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Coronary arteries and their branches supply blood to myocardium. Theobstruction of coronary arteries results in significant loss of myocardium,called acute myocardial infarction, and the number one cause of death globally.Hence, quantification of the regional amount of heart muscle subtended byobstructed coronary arteries is of critical value in clinical medicine.However, the conventional methods are inaccurate and frequently disagree withclinical practices. This study proposes a novel algorithm to segment regionalmyocardium-at-risk subtended by any potentially obstructed coronary artery.Assuming the geometric triangular mesh models of coronary artery and myocardiumderived from an individual cardiac computed tomography image, the proposedalgorithm performs (i) computation of the medial axis of the coronary arteryand (ii) segmentation of the coronary artery and myocardium using the medialaxis. The algorithm provides the fused segmentation of coronary artery andmyocardium via the medial axis. The computed result provides a robustmathematical linkage between myocardium-at-risk and supplying coronary arteriesso that ischemic myocardial regions can be accurately identified, and both theextent and severity of myocardial ischemia can be quantified effectively andefficiently. Furthermore, the correspondence between segmented coronary arteryand myocardium can be more importantly used for building optimization models ofcardiac systems for various applications. We believe that the proposedalgorithm and implemented VoroHeart program, which is freely available athttp://voronoi.hanyang.ac.kr/software/voroheart, will be an invaluable tool forpatient-specific risk predictions and the treatment of obstructed coronaryartery disease in clinical medicine. The algorithmic accuracy and efficiencyare theoretically asserted and experimentally verified.

Fused Segmentation of Geometric Models for Myocardium and Coronary Artery via Medial Axis