Accurate segmentation of coronary arteries is a pivotal process in assessing cardiovascular diseases. However, the intricate structure of the cardiovascular system presents significant challenges for automatic segmentation, especially when utilizing methodologies like the SYNTAX Score, which relies extensively on detailed structural information for precise risk stratification. To address these difficulties and cater to this need, we present MPSeg, an innovative multi-phase strategy designed for coronary artery segmentation. Our approach specifically accommodates these structural complexities and adheres to the principles of the SYNTAX Score. Initially, our method segregates vessels into two categories based on their unique morphological characteristics: Left Coronary Artery (LCA) and Right Coronary Artery (RCA). Specialized ensemble models are then deployed for each category to execute the challenging segmentation task. Due to LCA's higher complexity over RCA, a refinement model is utilized to scrutinize and correct initial class predictions on segmented areas. Notably, our approach demonstrated exceptional effectiveness when evaluated in the Automatic Region-based Coronary Artery Disease diagnostics using x-ray angiography imagEs (ARCADE) Segmentation Detection Algorithm challenge at MICCAI 2023.

翻译：准确分割冠状动脉是评估心血管疾病的关键步骤。然而，心血管系统的复杂结构给自动分割带来了重大挑战，尤其是在采用诸如SYNTAX评分等依赖于详细结构信息进行精确风险分层的方法时。为应对这些困难并满足这一需求，我们提出了MPSeg，一种专为冠状动脉分割设计的创新多阶段策略。我们的方法特别考虑了这些结构复杂性，并遵循SYNTAX评分的原则。首先，我们的方法根据血管独特的形态特征将其分为两类：左冠状动脉（LCA）和右冠状动脉（RCA）。随后，针对每个类别部署专门的集成模型以执行具有挑战性的分割任务。由于LCA的复杂性高于RCA，我们利用一个精炼模型来审查并修正分割区域的初始类别预测。值得注意的是，我们的方法在MICCAI 2023的基于区域的冠状动脉疾病自动诊断（ARCADE）分割检测算法挑战赛中展现了卓越的有效性。