quyijie / WSL4MIS

Scribbles or Points-based weakly-supervised learning for medical image segmentation, a strong baseline, and tutorial for research and application.

Weakly-supervised learning for medical image segmentation (WSL4MIS).

• 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}}
  

Dataset

• 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.

Requirements

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.

Usage

1. Clone the repo:

git clone https://github.com/HiLab-git/WSL4MIS
cd WSL4MIS

2. Download and pre-process data and put the data in ../data/ACDC.

3. Train the model

cd code
python train_XXX_2D.py

4. Test the model

python test_2D_fully.py

5. 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.

Implemented methods

• pCE
• pCE + TV
• pCE + Entropy Minimization
• pCE + GatedCRFLoss
• pCE + Intensity Variance Minimization

Acknowledgement

• 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.