Coronary artery disease (CAD) stands as the leading cause of death worldwide,
and invasive coronary angiography (ICA) remains the gold standard for assessing
vascular anatomical information. However, deep learning-based methods encounter
challenges in generating semantic labels for arterial segments, primarily due
to the morphological similarity between arterial branches. To address this
challenge, we model the vascular tree as a graph and propose a multi-graph
graph matching (MGM) algorithm for coronary artery semantic labeling. The MGM
algorithm assesses the similarity between arterials in multiple vascular tree
graphs, taking into account the cycle consistency between each pair of graphs.
This ensures that unannotated arterial segments are appropriately labeled by
matching them with annotated segments. Through the incorporation of anatomical
graph structure, radiomics features, and semantic mapping, the proposed MGM
model achieves an impressive accuracy of 0.9471 for coronary artery semantic
labeling. This approach presents a novel tool for coronary artery analysis
using ICA videos, offering valuable insights into vascular health and
pathology.

该研究提出了一种基于多图图匹配算法（MGM）的冠状动脉语义标签方法，通过融合解剖图结构、影像学特征和语义映射，实现了冠状动脉语义标签的准确率为 0.9471，为冠状动脉分析提供了一种新的工具。