多发性硬化病变脑MRI检测方法文献
A Bibliography of Multiple Sclerosis Lesions Detection Methods using Brain MRIs
摘要：
多发性硬化症（MS）是一种慢性疾病，影响全球数百万人。MS可以严重影响中枢神经系统的不同器官，如眼睛，脊髓和大脑。背景：为了帮助医生诊断MS病变，计算机辅助方法被广泛使用。在这方面，已经在磁共振图像（MRI）中的MS病变的自动检测和分割领域中进行了相当多的研究。方法：在这项研究中，我们回顾了不同的方法，已被用于计算机辅助检测和分割的MS病变。我们的综述将MS病变分割方法分为六大类：数据驱动、统计、监督机器学习、无监督机器学习、模糊和基于深度学习的技术。我们批判性地分析了这些方法下的不同技术，并强调了它们的优点和缺点。结果如下：从这项研究中，我们观察到相当多的工作（约25%的相关文献）集中在基于解剖学的MS病变分割技术上，其次是基于数据驱动的方法，占21.15%，深度学习占19.23%，监督方法占15.38%。启示：该研究指出了未来研究中需要解决的挑战/差距。该研究显示了在过去十年中在MS病变的检测和分割方面所做的工作。结果表明，近年来，深度学习方法的表现优于所有其他方法。
英文摘要：
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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