The topology integrity of blood vessel segmentation is crucial for clinical disease diagnosis. However, existing methods for enhancing vessel topology have overlooked the degree and type of topology interaction, and have not incorporated topology contrast relation into consideration. In this letter, we propose confindence-based topolopy interaction and topology contrast loss method to enhance the interaction and contrast relations for intra-class and inter-class of topology structure. Additionally, considering the influence of multiscale feature on topology learning, we propose lightweight global feature extraction and align fusion to better capture global features and mitigate feature misalignment. The quantitative and qualitative comparisons on the DRIVE and STARE datasets show the superiority of the proposed model. Furthermore, the improvements experiments of the advanced topology enhancement networks using our proposed methods, and ablation experiments on DCA1 datasets provide evidence of the effectiveness of the proposed method.

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.