Abstract
Coronary artery disease (CAD) is the most common form of cardiovascular disease, characterized by gradual narrowing of the artery walls due to plaque buildup. Computational fluid dynamics (CFD) is a non-invasive approach often used to investigate how these anatomical changes perturb local hemodynamics and contribute to the pathological mechanism of progression. Therefore, the accuracy of coronary tree alignment and anatomical feature detection is key to understanding these hemodynamically mediated mechanisms. Despite advances, current methods face challenges, such as the need for manual selection of landmarks, often resulting in a semi-automated experience. This study aims to improve this by developing a fully automated system to detect 3D anatomical characteristics and align coronary tree geometries in large clinical datasets. Our proposed algorithm enables full automatic placement of the corresponding centerline points and alignment evaluation through similarity-based assessment of Jaccard index (intersection over union) in a cohort of 73 coronary geometries.