Automated biventricular quantification in patients with repaired tetralogy of Fallot using a three-dimensional deep learning segmentation model.

Journal: Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance
PMID: 39270800

Abstract

BACKGROUND: Deep learning is the state-of-the-art approach for automated segmentation of the left ventricle (LV) and right ventricle (RV) in cardiovascular magnetic resonance (CMR) images. However, these models have been mostly trained and validated using CMR datasets of structurally normal hearts or cases with acquired cardiac disease, and are therefore not well-suited to handle cases with congenital cardiac disease such as tetralogy of Fallot (TOF). We aimed to develop and validate a dedicated model with improved performance for LV and RV cavity and myocardium quantification in patients with repaired TOF.

Authors

  • Sofie Tilborghs
    Department of Electrical Engineering, Division of Processing Speech and Images (ESAT/PSI), KU Leuven, Leuven, Belgium; Medical Imaging Research Center, UZ Leuven, Leuven, Belgium.
  • Tiffany Liang
    Connecticut Children's Innovation Center, University of Connecticut School of Medicine, Farmington, Connecticut 06032, United States.
  • Stavroula Raptis
    Division of Cardiology, Peter Munk Cardiac Centre, University of Toronto, Toronto, Canada.
  • Ayako Ishikita
    Division of Cardiology, Peter Munk Cardiac Centre, University of Toronto, Toronto, Canada.
  • Werner Budts
    Department of Cardiovascular Sciences, KU Leuven, Leuven, Belgium.
  • Tom Dresselaers
    Department of Imaging and Pathology, Division of Radiology, KU Leuven, Leuven, Belgium.
  • Jan Bogaert
    Department of Imaging and Pathology, Division of Radiology, KU Leuven, Leuven, Belgium.
  • Frederik Maes
    Department of Electrical Engineering (ESAT/PSI), KU Leuven, Kasteelpark Arenberg 10/2446, 3001, Leuven, Belgium; Medical Imaging Research Center (MIRC), UZ Leuven, Herestraat 49, 3000, Leuven, Belgium. Electronic address: frederik.maes@kuleuven.be.
  • Rachel M Wald
    University of Toronto, Toronto, ON, Canada.
  • Alexander Van de Bruaene
    KU Leuven, Leuven, Belgium.

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