PyTorch implementation for our paper (Accepted by Computers in Biology and Medicine):
"A Lightweight Network Guided with Differential Matched Filtering for Retinal Vessel Segmentation"
/data:dataset & dataloader & data pre-processing
/nets:implementation for our network (DMF-AU)
/utils:implementation for loss function & optimizer
/onnx
- Pytorch trained weights from DRIVE, STARE datasets.
- The *.onnx weights can be directly used to extract vessels from fundus images, see onnx\infer.py
/results
- segmentation results for popular datasets
- segmentation results for cross-dataset-validation
For any questions, please contact me. And my e-mails are
@article{tan2023lightweight,
title={A lightweight network guided with differential matched filtering for retinal vessel segmentation},
author={Tan, Yubo and Zhao, Shi-Xuan and Yang, Kai-Fu and Li, Yong-Jie},
journal={Computers in Biology and Medicine},
pages={106924},
year={2023},
publisher={Elsevier}
}