This project was originally developed for our two previous works WORD (MedIA2022) and WSL4MIS (MICCAI2022). If you use this project 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={Medical Image Computing and Computer Assisted Intervention -- MICCAI 2022},
year={2022},
pages={528--538}}
@article{luo2022word,
title={{WORD}: A large scale dataset, benchmark and clinical applicable study for abdominal organ segmentation from CT image},
author={Xiangde Luo, Wenjun Liao, Jianghong Xiao, Jieneng Chen, Tao Song, Xiaofan Zhang, Kang Li, Dimitris N. Metaxas, Guotai Wang, and Shaoting Zhang},
journal={Medical Image Analysis},
volume={82},
pages={102642},
year={2022},
publisher={Elsevier}}
@misc{wsl4mis2020,
title={{WSL4MIS}},
author={Luo, Xiangde},
howpublished={\url{https://github.com/Luoxd1996/WSL4MIS}},
year={2021}}
A re-implementation of this work based on the PyMIC can be found here (WSLDMPLS).
Some important required packages include:
pip install efficientnet_pytorch
Follow official guidance to install Pytorch.
Clone this project.
git clone https://github.com/HiLab-git/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.