Multiscale Attention Network via Topology Learning for Cerebral Vessel Segmentation in Angiography Images

Cerebrovascular diseases are a widespread threat to human health. The accurate extraction of cerebral vessel structures is of paramount importance in the diagnosis and treatment of cerebrovascular diseases. However, the complexity of cerebral vessel structures and the low imaging contrast present significant challenges for vessel segmentation. Therefore, we propose a Multiscale Attention Network based on topological learning to extract vessel structures from angiographic images. This method employs a Multiscale Squeeze Attention (MSA) module for channel-wise attention learning, extracting multiscale attention feature maps from angiographic images. To maintain the topological connectivity of vessel segmentation, we introduced the clDice loss function to enforce skeleton connectivity of vessel segmentation. We conducted an experimental analysis of the proposed method using a publicly available cerebral vessel dataset. The results demonstrated that the proposed method achieved a sensitivity score of 0.8507 and a dice score of 0.8669 for cerebrovascular segmentation, enabling accurate and complete extraction of vascular structures. The proposed method was extended to coronary angiography images. The results show that the proposed method can accurately extract coronary structures, proving its broad applicability to other vascular segmentation tasks.