Evaluating accuracy of striatal, pallidal, and thalamic segmentation methods: Comparing automated approaches to manual delineation
Evaluating accuracy of striatal, pallidal, and thalamic segmentation methods: Comparing automated approaches to manual delineation
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DOI：
10.1016/j.neuroimage.2017.02.069
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发表时间：
2018-04-15
期刊：
影响因子：
5.7
通讯作者：
Chakravarty, M. Mallar
中科院分区：
文献类型：
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作者：
Makowski, Carolina;Beland, Sophie;Chakravarty, M. Mallar
皮质下结构的精确自动量化是神经影像学中的一项重要工作。为了建立这些方法在定义纹状体，苍白球，丘脑的有效性和可靠性，我们研究了手动描绘和自动分割广泛使用的FreeSurfer和FSL软件包，以及最近的分割方法，MAGeT-Brain算法之间的体积差异。在第一组实验中，人工定义了30名受试者（15名首次发作精神病[FEP]，15名对照）的基底神经节和丘脑，并与三种自动化方法生成的标签进行了比较。我们的研究结果表明，所有的方法高估量相比，手动获得的“金标准”，与最不明显的差异产生使用MAGeT。最少的方法之间的变异性注意到纹状体，而手动分割和MAGeT相比FreeSurfer和FSL出现苍白球和丘脑的显着差异。对于MAGeT，手动分割和自动方法之间的相关性最强（范围：0.51至0.92; p < 0.01，校正），而FreeSurfer和FSL显示中度至强Pearson相关性（范围0.44-0.86; p < 0.05，校正），FreeSurfer苍白球（r=0.31，p=0.10）和FSL丘脑分段（r=0.37，p=0.051）除外。Bland-Altman图强调了手动标记和自动化方法之间在分布低端（即较小结构）的体积差异更大的趋势，这在自动化管道的双侧丘脑和FSL的左侧苍白球中最为突出。帕拉;&帕拉;然后，我们继续检查体积和形状的基底神经节结构使用自动化技术在135例FEP患者和88名对照。无论使用何种方法，FEP患者的纹状体和苍白球明显大于对照组。MAGeT-Brain对基于形状的组差异更敏感，并发现与对照组相比，FEP患者双侧纹状体和苍白球广泛的表面扩张，以及双侧丘脑的表面收缩（FDR校正）。相比之下，在使用推荐的聚类阈值方法后，FSL仅检测到右侧腹侧纹状体（FEP > Control）和左侧丘脑的一个聚类（Control > FEP）的差异。帕拉;&帕拉;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具有不同程度的变异性，其中FreeSurfer和FSL在苍白球和丘脑中发现了特别明显的差异。
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.& para;& para;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).& para;& para;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.