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
• ISSN: 0278-0062; 1558-254X; 1558-254X
• DOI: 10.1109/TMI.2023.3319720

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 segmentation performance that usually cannot satisfy clinical demands. To deal with these challenges, in this paper we propose an anatomy-and topology-preserving two-stage framework for coronary artery segmentation. The proposed framework consists of an anatomical dependency encoding (ADE) module and a hierarchical topology learning (HTL) module for coarse-to-fine segmentation, respectively. Specifically, the ADE module segments four heart chambers and aorta, and thus five distance field maps are obtained to encode distance between chamber surfaces and coarsely segmented coronary artery. Meanwhile, ADE also performs coronary artery detection to crop region-of-interest and eliminate foreground-background imbalance. The follow-up HTL module performs fine segmentation by exploiting three hierarchical vascular topologies, i.e ., key points, centerlines, and neighbor connectivity using a multi-task learning scheme. In addition, we adopt a bottom-up attention interaction (BAI) module to integrate the feature representations extracted across hierarchical topologies. Extensive experiments on public and in-house datasets show that the proposed framework achieves state-of-the-art performance for coronary artery segmentation.