This repository contains the Full-Scale Guided Network (FSG-Net), a novel approach for retinal vessel segmentation. FSG-Net introduces a feature representation module that effectively captures full-scale structural information using modernized convolution blocks. A guided convolution block then refines this information, leveraging an attention-guided filter similar to unsharp masking to enhance fine vascular structures. This architecture delivers competitive performance for retinal vessel segmentation across multiple public datasets.

The model was presented in the paper Full-scale Representation Guided Network for Retinal Vessel Segmentation (arXiv:2501.18921).

Code: https://github.com/ZombaSY/FSG-Net-pytorch

The model achieves competitive performance across several public datasets:

For detailed instructions on environment setup, dataset preparation, training, and inference, please refer to the official GitHub repository. Pre-trained models for each dataset can also be found on the GitHub releases page.

If you find this work helpful or inspiring, please consider citing our paper: