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. Our implemented 3D version of CCT (with main decoder and three auxiliary decoders) will be updated as soon as possible, and the other comparison method can be found in SSL4MIS

Results on the Left Atrium dataset (SOTA).

• The training set consists of 16 labeled scans and 64 unlabeled scans and the testing set includes 20 scans.

Methods	DICE (%)	Jaccard (%)	ASD (voxel)	95HD (voxel)	Reference	Released Date
UAMT	88.88	80.21	2.26	7.32	MICCAI2019	2019-10
SASSNet	89.54	81.24	2.20	8.24	MICCAI2020	2020-07
Orginal DTC	89.42	80.98	2.10	7.32	Arxiv	2020-09
LG-ER-MT	89.62	81.31	2.06	7.16	MICCAI2020	2020-10
DUWM	89.65	81.35	2.03	7.04	MICCAI2020	2020-10
Updated DTC	89.85	81.72	1.81	7.03	This repo	2020-10

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.