Dual-task Consistency
Code for this paper: Semi-supervised Medical Image Segmentation through Dual-task Consistency (AAAI2021)
@inproceedings{luo2021semi,
title={Semi-supervised Medical Image Segmentation through Dual-task Consistency},
author={Luo, Xiangde and Chen, Jieneng and Song, Tao and Wang, Guotai},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
volume={35},
number={10},
pages={8801--8809},
year={2021}
}
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
- Clone the repo:
git clone https://github.com/HiLab-git/DTC.git
cd DTC
-
Put the data in data/2018LA_Seg_Training Set.
-
Train the model
cd code
python train_la_dtc.py
- Test the model
python test_LA.py
Our pre-trained models are saved in the model dir DTC_model (both 8 labeled images and 16 labeled images), and the pretrained SASSNet and UAMT model can be download from SASSNet_model and UA-MT_model. 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 |
| DTC | 89.42 | 80.98 | 2.10 | 7.32 | AAAI2021 | 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 |
| MC-Net | 90.34 | 82.48 | 1.77 | 6.00 | Arxiv | 2021-03 |
- The training set consists of 8 labeled scans and 72 unlabeled scans and the testing set includes 20 scans.
| Methods | DICE (%) | Jaccard (%) | ASD (voxel) | 95HD (voxel) | Reference | Released Date |
|---|---|---|---|---|---|---|
| UAMT | 84.25 | 73.48 | 3.36 | 13.84 | MICCAI2019 | 2019-10 |
| SASSNet | 87.32 | 77.72 | 2.55 | 9.62 | MICCAI2020 | 2020-07 |
| DTC* | 87.51 | 78.17 | 2.36 | 8.23 | AAAI2021 | 2020-09 |
| LG-ER-MT | 85.54 | 75.12 | 3.77 | 13.29 | MICCAI2020 | 2020-10 |
| DUWM | 85.91 | 75.75 | 3.31 | 12.67 | MICCAI2020 | 2020-10 |
| MC-Net | 87.71 | 78.31 | 2.18 | 9.36 | Arxiv | 2021-03 |
- Note that, * denotes the results from MC-Net and the model has been openly available (provided by Dr. YiCheng), thanks for Dr. Yicheng.
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 been summarized in SSL4MIS.