Semantic Segmentation to Extract Coronary Arteries in Invasive Coronary Angiograms

Authors

  • Chen Zhao Michigan Technological University, Houghton, MI, 49931, USA
  • Robert Bober Ochsner Medical Center, New Orleans, LA, 70121, USA
  • Haipeng Tang University of Southern Mississippi, Hattiesburg, MS, 39406, USA
  • Jinshan Tang George Mason University, Fairfax, VA, 22030, USA https://orcid.org/0000-0001-7266-8534
  • Minghao Dong Zhengzhou University of Light Industry, Zhengzhou, Henan, China
  • Chaoyang Zhang University of Southern Mississippi, Hattiesburg, MS, 39406, USA
  • Zhuo He Michigan Technological University, Houghton, MI, 49931, USA
  • Michele L. Esposito Tufts Medical Center, Boston, MA, 02111, USA
  • Zhihui Xu The First Affiliated Hospital of Nanjing Medical University, Nanjing, China https://orcid.org/0000-0002-9944-3770
  • Weihua Zhou Michigan Technological University, Houghton, MI, 49931, USA

DOI:

https://doi.org/10.15377/2409-5761.2022.09.6

Keywords:

Binary Vascular Tree, Support Vector Machine, Coronary Artery Disease, Image Semantic Segmentation, Invasive Coronary Angiography

Abstract

Accurate semantic segmentation of each coronary artery using invasive coronary angiography (ICA) is important for stenosis assessment and coronary artery disease (CAD) diagnosis. In this paper, we propose a multi-step semantic segmentation algorithm based on analyzing arterial segments extracted from ICAs. The proposed algorithm firstly extracts the entire arterial binary mask (binary vascular tree) using a deep learning-based method. Then we extract the centerline of the binary vascular tree and separate it into different arterial segments. Finally, by extracting the underlying arterial topology, position, and pixel features, we construct a powerful coronary artery segment classifier based on a support vector machine. Each arterial segment is classified into the left coronary artery (LCA), left anterior descending (LAD), and other types of arterial segments. The proposed method was tested on a dataset with 225 ICAs and achieved a mean accuracy of 70.33% for the multi-class artery classification and a mean intersection over union of 0.6868 for semantic segmentation of arteries. The experimental results show the effectiveness of the proposed algorithm, which provides impressive performance for analyzing the individual arteries in ICAs.

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Author Biographies

  • Chen Zhao, Michigan Technological University, Houghton, MI, 49931, USA

    Department of Applied Computing

  • Robert Bober, Ochsner Medical Center, New Orleans, LA, 70121, USA

    Department of Cardiology

  • Haipeng Tang, University of Southern Mississippi, Hattiesburg, MS, 39406, USA

    School of Computing Sciences and Computer Engineering

  • Jinshan Tang, George Mason University, Fairfax, VA, 22030, USA

    Department of Health Administration and Policy

  • Minghao Dong, Zhengzhou University of Light Industry, Zhengzhou, Henan, China

    School of Computer and Communication Engineering

  • Chaoyang Zhang, University of Southern Mississippi, Hattiesburg, MS, 39406, USA

    School of Computing Sciences and Computer Engineering

  • Zhuo He, Michigan Technological University, Houghton, MI, 49931, USA

    Department of Applied Computing

  • Michele L. Esposito, Tufts Medical Center, Boston, MA, 02111, USA

    Department of Cardiology

  • Zhihui Xu, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China

    Department of Cardiology

  • Weihua Zhou, Michigan Technological University, Houghton, MI, 49931, USA

    Department of Applied Computing

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Published

2022-05-24

Issue

Vol. 9 (2022)

Section

AI & Metaheuristic Optimization Methods in Engineering & Biomedical Application

License

Copyright (c) 2022 Chen Zhao, Robert Bober, Haipeng Tang, Jinshan Tang, Minghao Dong, Chaoyang Zhang, Zhuo He, Michele L. Esposito, Zhihui Xu, Weihua Zhou

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

All the published articles are licensed under the terms of the Creative Commons Attribution Non-Commercial License (CC BY-NC 4.0) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.

How to Cite

Semantic Segmentation to Extract Coronary Arteries in Invasive Coronary Angiograms. (2022). Journal of Advances in Applied & Computational Mathematics, 9, 76-85. https://doi.org/10.15377/2409-5761.2022.09.6

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