Fully‑automated deep‑learning segmentation of pediatric cardiovascular magnetic resonance of patients with complex congenital heart diseases.

Journal: Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance
Published Date: Nov 30, 2020

Abstract

BACKGROUND: For the growing patient population with congenital heart disease (CHD), improving clinical workflow, accuracy of diagnosis, and efficiency of analyses are considered unmet clinical needs. Cardiovascular magnetic resonance (CMR) imaging offers non-invasive and non-ionizing assessment of CHD patients. However, although CMR data facilitates reliable analysis of cardiac function and anatomy, clinical workflow mostly relies on manual analysis of CMR images, which is time consuming. Thus, an automated and accurate segmentation platform exclusively dedicated to pediatric CMR images can significantly improve the clinical workflow, as the present work aims to establish.

Authors

  • Saeed Karimi-Bidhendi
    Center for Pervasive Communications and Computing, University of California, Irvine, Irvine, USA.
  • Arghavan Arafati
    The Edwards Lifesciences Center for Advanced Cardiovascular Technology, University of California, 2410 Engineering Hall, Irvine, CA 92697-2730, USA.
  • Andrew L Cheng
    The Keck School of Medicine, University of Southern California and Children's Hospital Los Angeles, Los Angeles, USA.
  • Yilei Wu
    Center for Pervasive Communications and Computing, University of California, Irvine, Irvine, USA.
  • Arash Kheradvar
    The Edwards Lifesciences Center for Advanced Cardiovascular Technology, University of California, Irvine, USA. Electronic address: arashkh@uci.edu.
  • Hamid Jafarkhani
    Center for Pervasive Communications and Computing, University of California, Irvine, USA. Electronic address: hamidj@uci.edu.

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