Scribbles or Points-based weakly-supervised learning for medical image segmentation, a strong baseline, and tutorial for research and application.
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.
git clone https://github.com/HiLab-git/WSL4MIS
cd WSL4MIS
cd code
python dataloaders/acdc_data_processing.py
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).
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