McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, Canada; Douglas Mental Health University Institute, McGill University, Montreal, Canada; McGill Centre for Integrative Neuroscience, McGill University, Montreal, Canada; Ludmer Centre for Neuroinformatics and Mental Health, Montreal, Canada.
Douglas Mental Health University Institute, McGill University, Montreal, Canada.
Neuroimage. 2018 Apr 15;170:182-198. doi: 10.1016/j.neuroimage.2017.02.069. Epub 2017 Mar 1.
Accurate automated quantification of subcortical structures is a greatly pursued endeavour in neuroimaging. In an effort to establish the validity and reliability of these methods in defining the striatum, globus pallidus, and thalamus, we investigated differences in volumetry between manual delineation and automated segmentations derived by widely used FreeSurfer and FSL packages, and a more recent segmentation method, the MAGeT-Brain algorithm. In a first set of experiments, the basal ganglia and thalamus of thirty subjects (15 first episode psychosis [FEP], 15 controls) were manually defined and compared to the labels generated by the three automated methods. Our results suggest that all methods overestimate volumes compared to the manually derived "gold standard", with the least pronounced differences produced using MAGeT. The least between-method variability was noted for the striatum, whereas marked differences between manual segmentation and MAGeT compared to FreeSurfer and FSL emerged for the globus pallidus and thalamus. Correlations between manual segmentation and automated methods were strongest for MAGeT (range: 0.51 to 0.92; p<0.01, corrected), whereas FreeSurfer and FSL showed moderate to strong Pearson correlations (range 0.44-0.86; p<0.05, corrected), with the exception of FreeSurfer pallidal (r=0.31, p=0.10) and FSL thalamic segmentations (r=0.37, p=0.051). Bland-Altman plots highlighted a tendency for greater volumetric differences between manual labels and automated methods at the lower end of the distribution (i.e. smaller structures), which was most prominent for bilateral thalamus across automated pipelines, and left globus pallidus for FSL. We then went on to examine volume and shape of the basal ganglia structures using automated techniques in 135 FEP patients and 88 controls. The striatum and globus pallidus were significantly larger in FEP patients compared to controls bilaterally, irrespective of the method used. MAGeT-Brain was more sensitive to shape-based group differences, and uncovered widespread surface expansions in the striatum and globus pallidus bilaterally in FEP patients compared to controls, and surface contractions in bilateral thalamus (FDR-corrected). By contrast, after using a recommended cluster-wise thresholding method, FSL only detected differences in the right ventral striatum (FEP>Control) and one cluster of the left thalamus (Control>FEP). These results suggest that different automated pipelines segment subcortical structures with varying degrees of variability compared to manual methods, with particularly pronounced differences found with FreeSurfer and FSL for the globus pallidus and thalamus.
准确的皮质下结构的自动定量是神经影像学中一个非常追求的目标。为了确定这些方法在定义纹状体、苍白球和丘脑方面的有效性和可靠性，我们研究了手动勾画和广泛使用的 FreeSurfer 和 FSL 包以及一种较新的分割方法 MAGeT-Brain 算法自动分割之间的体积差异。在第一组实验中，对 30 名受试者（15 名首发精神病[FEP]，15 名对照）的基底节和丘脑进行了手动定义，并与三种自动方法生成的标签进行了比较。我们的结果表明，与手动衍生的“金标准”相比，所有方法都高估了体积，而使用 MAGeT 产生的差异最小。在纹状体中观察到的组间差异最小，而与手动分割和 MAGeT 相比，在苍白球和丘脑之间出现了明显的差异。手动分割与自动方法之间的相关性最强的是 MAGeT（范围：0.51 至 0.92；p<0.01，校正），而 FreeSurfer 和 FSL 显示出中度至强 Pearson 相关性（范围 0.44-0.86；p<0.05，校正），除了 FreeSurfer 苍白球（r=0.31，p=0.10）和 FSL 丘脑分割（r=0.37，p=0.051）。Bland-Altman 图突出显示了手动标签和自动方法之间在分布低端（即较小的结构）之间存在更大的体积差异的趋势，在所有自动管道中，双侧丘脑最为明显，而对于 FSL，则为左侧苍白球。然后，我们使用 135 名 FEP 患者和 88 名对照的自动技术检查了基底节结构的体积和形状。与对照组相比，FEP 患者双侧的纹状体和苍白球明显更大，无论使用何种方法。MAGeT-Brain 对基于形状的组间差异更敏感，并在 FEP 患者双侧的纹状体和苍白球中发现了广泛的表面扩张，以及双侧丘脑的表面收缩（FDR 校正）。相比之下，在用推荐的聚类阈值方法后，FSL 仅检测到右侧腹侧纹状体（FEP>对照）和左侧丘脑的一个簇（对照>FEP）的差异。这些结果表明，与手动方法相比，不同的自动管道分割皮质下结构的变异性程度不同，特别是在苍白球和丘脑方面，FreeSurfer 和 FSL 差异更为明显。
