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.

翻译：引言：多发性硬化是一种影响全球数百万人的慢性疾病。多发性硬化可严重损害中枢神经系统的不同器官，如眼睛、脊髓和大脑。背景：为辅助医生诊断多发性硬化病灶，计算机辅助方法被广泛应用。为此，在磁共振图像中多发性硬化病灶的自动检测与分割领域开展了大量研究。方法：本研究系统回顾了计算机辅助检测与分割多发性硬化病灶所采用的不同方法。通过综述分析，我们将多发性硬化病灶分割方法归纳为六大类：数据驱动方法、统计方法、有监督机器学习方法、无监督机器学习方法、模糊逻辑方法以及基于深度学习的方法。我们对这些方法下的不同技术进行批判性分析，并着重指出其优势与局限。结果：研究发现，约25%的相关文献聚焦于基于统计的多发性硬化病灶分割技术，数据驱动方法占21.15%，深度学习方法占19.23%，有监督方法占15.38%。启示：本研究指出了未来研究中需要解决的关键挑战与空白。通过梳理近十年来多发性硬化病灶检测与分割领域的研究成果，结果表明深度学习方法的性能已超越其他所有方法。