Deep learning techniques for automated coronary artery segmentation and coronary artery disease detection: A systematic review of the last decade (2013-2024).

Journal: Computer methods and programs in biomedicine
Published Date: May 15, 2025

Abstract

BACKGROUND: Coronary artery disease (CAD) is the most common cardiovascular disease, exacting high morbidity and mortality worldwide. CAD is detected on coronary artery imaging; coronary artery segmentation (CAS) of the images is essential for coronary lesion characterization. Both CAD detection and CAS require expert input, are labor-intensive, and error-prone.

Authors

  • Suleyman Yaman
    Biomedical Department, Vocational School of Technical Sciences, Firat University, Elazig, Turkey; School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, QLD 4300, Australia.
  • Ozkan Aslan
    Computer Engineering Department, Engineering Faculty, Afyon Kocatepe University, Afyonkarahisar, Turkey.
  • Hasan Güler
    Electrical-Electronics Engineering Department, Engineering Faculty, Firat University, Elazig, Turkey.
  • Abdulkadir Şengür
    Electrical and Electronics Engineering Department, Firat University, Elazig, Turkey.
  • Abdul Hafeez-Baig
    School of Business, University of Southern Queensland, Toowoomba, QLD, Australia.
  • Ru-San Tan
    National Heart Centre Singapore, Singapore, Singapore.
  • Ravinesh C Deo
    School of Agricultural Computational and Environmental Sciences, International Centre of Applied Climate Science (ICACS), University of Southern Queensland, Springfield, QLD, 4300, Australia. ravinesh.deo@usq.edu.au.
  • Prabal Datta Barua
    Cogninet Australia, Sydney, NSW 2010 Australia.
  • U Rajendra Acharya
    School of Business (Information Systems), Faculty of Business, Education, Law & Arts, University of Southern Queensland, Darling Heights, Australia.

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