Subject-Based Transfer Learning in Longitudinal Multiple Sclerosis Lesion Segmentation.

Journal: Journal of neuroimaging : official journal of the American Society of Neuroimaging
PMID: 39923192

Abstract

BACKGROUND AND PURPOSE: Accurate and consistent lesion segmentation from magnetic resonance imaging is required for longitudinal multiple sclerosis (MS) data analysis. In this work, we propose two new transfer learning-based pipelines to improve segmentation performance for subjects in longitudinal MS datasets.

Authors

  • Sibaji Gaj
    Department of Biomedical Engineering, Cleveland Clinic, Cleveland, Ohio, United States of America.
  • Bhaskar Thoomukuntla
    Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, USA.
  • Daniel Ontaneda
    Mellen Center for Multiple Sclerosis, Cleveland Clinic, Cleveland, Ohio, United States of America.
  • Kunio Nakamura
    Department of Biomedical Engineering, Cleveland Clinic, Cleveland, Ohio, United States of America.

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