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Review of automatic segmentation methods of multiple sclerosis white matter lesions on conventional magnetic resonance imaging

机译：传统磁共振成像对多发性硬化症白质病变自动分割方法的综述

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Magnetic resonance (MR) imaging is often used to characterize and quantify multiple sclerosis (MS) lesions in the brain and spinal cord. The number and volume of lesions have been used to evaluate MS disease burden, to track the progression of the disease and to evaluate the effect of new pharmaceuticals in clinical trials. Accurate identification of MS lesions in MR images is extremely difficult due to variability in lesion location, size and shape in addition to anatomical variability between subjects. Since manual segmentation requires expert knowledge, is time consuming and is subject to intra- and inter-expert variability, many methods have been proposed to automatically segment lesions.The objective of this study was to carry out a systematic review of the literature to evaluate the state of the art in automated multiple sclerosis lesion segmentation. From 1240. hits found initially with PubMed and Google scholar, our selection criteria identified 80 papers that described an automatic lesion segmentation procedure applied to MS. Only 47 of these included quantitative validation with at least one realistic image. In this paper, we describe the complexity of lesion segmentation, classify the automatic MS lesion segmentation methods found, and review the validation methods applied in each of the papers reviewed. Although many segmentation solutions have been proposed, including some with promising results using MRI data obtained on small groups of patients, no single method is widely employed due to performance issues related to the high variability of MS lesion appearance and differences in image acquisition. The challenge remains to provide segmentation techniques that work in all cases regardless of the type of MS, duration of the disease, or MRI protocol, and this within a comprehensive, standardized validation framework. MS lesion segmentation remains an open problem.
机译：磁共振（MR）成像通常用于表征和量化大脑和脊髓中的多发性硬化（MS）病变。病变的数量和体积已用于评估MS疾病负担，追踪疾病进展并评估新药物在临床试验中的作用。由于病变位置，大小和形状的可变性以及受试者之间的解剖学可变性，因此很难准确识别MR图像中的MS病变。由于手动分割需要专家知识，耗时且受专家内部和专家间差异的影响，因此提出了许多自动分割病变的方法。本研究的目的是对文献进行系统的回顾以评估病变的程度。自动多发性硬化病变分割的最新技术。从1240年最初由PubMed和Google学者发现的命中数据开始，我们的选择标准确定了80篇论文，这些论文描述了应用于MS的自动病变分割程序。其中只有47个包含了至少一张逼真的图像的定量验证。在本文中，我们描述了病灶分割的复杂性，对发现的自动MS病灶分割方法进行了分类，并回顾了每篇综述中所采用的验证方法。尽管已经提出了许多分割解决方案，包括一些使用在小群患者身上获得的MRI数据获得的有希望的结果，但是由于与MS病变外观的高变异性和图像采集差异有关的性能问题，没有一种方法被广泛采用。面临的挑战仍然是提供一种在任何情况下都可以使用的分割技术，无论MS的类型，疾病的持续时间或MRI方案如何，而且要在一个全面，标准化的验证框架内进行。 MS病变分割仍然是一个未解决的问题。

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