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This project was originally developed for our previous works WORD[Paper & Dataset]. We also investigated the comprehensive results on the ACDC dataset on this Tech Report. If you use this codebase in your research, please cite the following works:
@article{luo2022scribbleseg, title={Scribble-Supervised Medical Image Segmentation via Dual-Branch Network and Dynamically Mixed Pseudo Labels Supervision}, author={Xiangde Luo, Minhao Hu, Wenjun Liao, Shuwei Zhai, Tao Song, Guotai Wang, Shaoting Zhang}, journal={arXiv preprint arXiv:2203.02106}, year={2022}} @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 the pre-processed ACDC data in Here.
- To simulate the scribble annotation for other datasets, we further provide the simulation code at Here.
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 this project.
git clone https://github.com/Luoxd1996/WSL4MIS
cd WSL4MIS
- Data pre-processing os used or the processed data.
cd code
python dataloaders/acdc_data_processing.py
- Train the model
cd code
bash train_wss.sh # train model with scribble or dense annotations.
bash train_ssl.sh # train model with mix-supervision (mask annotations and without annotation).
- Test the model
python test_2D_fully.py --sup_type scribble/label --exp ACDC/the trained model fold --model unet
python test_2D_fully_sps.py --sup_type scribble --exp ACDC/the trained model fold --model unet_cct
- 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.
- pCE
- pCE + TV
- pCE + Entropy Minimization
- pCE + GatedCRFLoss
- pCE + Intensity Variance Minimization
- pCE + Random Walker
- pCE + MumfordShah_Loss
- Scribble2Label
- USTM
- 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.