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 ...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.展开更多
基金supported by the National Natural Science Foundation of China(81827805,82130060,61821002,92148205)the National Key Research and Development Program(2018YFA0704100,2018YFA0704104)+5 种基金Project funded by China Postdoctoral Science Foundation(2021M700772)Zhuhai Industry-University-Research Collaboration Program(ZH22017002210011PWC)Jiangsu Provincial Science and Technology Plan Project(BE2023769)Jiangsu Provincial Medical Innovation Center(CXZX202219)Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions,Nanjing Life Health Science and Technology Project(202205045)the Natural Science Foundation of Guangdong Province of China(2021A1515220183,2022B1515020010).
文摘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.
