yiyg510/dtc

Semi-supervised Medical Image Segmentation through Dual-task Consistency

Dual-task Consistency

Code for this paper: Semi-supervised Medical Image Segmentation through Dual-task Consistency (DTC)

• More details and comparison methods will be released if the paper is accepted.
• The multi-classes DTC is under doing, and also will be released as we finished it.

Requirements

Some important required packages include:

• Pytorch version >=0.4.1.
• TensorBoardX
• Python == 3.6
• 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/DTC.git 
cd DTC

2. Put the data in data/2018LA_Seg_Training Set.

3. Train the model

cd code
python train_la_dtc.py or python train_la_dtc_v2.py

4. Test the model

python test_LA.py

Our best model is saved in the model dir DTC_model, and the pretrained SASSNet and UAMT model can be download from SASSNet_model and UA-MT_model.

Citation

If you find this repository is useful in your research, please consider to cite:

@article{luo2020semi,
  title={Semi-supervised Medical Image Segmentation through Dual-task Consistency},
  author={Luo, Xiangde and Chen, Jieneng and Song, Tao and Chen, Yinan and Wang, Guotai and Zhang, Shaoting},
  journal={arXiv preprint arXiv:2009.04448},
  year={2020}
}

Acknowledgement

• This code is adapted from UA-MT, SASSNet, SegWithDistMap.
• We thank Dr. Lequan Yu, M.S. Shuailin Li and Dr. Jun Ma for their elegant and efficient code base.
• More semi-supervised learning approaches for medical image segmentation have summarized in this repository SSL4MIS.