Hyper Association Graph Matching with Uncertainty Quantification for Coronary Artery Semantic Labeling
This work addresses the need for efficient and accurate coronary artery semantic labeling in clinical decision-making for coronary artery disease diagnosis, representing an incremental improvement over existing methods.
The paper tackled the problem of accurately labeling coronary artery segments in angiograms, which is challenging due to morphological similarities, by proposing a hyper association graph-matching neural network with uncertainty quantification, achieving an accuracy of 0.9345 with fast inference speed.
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.