but we could do this instead:

scale_factor = 1 / voxel_dims[0]

scaled_voxel_dims = [voxel_dims[0] * scale_factor, voxel_dims[1] * scale_factor, voxel_dims[2] * scale_factor]

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Then the latter would be the best, imho.

print("New voxel dimensions scaled relative to original", scaled_voxel_dims)
viewer.add_image(d, name="Scaled to physical world dimensions", scale=scaled_voxel_dims) # Note that the scaled image layer contains the exact same data as the non-scaled image, but is only visualized differently to show the correct proportions.

# Create 2D slices (YX, ZX, and ZY)
mid_z_slice_ind = shape[0] // 2
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while // 2 is cool maybe doing a floating point division and then casting to integer could be more explicit for beginners?

mid_y_slice_ind = shape[1] // 2
mid_x_slice_ind = shape[2] // 2

viewer.add_image(d[mid_z_slice_ind], name=f"z={mid_z_slice_ind}")
viewer.add_image(d[mid_z_slice_ind], name=f"scaled: z={mid_z_slice_ind}", scale = scaled_voxel_dims[1:])
viewer.add_image(d[:, mid_y_slice_ind, :], name=f"y={mid_y_slice_ind}")
viewer.add_image(d[:, mid_y_slice_ind, :], name=f"scaled: y={mid_y_slice_ind}", scale = [scaled_voxel_dims[0], scaled_voxel_dims[2]])
viewer.add_image(d[:, :, mid_x_slice_ind], name=f"x={mid_x_slice_ind}")
viewer.add_image(d[:, :, mid_x_slice_ind], name=f"scaled: x={mid_x_slice_ind}", scale = scaled_voxel_dims[:2])

# View in grid mode side by side
viewer.grid.stride = 1
viewer.grid.shape = (-1, 2)
viewer.grid.enabled=True
6 changes: 3 additions & 3 deletions _modules/volume_slicing.md
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Expand Up @@ -17,13 +17,13 @@ concept_map: >
V["Volume data"] --> S("Slicing")
V --- A["Anisotropic"]
S --> M["2D image"]
M -.- A
M -.- A

figure: /figures/volume_slicing.png
figure_legend: "Extracting 2-D slices from a 3-D volume, e.g. to be visualised on a computer monitor."
figure_legend: "Extracting 2-D slices from a 3-D volume, e.g. to be visualised on a computer monitor."

multiactivities:
- ["volume_slicing/volume_slicing_act1.md", [["ImageJ GUI", "volume_slicing/volume_slicing_act1_imagej-gui.md", "markdown"], ["ImageJ Macro", "volume_slicing/volume_slicing_act1_imagej-macro.ijm", "java"], ["ImageJ Jython", "volume_slicing/volume_slicing_act1_imagej-jython.py", "python"]]]
- ["volume_slicing/volume_slicing_act1.md", [["ImageJ GUI", "volume_slicing/volume_slicing_act1_imagej-gui.md", "markdown"], ["ImageJ Macro", "volume_slicing/volume_slicing_act1_imagej-macro.ijm", "java"], ["Python Napari", "volume_slicing/volume_slicing_act1_python-napari.py", "python"]]]

assessment: >

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