BPENet: Boundary perception enhancement network for retinal vessel and coronary angiogram segmentation

Vessel segmentation is crucial for assisting in the diagnosis and treatment of a range of diseases, such as retinal diseases and coronary artery diseases. However, current vessel segmentation methods often face the problem of poor vessel boundary segmentation, making the vessel segmentation task still challenging. This study aims to alleviate this problem, thereby improving the vessel segmentation results. By introducing a boundary detection auxiliary task to the vessel segmentation main task, we propose a boundary perception enhancement network (BPENet) for retinal vessel and coronary angiogram segmentation. Among them, to enhance vessel feature extraction capability, BPENet introduces the feature extraction enhancement (FE2) module. To enhance vessel boundary perception capability, BPENet introduces the boundary enhancement (BE) module. In addition, to fully leverage feature information from different layers, BPENet introduces the deep feature aggregation (DFA) module. Experimental results on retinal vessel datasets (DRIVE, CHASE-DB1, and STARE) and coronary angiogram datasets (DCA1 and XCAD) show that BPENet outperforms the existing mainstream segmentation methods. BPENet is expected to provide a reliable vessel segmentation result for doctors during the diagnosis and treatment of related diseases.