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Multiple Sclerosis (MS) is a chronic disease that affectsmillions of people across the globe. MS can critically affect different organsof the central nervous system such as the eyes, the spinal cord, and the brain.  Background: To help physicians in diagnosing MS lesions, computer-aidedmethods are widely used. In this regard, a considerable research has beencarried out in the area of automatic detection and segmentation of MS lesionsin magnetic resonance images (MRIs).  Methodology: In this study, we review the different approaches that have beenused in computer-aided detection and segmentation of MS lesions. Our reviewresulted in categorizing MS lesion segmentation approaches into six broadcategories: data-driven, statistical, supervised machine learning, unsupervisedmachine learning, fuzzy, and deep learning-based techniques. We criticallyanalyze the different techniques under these approaches and highlight theirstrengths and weaknesses.  Results: From the study, we observe that a considerable amount of work,around 25% of related literature, is focused on statistical-based MS lesionsegmentation techniques, followed by 21.15% for data-driven based methods,19.23% for deep learning and 15.38% for supervised methods.  Implication: The study points out the challenges/gaps to be addressed infuture research. The study shows the work which has been done in last onedecade in detection and segmentation of MS lesions. The results show that, inrecent years, deep learning methods are outperforming all the others methods.

A Bibliography of Multiple Sclerosis Lesions Detection Methods using Brain MRIs