Segmentation and Classification of Multiple Sclerosis Using Deep Learning Networks: A Review

Abstract

The central nervous system is potentially disabled by multiple sclerosis, in which the myelin sheaths of neuron destroyed and cause communication problems between the brain and the rest of the body. Magnetic resonance imaging is used to track the new lesions and enlarged lesions. This is particularly challenging since the new lesions are very small and changes are often subtle. Lesion activity is determined by observing their tactile sensation and their position. MS lesion activity is used as a secondary endpoint in numerous MS clinical drug trials, and the detection of lesion activity between two-time points is a crucial biomarker since it decides the disease progression. Segmentation and classification of multiple sclerosis lesions are very important in helping MS diagnosis and patient disease follow-up. This paper reviews the deep learning networks for segmenting and classifying the brain tissues of multiple sclerosis patients through magnetic resonance images.