Boundary Preserving with Attention-Guided Filtering Network for Retinal Vessel Segmentation

Accurate segmentation of retinal vessels is critical for the early detection of vision-threatening diseases. Although U-Net-based methods have shown strong performance, they often fail to capture thin vessels and preserve boundary details due to repeated downsampling. To overcome these limitations, we propose an enhanced U-shaped network that incorporates a multi-scale attention guided filtering module, allowing the model to retain edge details and suppress noise more effectively. Experiments conducted on the DRIVE, STARE, CHASE_DB1, and HRF datasets demonstrate that the proposed method consistently achieves the best results across multiple metrics. The improvements in F1 score and sensitivity confirm its capability to recover fine vascular structures and its potential for clinical application.