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EAGMN: Coronary artery semantic labeling using edge attention graph matching network.

Chen Zhao1, Zhihui Xu2, Guang-Uei Hung3

  • 1Department of Applied Computing, Michigan Technological University, Houghton, MI, USA.

Computers in Biology and Medicine
|September 19, 2023
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Summary

This study introduces the Edge Attention Graph Matching Network (EAGMN) for precise coronary artery semantic labeling from invasive coronary angiography (ICA) images. The EAGMN effectively addresses challenges in deep learning models for coronary artery disease (CAD) diagnosis.

Keywords:
Coronary artery diseaseCoronary artery semantic labelingGraph attentionGraph matchingInvasive coronary angiography

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Area of Science:

  • Cardiovascular Imaging and Intervention
  • Artificial Intelligence in Medicine
  • Medical Image Analysis

Background:

  • Coronary artery disease (CAD) is a leading global cause of death, necessitating accurate diagnosis through invasive coronary angiography (ICA).
  • Extracting individual arterial branches from ICA is critical for detecting stenosis and diagnosing CAD.
  • Deep learning models struggle with semantic segmentation of coronary arteries due to morphological similarities among vessels.

Purpose of the Study:

  • To propose an innovative approach, the Edge Attention Graph Matching Network (EAGMN), for accurate coronary artery semantic labeling.
  • To overcome the limitations of existing deep learning models in segmenting complex coronary artery structures.
  • To improve the efficiency and accuracy of CAD diagnosis by enhancing semantic labeling of coronary arteries.

Main Methods:

  • Developed the EAGMN, a novel deep learning model that compares arterial branches between two graphs derived from ICAs.
  • Represented arterial segments as nodes in individual graphs and utilized graph attention for feature embedding and aggregation.
  • Converted semantic segmentation into a graph node similarity comparison task to achieve node-to-node semantic mapping and labeling.

Main Results:

  • The EAGMN achieved a weighted accuracy of 0.8653, precision of 0.8656, recall of 0.8653, and F1-score of 0.8643 on a dataset of 263 labeled ICAs.
  • The model demonstrated effective semantic labeling of unlabeled coronary arterial segments based on learned node-to-node relationships.
  • Interpretability was provided using ZORRO to explain the graph matching process for artery semantic labeling.

Conclusions:

  • The EAGMN offers a promising solution for accurate and efficient coronary artery semantic labeling using ICA.
  • The model's approach of graph node similarity comparison effectively addresses challenges in segmenting similar arterial morphologies.
  • This technique has the potential to significantly improve CAD diagnosis and treatment planning.