CGRNet: Contour-Guided Graph Reasoning Network for Ambiguous Biomedical Image Segmentation

Authors: Kun Wang, Xiaohong Zhang, Yuting Lu, Xiangbo Zhang, Wei Zhang.

1.1. 🔥NEWS🔥 :

• [2021/10/30]:fire: Release the inference code!
• [2021/10/28] Create repository.

Prerequisites

• Python 3.5
• Pytorch 1.1
• OpenCV 4.0
• Numpy 1.15
• TensorboardX

Clone repository

git clone https://github.com/DLWK/CGRNet.git
cd CGRNet/

Download dataset

Download the datasets and unzip them into data folder

• COVID-19
• Download dataset from following URL
• You can use our data/dataloader2.py to load the datasets.

Training & Evaluation

 cd CGRNet/
 python3 train.py
 ################
 python3 test.py
 

Demo

from  CGRmodes.CGR import  CGRNet
if __name__ == '__main__':
    ras =CGRNet(n_channels=3, n_classes=1).cuda()
    input_tensor = torch.randn(4, 3, 352, 352).cuda()
    out,out1 = ras(input_tensor)
    print(out.shape) 

2.1 Overview framework

2.2 Visualization Results

Citation

• If you find this work is helpful, please cite our paper

@article{wang2022cgrnet,
  title={CGRNet: Contour-guided graph reasoning network for ambiguous biomedical image segmentation},
  author={Wang, Kun and Zhang, Xiaohong and Lu, Yuting and Zhang, Xiangbo and Zhang, Wei},
  journal={Biomedical Signal Processing and Control},
  volume={75},
  pages={103621},
  year={2022},
  publisher={Elsevier}

Tips

🔥If you have any questions about our work, please do not hesitate to contact us by emails. ⬆ back to top