  Lesion segmentation is a core task for quantitative analysis of MRI scans of
Multiple Sclerosis patients. The recent success of deep learning techniques in
a variety of medical image analysis applications has renewed community interest
in this challenging problem and led to a burst of activity for new algorithm
development. In this survey, we investigate the supervised CNN-based methods
for MS lesion segmentation. We decouple these reviewed works into their
algorithmic components and discuss each separately. For methods that provide
evaluations on public benchmark datasets, we report comparisons between their
results.
