부산대학교 도서관

Deep learning-based vessel segmentation tasks serve as important auxiliary tools for disease diagnosis. However, the region connecting the foreground and background, which is named the edge-buffer region in this letter, suffers from noisy labels and a lack of discriminative features due to low contrast and limitations of imaging devices. To address these limitations, we propose a self-correcting region supervision to revamp the noisy labels in the edge-buffer region. Furthermore, we introduce the concept of treating the edge-buffer region independently from the foreground and background, leveraging the designed contrastive learning method and edge-blur-guided module to enhance discriminative learning ability and collaborative learning ability across different regions, respectively. The experimental results comparison with other classical and state-of-the-art methods on DRIVE, CHASEDB1, and DCA1 datasets has proven the effectiveness of the proposed methods.