# Multi-graph Graph Matching for Coronary Artery Semantic Labeling

arXiv: https://arxiv.org/abs/2402.15894 · HTML: https://mlsanity.com/paper/2402.15894/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 41 references that are in the map.
- First posted on arXiv 24 Feb 2024.

## Builds on (most cited first, 8 of 8)

- [Learning Combinatorial Optimization Algorithms over Graphs](https://mlsanity.com/paper/1704.01665/) (2017)
- [Neural Graph Matching Network: Learning Lawler's Quadratic Assignment Problem with Extension to Hypergraph and Multiple-graph Matching](https://mlsanity.com/paper/1911.11308/) (2019)
- [Learning Combinatorial Optimization on Graphs: A Survey with Applications to Networking](https://mlsanity.com/paper/2005.11081/) (2020)
- [Zorro: Valid, Sparse, and Stable Explanations in Graph Neural Networks](https://mlsanity.com/paper/2105.08621/) (2021)
- [CPR-GCN: Conditional Partial-Residual Graph Convolutional Network in Automated Anatomical Labeling of Coronary Arteries](https://mlsanity.com/paper/2003.08560/) (2020)
- [AGMN: Association Graph-based Graph Matching Network for Coronary Artery Semantic Labeling on Invasive Coronary Angiograms](https://mlsanity.com/paper/2301.04733/) (2023)
- [Neural Graph Matching for Pre-training Graph Neural Networks](https://mlsanity.com/paper/2203.01597/) (2022)
- [Coronary Artery Semantic Labeling using Edge Attention Graph Matching Network](https://mlsanity.com/paper/2305.12327/) (2023)

## Built on by (most cited first, 2 of 2)

- [RAU: Reference-based Anatomical Understanding with Vision Language Models](https://mlsanity.com/paper/2509.22404/) (2025)
- [CARDIAG: A Dense Segment Classification Benchmark of Deep Learning Architectures for Coronary Angiography](https://mlsanity.com/paper/2607.22139/) (2026)

## Abstract (arXiv)

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 and varying anatomy of arterial system between different projection view angles and patients. 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, considering the cycle consistency between each pair of graphs. As a result, the 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 using our multi-site dataset with 718 ICAs. With the semantic labeled arteries, an overall accuracy of 0.9155 was achieved for stenosis detection. The proposed MGM presents a novel tool for coronary artery analysis using multiple ICA-derived graphs, offering valuable insights into vascular health and pathology.