An Anatomy- and Topology-Preserving Framework for Coronary Artery Segmentation

• Published in: IEEE transactions on medical imaging Vol. 43; no. 2; p. 1
• Main Authors: Zhang, Xiao, Sun, Kaicong, Wu, Dijia, Xiong, Xiaosong, Liu, Jiameng, Yao, Linlin, Li, Shufang, Wang, Yining, Feng, Jun, Shen, Dinggang
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.02.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: anatomical dependency encoding; Anatomy; Aorta; Arteries; Biomedical imaging; Cardiovascular disease; coarse-to-fine segmentation; Coronary Artery Disease; Coronary artery segmentation; Coronary vessels; Deep Learning; Encoding; Feature extraction; Heart; Heart - diagnostic imaging; Heart diseases; hierarchical topology representation; Humans; Image Processing, Computer-Assisted; Image segmentation; Learning; Modules; multi-task learning; Segmentation; Topology

Coronary artery segmentation is critical for coronary artery disease diagnosis but challenging due to its tortuous course with numerous small branches and inter-subject variations. Most existing studies ignore important anatomical information and vascular topologies, leading to less desirable segmen...