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This project was originally developed for our previous works WORD[Paper & Dataset]. If you use this codebase in your research, please cite the following works:
@article{luo2021word, title={{WORD}: Revisiting Organs Segmentation in the Whole Abdominal Region}, author={Luo, Xiangde and Liao, Wenjun and Xiao, Jianghong and Song, Tao and Zhang, Xiaofan and Li, Kang and Wang, Guotai and Zhang, Shaoting}, journal={arXiv preprint arXiv:2111.02403}, year={2021}} @misc{wsl4mis2020, title={{WSL4MIS}}, author={Luo, Xiangde}, howpublished={\url{https://github.com/Luoxd1996/WSL4MIS}}, year={2021}}
- The ACDC dataset with mask annotations can be downloaded from: ACDC.
- The Scribble annotations of ACDC can be downloaded from: Scribble.
- The data processing code in Here or email me for pre-processed data.
Some important required packages include:
- Pytorch version >=0.4.1.
- TensorBoardX
- Python == 3.6
- Efficientnet-Pytorch
pip install efficientnet_pytorch
- Some basic python packages such as Numpy, Scikit-image, SimpleITK, Scipy ......
Follow official guidance to install Pytorch.
- Clone the repo:
git clone https://github.com/HiLab-git/WSL4MIS
cd WSL4MIS
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Download and pre-process data and put the data in
../data/ACDC
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Train the model
cd code
python train_XXX_2D.py
- Test the model
python test_2D_fully.py
- Training curves on the fold1 Note: pCE means partially cross-entropy, TV means total variation, label denotes supervised by mask, scribble represents just supervised by scribbles.
- The GatedCRFLoss is adapted from GatedCRFLoss for medical image segmentation.
- The codebase is adapted from our previous work SSL4MIS.
- The WORD dataset will be presented at WORD.