Multi-graph Graph matching for coronary artery semantic labeling in invasive coronary angiograms
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Published on: September 22, 2023
Chen Zhao1, Zhihui Xu2, Pukar Baral3
1Department of Computer Science, Kennesaw State University, Marietta GA, USA.
Insights
A novel multi-graph matching algorithm accurately labels coronary artery segments from invasive coronary angiography (ICA). This advancement improves stenosis detection, offering new insights into coronary artery disease (CAD) analysis.
Area of Science:
- Cardiovascular Imaging
- Artificial Intelligence in Medicine
- Graph Theory Applications
Background:
- Coronary artery disease (CAD) is a leading global cause of mortality.
- Invasive coronary angiography (ICA) is the standard for evaluating coronary anatomy.
- Deep learning methods struggle with semantic labeling of coronary artery segments due to anatomical variations and branch similarities.
Purpose of the Study:
- To develop an advanced algorithm for accurate semantic labeling of coronary artery segments.
- To improve the detection of stenosis in coronary arteries.
- To leverage graph modeling for enhanced coronary artery analysis.
Main Methods:
- Modeled the coronary vascular tree as a graph.
- Proposed a multi-graph matching (MGM) algorithm incorporating cycle consistency.
- Utilized anatomical graph structure, radiomics features, and semantic mapping.
Main Results:
- Achieved 0.9471 accuracy for coronary artery semantic labeling on a multi-site dataset (718 ICAs).
- Reached 0.9155 overall accuracy for stenosis detection using semantically labeled arteries.
- Demonstrated the effectiveness of MGM in handling varying arterial anatomy and projection angles.
Conclusions:
- The MGM algorithm offers a novel and accurate approach to coronary artery semantic labeling.
- This method enhances the analysis of coronary arteries derived from multiple ICAs.
- The findings provide valuable insights for understanding vascular health and pathology in CAD.
Abstract:
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 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.
