smallmax00 / Graph_Region_Boudnary

IEEE-TMI 2021: code for 'Graph-based Region and Boundary Aggregation for Biomedical Image Segmentation'

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IEEE-TMI: Graph-based Region and Boundary Aggregation for Biomedical Image Segmentation

ODOC Segmentation

Data Preparation

Prepare your data, the index of the test data is in test.list  

Pre-trained Model

--Download best_model.pth and put it into ./model/

Pred-trained backbone

--Download res2net50_v1b_26w_4s-3cf99910.pth and put it into ./res_weight/

Predict

 -- Run test.py  

Citation

If you find our work useful or our work gives you any insights, please cite:

@article{meng2021graph,
  title={Graph-based region and boundary aggregation for biomedical image segmentation},
  author={Meng, Yanda and Zhang, Hongrun and Zhao, Yitian and Yang, Xiaoyun and Qiao, Yihong and MacCormick, Ian JC and Huang, Xiaowei and Zheng, Yalin},
  journal={IEEE transactions on medical imaging},
  volume={41},
  number={3},
  pages={690--701},
  year={2021},
  publisher={IEEE}
}

Acknowledgement

Part of our code is built based on PraNet, thanks Dr.Fan Dengping for such a good project.

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IEEE-TMI 2021: code for 'Graph-based Region and Boundary Aggregation for Biomedical Image Segmentation'

License:MIT License


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Language:Python 100.0%