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Complementary consistency semi-supervised learning for 3D left atrial image segmentation
by Hejun Huang, Zuguo Chen*, Chaoyang Chen, Ming Lu, Ying Zou
Introduction
This repository is for our paper 'Complementary consistency semi-supervised learning for 3D left atrial image segmentation'.
Requirements
This repository is based on Pytorch 1.9.1, CUDA11.1 and Python 3.6.5
Usage
Install
Clone the repo:
git clone https://github.com/Cuthbert-Huang/CC-Net.git
Dataset
We use the dataset of 2018 Atrial Segmentation Challenge. The processed h5 datas were provided in googleDrive and baiduNetdisk (password: cuth). Please unzip and put them in the data/LA
folder.
Preprocess
If you want to process .nrrd data into .h5 data, you can use code/dataloaders/preprocess.py
.
Pretrained models
The pretrained models were provided in googleDrive and baiduNetdisk (password: cuth).
Please put them in the pretrained
folder.
Train
If you want to train CC-Net for 10% labels on LA.
cd CC-Net
python ./code/train_ccnet_3d_v1.py --dataset_name LA --model ccnet3d_v1 --exp CCNET --labelnum 8 --gpu 0 --temperature 0.1 --max_iteration 10000
If you want to train CC-Net for 20% labels on LA.
cd CC-Net
python ./code/train_ccnet_3d_v1.py --dataset_name LA --model ccnet3d_v1 --exp CCNET --labelnum 16 --gpu 0 --temperature 0.1 --max_iteration 10000
Test
If you want to test CC-Net for 10% labels on LA.
cd CC-Net
python ./code/test.py --dataset_name LA --model ccnet3d_v1 --exp CCNET --labelnum 8 --gpu 0
If you want to test CC-Net for 20% labels on LA.
cd CC-Net
python ./code/test.py --dataset_name LA --model ccnet3d_v1 --exp CCNET --labelnum 16 --gpu 0
Citation
If our CC-Net is useful for your research, please consider citing:
@article{huang2023complementary,
title = {Complementary consistency semi-supervised learning for 3D left atrial image segmentation},
journal = {Computers in Biology and Medicine},
volume = {165},
pages = {107368},
year = {2023},
issn = {0010-4825},
doi = {https://doi.org/10.1016/j.compbiomed.2023.107368},
url = {https://www.sciencedirect.com/science/article/pii/S0010482523008338},
author = {Hejun Huang and Zuguo Chen and Chaoyang Chen and Ming Lu and Ying Zou}
}
If you use the dataset of 2018 Atrial Segmentation Challenge, please consider citing:
@article{Xiong_A_global2021,
author = {Xiong, Zhaohan and Xia, Qing and Hu, Zhiqiang and Huang, Ning and Bian, Cheng and Zheng, Yefeng and Vesal, Sulaiman and Ravikumar, Nishant and Maier, Andreas and Yang, Xin},
title = {A global benchmark of algorithms for segmenting the left atrium from late gadolinium-enhanced cardiac magnetic resonance imaging},
journal = {Medical Image Analysis},
year = {2021} }
Acknowledgements
Our code is origin from UAMT, SASSNet, DTC, and MC-Net+. Thanks to these authors for their excellent work.