Progressive Deep Segmentation of Coronary Artery via Hierarchical Topology Learning

Coronary artery segmentation is a critical yet challenging step in coronary artery stenosis diagnosis. Most existing studies ignore important contextual anatomical information and vascular topologies, leading to limited performance. To this end, this paper proposes a progressive deep-learning based framework for accurate coronary artery segmentation by leveraging contextual anatomical information and vascular topologies. The proposed framework consists of a spatial anatomical dependency (SAD) module and a hierarchical topology learning (HTL) module. Specifically, the SAD module coarsely segments heart chambers and coronary artery for region proposals, and captures spatial relationship between coronary artery and heart chambers. Then, the HTL module adopts a multi-task learning mechanism to improve the coarse coronary artery segmentation by simultaneously predicting the hierarchical vascular topologies i.e., key points, centerlines, and neighboring cube-connectivity. Extensive evaluations, ablation studies, and comparisons with existing methods show that our method achieves state-of-the-art segmentation performance.