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Duo-SegNet: Adversarial Dual-Views for Semi-Supervised Medical Image Segmentation
This repo contains the supported pytorch code and configuration files to reproduce medical image segmentaion results of Duo-SegNet.
<a href="https://www.codecogs.com/eqnedit.php?latex={\mathcal{F}_i(\cdot)}_{i=1}^2" target="_blank"><img src="https://latex.codecogs.com/gif.latex?{\mathcal{F}_i(\cdot)}_{i=1}^2" title="{\mathcal{F}_i(\cdot)}_{i=1}^2" /></a> and <a href="https://www.codecogs.com/eqnedit.php?latex=\psi&space;(\cdot)" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\psi&space;(\cdot)" title="\psi (\cdot)" /></a> denote Segmentation networks and Critic network. Here, Critic criticizes between prediction masks and the ground truth masks to perform the min-max game.
Environment
Please prepare an environment with python=3.8, and then run the command "pip install -r requirements.txt" for the dependencies.
Data Preparation
-
For experiments we used three datasets:
- Nuclei (2018 Data Science Bowl)
- Spleen (Medical segmentation decathlon - MSD)
- Heart ([Medical segmentation decathlon - MSD)
-
File structure
data ├── nuclei | ├── train │ │ ├── image │ │ │ └── 00ae65... │ │ └── mask │ │ └── 00ae65... ├── spleen ├── heart │ | Duo-SegNet ├──train.py ... -
Use Med2Image to convert NIFTI to PNG.
Train/Test
- Train : Run the train script on nuclei dataset for 5% of labeled data.
python train.py --dataset nuclei --ratio 0.05 --epoch 200
- Test : Run the test script on nuclei dataset.
python test.py --dataset nuclei
Acknowledgements
This repository makes liberal use of code from Deep Co-training and pytorch-CycleGAN-and-pix2pix
References
Citing Duo-SegNet
@inproceedings{peiris2021duo,
title={Duo-SegNet: Adversarial Dual-Views for Semi-Supervised Medical Image Segmentation},
author={Peiris, Himashi and Chen, Zhaolin and Egan, Gary and Harandi, Mehrtash},
booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
pages={428--438},
year={2021},
organization={Springer}
}