Deep Learning Corpus Callosum Segmentation as a Neurodegenerative Marker in Multiple Sclerosis.

Journal: Journal of neuroimaging : official journal of the American Society of Neuroimaging
Published Date: Feb 15, 2021

Abstract

BACKGROUND AND PURPOSE: Corpus callosum atrophy is a sensitive biomarker of multiple sclerosis (MS) neurodegeneration but typically requires manual 2D or volumetric 3D-based segmentations. We developed a supervised machine learning algorithm, DeepnCCA, for corpus callosum segmentation and relate callosal morphology to clinical disability using conventional MRI scans collected in clinical routine.

Authors

  • 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.
  • Irene Brusini
    School of Engineering Sciences in Chemistry, Biotechnology and Health, Royal Institute of Technology, Stockholm, Sweden.
  • Olle Andersson
    School of Engineering Sciences in Chemistry, Biotechnology and Health, Royal Institute of Technology, Stockholm, Sweden.
  • 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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