Real-time coronary artery segmentation in CAG images: A semi-supervised deep learning strategy.

Journal: Artificial intelligence in medicine
Published Date: May 9, 2024

Abstract

BACKGROUND: When treating patients with coronary artery disease and concurrent renal concerns, we often encounter a conundrum: how to achieve a clearer view of vascular details while minimizing the contrast and radiation doses during percutaneous coronary intervention (PCI). Our goal is to use deep learning (DL) to create a real-time roadmap for guiding PCI. To this end, segmentation, a critical first step, paves the way for detailed vascular analysis. Unlike traditional supervised learning, which demands extensive labeling time and manpower, our strategy leans toward semi-supervised learning. This method not only economizes on labeling efforts but also aims at reducing contrast and radiation exposure.

Authors

  • Chih-Kuo Lee
    Department of Internal Medicine, National Taiwan University HsinChu Hospital, HsinChu, Taiwan.
  • Jhen-Wei Hong
    Department of Electronic Engineering, National Taipei University of Technology, No. 1, Sec. 3, Chunghsiao E. Rd., Taipei City 10608, Taiwan.
  • Chia-Ling Wu
    Sarepta Therapeutics, Cambridge, MA, USA.
  • Jia-Ming Hou
    Department of Electronic Engineering, National Taipei University of Technology, No. 1, Sec. 3, Chunghsiao E. Rd., Taipei City 10608, Taiwan.
  • Yen-An Lin
    Department of Electronic Engineering, National Taipei University of Technology, No. 1, Sec. 3, Chunghsiao E. Rd., Taipei City 10608, Taiwan.
  • Kuan-Chih Huang
    Graduate Institute of Clinical Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan; Heart Center, Cheng-Hsin General Hospital, Taipei, Taiwan.
  • Po-Hsuan Tseng
    Department of Electronic Engineering, National Taipei University of Technology, No. 1, Sec. 3, Chunghsiao E. Rd., Taipei City 10608, Taiwan. Electronic address: phtseng@ntut.edu.tw.

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