Global and Regional Deep Learning Models for Multiple Sclerosis Stratification From MRI.

Journal: Journal of magnetic resonance imaging : JMRI
Published Date: Oct 6, 2023

Abstract

BACKGROUND: The combination of anatomical MRI and deep learning-based methods such as convolutional neural networks (CNNs) is a promising strategy to build predictive models of multiple sclerosis (MS) prognosis. However, studies assessing the effect of different input strategies on model's performance are lacking.

Authors

  • Llucia Coll
    Research Institute of Computer Vision and Robotics, University of Girona, Girona, Spain.
  • Deborah Pareto
    Magnetic Resonance Unit, Dept of Radiology, Vall d'Hebron University Hospital, Spain.
  • Pere Carbonell-Mirabent
    Multiple Sclerosis Centre of Catalonia (Cemcat), Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain.
  • Álvaro Cobo-Calvo
    Multiple Sclerosis Centre of Catalonia (Cemcat), Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain.
  • Georgina Arrambide
    Multiple Sclerosis Centre of Catalonia (Cemcat), Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain.
  • Ángela Vidal-Jordana
    Multiple Sclerosis Centre of Catalonia (Cemcat), Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain.
  • Manuel Comabella
    Multiple Sclerosis Centre of Catalonia (Cemcat), Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain.
  • Joaquín Castilló
    Multiple Sclerosis Centre of Catalonia (Cemcat), Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain.
  • Breogán Rodrı Guez-Acevedo
    Multiple Sclerosis Centre of Catalonia (Cemcat), Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain.
  • Ana Zabalza
    Multiple Sclerosis Centre of Catalonia (Cemcat), Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain.
  • Ingrid Galán
    Multiple Sclerosis Centre of Catalonia (Cemcat), Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain.
  • Luciana Midaglia
    Multiple Sclerosis Centre of Catalonia (Cemcat), Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain.
  • Carlos Nos
    Multiple Sclerosis Centre of Catalonia (Cemcat), Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain.
  • Cristina Auger
    Section of Neuroradiology, Department of Radiology (IDI), Vall d'Hebron University Hospital, Spain, Universitat Autònoma de Barcelona, Barcelona, Spain.
  • Manel Alberich
    Section of Neuroradiology, Department of Radiology (IDI), Vall d'Hebron University Hospital, Spain, Universitat Autònoma de Barcelona, Barcelona, Spain.
  • Jordi Río
    Multiple Sclerosis Centre of Catalonia (Cemcat), Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain.
  • Jaume Sastre-Garriga
    Multiple Sclerosis Centre of Catalonia (Cemcat), Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain.
  • Arnau Oliver
    Research institute of Computer Vision and Robotics, University of Girona, Spain.
  • Xavier Montalbán
    Hospital Universitari Vall d'Hebron, Barcelona, Spain.
  • Àlex Rovira
    Magnetic Resonance Unit, Dept of Radiology, Vall d'Hebron University Hospital, Spain.
  • Mar Tintoré
    Hospital Vall d'Hebron, Barcelona, Spain.
  • Xavier Lladó
    Research institute of Computer Vision and Robotics, University of Girona, Spain.
  • Carmen Tur
    Centre d'Esclerosi Múltiple de Catalunya (Cemcat), Barcelona, Spain; Queen Square Multiple Sclerosis Centre, UCL Queen Square Institute of Neurology, University College London, London, UK.

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