Anatomically informed deep learning on contrast-enhanced cardiac magnetic resonance imaging for scar segmentation and clinical feature extraction.

Journal: Cardiovascular digital health journal
Published Date: Nov 26, 2021

Abstract

BACKGROUND: Visualizing fibrosis on cardiac magnetic resonance (CMR) imaging with contrast enhancement (late gadolinium enhancement; LGE) is paramount in characterizing disease progression and identifying arrhythmia substrates. Segmentation and fibrosis quantification from LGE-CMR is intensive, manual, and prone to interobserver variability. There is an unmet need for automated LGE-CMR image segmentation that ensures anatomical accuracy and seamless extraction of clinical features.

Authors

  • Dan M Popescu
    Alliance for Cardiovascular Diagnostic and Treatment Innovation (ADVANCE), Johns Hopkins University, Baltimore, Maryland.
  • Haley G Abramson
    Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland.
  • Rebecca Yu
    Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland.
  • Changxin Lai
    Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland.
  • Julie K Shade
    Alliance for Cardiovascular Diagnostic and Treatment Innovation (ADVANCE), Johns Hopkins University, Baltimore, Maryland.
  • Katherine C Wu
    Division of Cardiology, Johns Hopkins University Department of Medicine, Baltimore, Maryland, USA.
  • Mauro Maggioni
    Alliance for Cardiovascular Diagnostic and Treatment Innovation (ADVANCE), Johns Hopkins University, Baltimore, Maryland.
  • Natalia A Trayanova
    Alliance for Cardiovascular Diagnostic and Treatment Innovation (ADVANCE), Johns Hopkins University, Baltimore, Maryland.

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