  Multiple sclerosis (MS) is a chronic inflammatory and degenerative disease of
the central nervous system, characterized by the appearance of focal lesions in
the white and gray matter that topographically correlate with an individual
patient's neurological symptoms and signs. Magnetic resonance imaging (MRI)
provides detailed in-vivo structural information, permitting the quantification
and categorization of MS lesions that critically inform disease management.
Traditionally, MS lesions have been manually annotated on 2D MRI slices, a
process that is inefficient and prone to inter-/intra-observer errors.
Recently, automated statistical imaging analysis techniques have been proposed
to extract and segment MS lesions based on MRI voxel intensity. However, their
effectiveness is limited by the heterogeneity of both MRI data acquisition
techniques and the appearance of MS lesions. By learning complex lesion
representations directly from images, deep learning techniques have achieved
remarkable breakthroughs in the MS lesion segmentation task. Here, we provide a
comprehensive review of state-of-the-art automatic statistical and
deep-learning MS segmentation methods and discuss current and future clinical
applications. Further, we review technical strategies, such as domain
adaptation, to enhance MS lesion segmentation in real-world clinical settings.
