An accurate and time-efficient deep learning-based system for automated segmentation and reporting of cardiac magnetic resonance-detected ischemic scar.

Journal: Computer methods and programs in biomedicine
PMID: 36586175

Abstract

BACKGROUND AND OBJECTIVES: Myocardial infarction scar (MIS) assessment by cardiac magnetic resonance provides prognostic information and guides patients' clinical management. However, MIS segmentation is time-consuming and not performed routinely. This study presents a deep-learning-based computational workflow for the segmentation of left ventricular (LV) MIS, for the first time performed on state-of-the-art dark-blood late gadolinium enhancement (DB-LGE) images, and the computation of MIS transmurality and extent.

Authors

  • Daniele M Papetti
    Department of Informatics, Systems and Communication, University of Milano-Bicocca, Viale Sarca 336, Milano 20126, Italy.
  • Kirsten Van Abeelen
    Department of Medicine and Surgery, University of Milano-Bicocca, Milan 20126, Italy.
  • Rhodri Davies
    Institute for Cardiovascular Science, University College London, United Kingdom
  • Roberto Menè
    Department of Medicine and Surgery, University of Milano-Bicocca, 20100 Milan, Italy.
  • Francesca Heilbron
    Department of Cardiology, IRCCS Istituto Auxologico Italiano, Via Magnasco 2, Milan 20145, Italy.
  • Francesco P Perelli
    Department of Medicine and Surgery, University of Milano-Bicocca, Milan 20126, Italy; Department of Cardiology, IRCCS Istituto Auxologico Italiano, Via Magnasco 2, Milan 20145, Italy.
  • Jessica Artico
    Barts Heart CentreBarts Health NHS Trust London United Kingdom.
  • Andreas Seraphim
    Cardiac Imaging Department, Barts Heart Centre, St Bartholomew's Hospital, London, UK; Institute of Cardiovascular Science, University College London, London, UK.
  • James C Moon
    Cardiac Imaging Department, Barts Heart Centre, St Bartholomew's Hospital, London, UK; Institute of Cardiovascular Science, University College London, London, UK. Electronic address: j.moon@ucl.ac.uk.
  • Gianfranco Parati
    Department of Medicine and Surgery, University of Milano-Bicocca; Milan-Italy.
  • Hui Xue
    Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, People's Republic of China.
  • Peter Kellman
    National Heart, Lung, and Blood Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, MD (H.X., P.K.).
  • Luigi P Badano
    Department of Cardiac, Thoracic and Vascular Sciences, University of Padua, Padua, Italy.
  • Daniela Besozzi
    Department of Informatics, Systems and Communication, University of Milano-Bicocca, Viale Sarca 336, Milano 20126, Italy; Bicocca Bioinformatics Biostatistics and Bioimaging Centre (B4), University of Milano-Bicocca, Vedano al Lambro 20854, Italy. Electronic address: daniela.besozzi@unimib.it.
  • Marco S Nobile
    Bicocca Bioinformatics Biostatistics and Bioimaging Centre (B4), University of Milano-Bicocca, Vedano al Lambro 20854, Italy; Department of Environmental Sciences, Informatics and Statistics, Ca' Foscari University of Venice, Via Torino 155, Mestre, Venice 30172, Italy. Electronic address: marco.nobile@unive.it.
  • Camilla Torlasco
    Cardiovascular Imaging Unit, Department of Cardiovascular, Neural and Metabolic Sciences, Instituto Auxologico Italiano, IRCCS; Milan-Italy.

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