Semantic labeling of coronary arterial segments in invasive coronary
angiography (ICA) is important for automated assessment and report generation
of coronary artery stenosis in the computer-aided diagnosis of coronary artery
disease (CAD). Inspired by the training procedure of interventional
cardiologists for interpreting the structure of coronary arteries, we propose
an association graph-based graph matching network (AGMN) for coronary arterial
semantic labeling. We first extract the vascular tree from invasive coronary
angiography (ICA) and convert it into multiple individual graphs. Then, an
association graph is constructed from two individual graphs where each vertex
represents the relationship between two arterial segments. Using the
association graph, the AGMN extracts the vertex features by the embedding
module, aggregates the features from adjacent vertices and edges by graph
convolution network, and decodes the features to generate the semantic mappings
between arteries. By learning the mapping of arterial branches between two
individual graphs, the unlabeled arterial segments are classified by the
labeled segments to achieve semantic labeling. A dataset containing 263 ICAs
was employed to train and validate the proposed model, and a five-fold
cross-validation scheme was performed. Our AGMN model achieved an average
accuracy of 0.8264, an average precision of 0.8276, an average recall of
0.8264, and an average F1-score of 0.8262, which significantly outperformed
existing coronary artery semantic labeling methods. In conclusion, we have
developed and validated a new algorithm with high accuracy, interpretability,
and robustness for coronary artery semantic labeling on ICAs.

开发了一种新的算法 AGMN，通过 ICA 中提取的血管树和构建关联图，实现冠状动脉分段的语义标注，并成功地在 263 个 ICA 上进行了训练和验证。该算法在精度、可解释性和鲁棒性方面表现出良好的性能，显著优于现有的冠状动脉语义标注方法。