Multi-graph Graph Matching for Coronary Artery Semantic Labeling
Chen Zhao Zhihui Xu Pukar Baral Michel Esposito Weihua Zhou
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.