Hyper Association Graph Matching with Uncertainty Quantification for
  Coronary Artery Semantic Labeling
Chen Zhao Michele Esposito Zhihui Xu Weihua Zhou
Coronary artery disease (CAD) is one of the primary causes leading to death
worldwide. Accurate extraction of individual arterial branches on invasive
coronary angiograms (ICA) is important for stenosis detection and CAD
diagnosis. However, deep learning-based models face challenges in generating
semantic segmentation for coronary arteries due to the morphological similarity
among different types of coronary arteries. To address this challenge, we
propose an innovative approach using the hyper association graph-matching
neural network with uncertainty quantification (HAGMN-UQ) for coronary artery
semantic labeling on ICAs. The graph-matching procedure maps the arterial
branches between two individual graphs, so that the unlabeled arterial segments
are classified by the labeled segments, and the coronary artery semantic
labeling is achieved. By incorporating the anatomical structural loss and
uncertainty, our model achieved an accuracy of 0.9345 for coronary artery
semantic labeling with a fast inference speed, leading to an effective and
efficient prediction in real-time clinical decision-making scenarios.