Deep learning-based atherosclerotic coronary plaque segmentation on coronary CT angiography.

Journal: European radiology
Published Date: May 7, 2022

Abstract

OBJECTIVES: Volumetric evaluation of coronary artery disease (CAD) allows better prediction of cardiac events. However, CAD segmentation is labor intensive. Our objective was to create an open-source deep learning (DL) model to segment coronary plaques on coronary CT angiography (CCTA).

Authors

  • Natasa Jávorszky
    MTA-SE Cardiovascular Imaging Research Group, Heart and Vascular Center, Semmelweis University, 68 Városmajor Street, Budapest, Hungary.
  • Bálint Homonnay
    Hyperplane Szoftverfejlesző Ltd., 15/d Bartók Béla str., Budapest, 1114, Hungary.
  • Gary Gerstenblith
    Department of Medicine, Johns Hopkins University School of Medicine, 733 N Broadway, Baltimore, MD, 21205, USA.
  • David Bluemke
    University of Wisconsin School of Medicine and Public Health, 750 Highland Ave, Madison, WI, 53726, USA.
  • Péter Kiss
    Centre for Discrete Mathematics and its Applications, University of Warwick, 6 Lord Bhattacharyya Way, Coventry, CV4 7EZ, UK.
  • Mihály Török
    Lain Consulting Ltd., 2/c Kék Golyó str., Budapest, 1123, Hungary.
  • David Celentano
    Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, 614 Wolfe N Wolfe St., Baltimore, MD, 21205, USA.
  • Hong Lai
    School of Software Engineering, Shenzhen Institute of Information Technology, Shenzhen 518172, China. Electronic address: laih@sziit.edu.cn.
  • Shenghan Lai
    Department of Medicine, Johns Hopkins University School of Medicine, 733 N Broadway, Baltimore, MD, 21205, USA. slai@ihv.umaryland.edu.
  • Márton Kolossváry
    Cardiovascular Imaging Research Group, Heart and Vascular Center, Semmelweis University, Budapest, Hungary. Electronic address: marton.kolossvary@cirg.hu.

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