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ME-UNet

Official repository for "ME-UNet: Enhancing Mamba for Myocardial Pathology Segmentation in Multi-Center Multi-Sequence CMR Images".

Release

  • News: 2024/11/27: ME-UNet released.

Get Start

Requirements: CUDA ≥ 11.6

  1. Create a virtual environment: conda create -n meunet python=3.10 -y and conda activate meunet
  2. Install Pytorch 2.0.1: pip install torch==2.0.1 torchvision==0.15.2
  3. Install Mamba: pip install causal-conv1d==1.1.1 and pip install mamba-ssm
  4. Download code: git clone https://github.com/AFuJianPeople/ME-UNet
  5. cd ME-UNet/me-unet
  6. run pip install -e .

Data Preparation

Download MyoPS++2024 dataset, then put them into the ME-Unet/data/nnUNet_raw folder. ME-UNet is built on the popular nnU-Net framework. If you want to train ME-UNet on your own dataset, please follow this guideline to prepare the dataset.

Please organize the dataset as follows:

data/
├── nnUNet_raw/
│   ├── Dataset001_Myops/
│   │   ├── imagesTr
│   │   │   ├── Myops_0001_0000.nii.gz
│   │   │   ├── Myops_0002_0000.nii.gz
│   │   │   ├── ...
│   │   ├── labelsTr
│   │   │   ├── Myops_0001.nii.gz
│   │   │   ├── Myops_0002.nii.gz
│   │   │   ├── ...
│   │   ├── dataset.json
│   ├── ...

Based on nnUNet, preprocess the data and generate the corresponding configuration files (the generated results can be found in the ME-Unet/data/nnUNet_preprocessed folder).

nnUNetv2_plan_and_preprocess -d DATASET_ID --verify_dataset_integrity

Model Training

Train 2D models

  • Train 2D ME-Unet model
nnUNetv2_train DATASET_ID 2d all -tr nnUNetTrainerMEUNet

Train 3D models

  • Train 3D ME-Unet model
nnUNetv2_train DATASET_ID 3d_fullres all -tr nnUNetTrainerMEUNet

Inference

Inference 2D models

  • Inference 2D ME-Unet model
nnUNetv2_predict -i INPUT_FOLDER -o OUTPUT_FOLDER -d DATASET_ID -c 2d -tr nnUNetTrainerMEUNet --disable_tta

Inference 3D models

  • Inference 3D ME-Unet model
nnUNetv2_predict -i INPUT_FOLDER -o OUTPUT_FOLDER -d DATASET_ID -c 3d_fullres -tr nnUNetTrainerMEUNet --disable_tta

Citation

If you find our work helpful, please consider citing the papers

Acknowledgements

We acknowledge all the authors of the employed public datasets, allowing the community to use these valuable resources for research purposes. We also thank the authors of nnU-Net, Mamba and U-Mamba for making their valuable code publicly available.