Blood Vessel Segmentation via Topology Interaction and Contrast

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.