Background

Deep grey matter (DGM) atrophy in multiple sclerosis (MS) and its relation to cognitive and clinical decline requires accurate measurements. MS pathology may deteriorate the performance of automated segmentation methods. Accuracy of DGM segmentation methods is compared between MS and controls, and the relation of performance with lesions and atrophy is studied.

Methods

On images of 21 MS subjects and 11 controls, three raters manually outlined caudate nucleus, putamen and thalamus; outlines were combined by majority voting. FSL-FIRST, FreeSurfer, Geodesic Information Flow and volBrain were evaluated. Performance was evaluated volumetrically (intra-class correlation coefficient (ICC)) and spatially (Dice similarity coefficient (DSC)). Spearman's correlations of DSC with global and local lesion volume, structure of interest volume (ROIV), and normalized brain volume (NBV) were assessed.

Results

ICC with manual volumes was mostly good and spatial agreement was high. MS exhibited significantly lower DSC than controls for thalamus and putamen. For some combinations of structure and method, DSC correlated negatively with lesion volume or positively with NBV or ROIV. Lesion-filling did not substantially change segmentations.

Conclusions

Automated methods have impaired performance in patients. Performance generally deteriorated with higher lesion volume and lower NBV and ROIV, suggesting that these may contribute to the impaired performance.

中文翻译：

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多发性硬化症中MRI深层灰质分割的准确性降低：在多中心队列中针对手动参考分割的四种自动化方法的评估。

背景

多发性硬化症（MS）中的深灰质（DGM）萎缩及其与认知和临床衰退的关系需要精确的测量。MS病理可能会降低自动分割方法的性能。比较了MS和对照之间DGM分割方法的准确性，并研究了其与病变和萎缩之间的关系。

方法

在21位MS受试者和11位对照的图像上，三个评估者手动勾勒出尾状核，壳壳和丘脑；大纲以多数表决通过。评估了FSL-FIRST，FreeSurfer，大地测量信息流和volBrain。通过体积（类内相关系数（ICC））和空间（骰子相似系数（DSC））评估性能。评估了DSC与整体和局部病变体积，感兴趣的体积结构（ROIV）和标准化的大脑体积（NBV）的Spearman相关性。

结果

带有手动卷的ICC总体上不错，空间一致性也很高。MS表现出的DSC明显低于对照组的丘脑和壳核。对于结构和方法的某些组合，DSC与病变体积负相关，或与NBV或ROIV正相关。病变填充基本上没有改变分割。

结论

自动化方法会损害患者的表现。随着病变体积的增加以及NBV和ROIV的降低，性能通常会下降，这表明这些可能会导致性能受损。