Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation

by Lequan Yu, Shujun Wang, Xiaomeng Li, Chi-Wing Fu, Pheng-Ann Heng.

News

We add our processed h5 data for LA segmentation. Please consider citing the summary paper when you use the data.

Introduction

This repository is for our MICCAI 2019 paper 'Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation'.

Installation

This repository is based on PyTorch 0.4.1.

Usage

1. Clone the repository:

   git clone https://github.com/yulequan/UA-MT.git
   cd UA-MT

2. Put the data in data/2018LA_Seg_TrainingSet.

3. Train the model:

   cd code
   python train_LA_meanteacher_certainty_unlabel.py --gpu 0

Citation

If UA-MT is useful for your research, please consider citing:

@inproceedings{yu2018pu,
     title={Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation},
     author={Yu, Lequan and Wang, Shujun and Li, Xiaomeng and Fu, Chi-Wing and Heng, Pheng-Ann},
     booktitle = {MICCAI},
     year = {2019} }

If you use the LA segmentation data, please also consider citing:

  @article{xiong2020global,
     title={A Global Benchmark of Algorithms for Segmenting Late Gadolinium-Enhanced Cardiac Magnetic Resonance Imaging},
     author={Xiong, Zhaohan and Xia, Qing and Hu, Zhiqiang and Huang, Ning and Vesal, Sulaiman and Ravikumar, Nishant and Maier, Andreas and Li, Caizi and Tong,          Qianqian and Si, Weixin and others},
     journal={Medical Image Analysis},
     year={2020} }

Note for data

We provided the processed h5 data in the data folder. You can refer the code in code/dataloaders/la_heart_processing.py to process your own data.

Questions

Please contact '[email protected]'