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}
}
Some important required packages include:
Follow official guidance to install Pytorch.
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
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 |
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 |