Automatic deep learning multicontrast corpus callosum segmentation in multiple sclerosis.

Journal: Journal of neuroimaging : official journal of the American Society of Neuroimaging
Published Date: May 1, 2022

Abstract

BACKGROUND AND PURPOSE: Corpus callosum (CC) atrophy is predictive of future disability in multiple sclerosis (MS). However, current segmentation methods are either labor- or computationally intensive. We therefore developed an automated deep learning-based CC segmentation tool and hypothesized that its output would correlate with disability.

Authors

  • Irene Brusini
    School of Engineering Sciences in Chemistry, Biotechnology and Health, Royal Institute of Technology, Stockholm, Sweden.
  • Michael Platten
    Neurology Clinic, Heidelberg University Hospital, Heidelberg, Germany; German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany; Department of Neurology, Mannheim Medical Center, University of Heidelberg, Mannheim, Germany.
  • Russell Ouellette
    Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden; Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, USA.
  • Fredrik Piehl
    Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.
  • Chunliang Wang
    School of Technology and Health, KTH Royal Institute of Technology, Stockholm, Sweden.
  • Tobias Granberg
    Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden; Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, USA.

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