MPSeg : Multi-Phase strategy for coronary artery Segmentation
Jonghoe Ku Yong-Hee Lee Junsup Shin In Kyu Lee Hyun-Woo Kim
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.