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.

我们介绍了一种名为 MPSeg 的创新多阶段策略，专门用于冠状动脉分割，其采用了分离左冠状动脉和右冠状动脉以及集成模型等方法，针对冠状动脉的结构复杂性和 SYNTAX Score 的原则进行了设计和优化，并在 MICCAI 2023 年的 ARCADE 挑战中表现出了卓越的效果。