A Bibliography of Multiple Sclerosis Lesions Detection Methods using Brain MRIs

Atif Shah; Maged S. Al-Shaibani; Moataz Ahmad; Reem Bunyan

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arXiv · 2302.09516

A Bibliography of Multiple Sclerosis Lesions Detection Methods using Brain MRIs

Atif Shah·Maged S. Al-Shaibani·Moataz Ahmad·Reem Bunyan

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Abstract

Introduction: Multiple Sclerosis (MS) is a chronic disease that affects millions of people across the globe. MS can critically affect different organs of the central nervous system such as the eyes, the spinal cord, and the brain. Background: To help physicians in diagnosing MS lesions, computer-aided methods are widely used. In this regard, a considerable research has been carried out in the area of automatic detection and segmentation of MS lesions in magnetic resonance images (MRIs). Methodology: In this study, we review the different approaches that have been used in computer-aided detection and segmentation of MS lesions. Our review resulted in categorizing MS lesion segmentation approaches into six broad categories: data-driven, statistical, supervised machine learning, unsupervised machine learning, fuzzy, and deep learning-based techniques. We critically analyze the different techniques under these approaches and highlight their strengths 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 lesion segmentation 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 in future research. The study shows the work which has been done in last one decade in detection and segmentation of MS lesions. The results show that, in recent years, deep learning methods are outperforming all the others methods.

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Atif Shah, Maged S. Al-Shaibani, Moataz Ahmad, Reem Bunyan. 2023-02-19. A Bibliography of Multiple Sclerosis Lesions Detection Methods using Brain MRIs. https://arxiv.org/abs/2302.09516

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