A direct morphometric comparison of five labeling protocols for multi-atlas driven automatic segmentation of the hippocampus in Alzheimer's disease.
A direct morphometric comparison of five labeling protocols for multi-atlas driven automatic segmentation of the hippocampus in Alzheimer's disease.
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DOI：
10.1016/j.neuroimage.2012.10.081
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发表时间：
2013-02-01
期刊：
影响因子：
5.7
通讯作者：
Alzheimer's Disease Neuroimaging Initiative
中科院分区：
文献类型：
--
作者：
Nestor SM;Gibson E;Gao FQ;Kiss A;Black SE;Alzheimer's Disease Neuroimaging Initiative
来自结构MRI的海马体积测定越来越多地用于描绘功能测量的感兴趣区域，评估阿尔茨海默病（AD）治疗试验的疗效，并已被新的AD诊断指南认可为疾病进展的放射学标志物。不幸的是，AD的形态异质性可能会妨碍海马的准确划分。自动容量分析的最新发展通常使用由专家手动标签驱动的多模板融合，从而在疾病和健康受试者中实现高度准确和可再现的分割。然而，有几个协议来定义海马体解剖学在体内，用于生成图谱的方法可能会影响自动准确性和灵敏度-特别是在病理异质性样本。在这里，我们报告了一种全自动分割技术，该技术提供了一个强大的平台，可以在解剖学上独特的标记协议中直接评估AD的技术和生物标志物性能。我们首次使用Sunnybrook纵向痴呆研究和整个阿尔茨海默病神经影像学倡议1（ADNI-1）基线和24个月数据集，对五种常见的海马标记协议进行头对头测试，以进行基于多图谱的分割。我们基于这些图谱库的协议，和一个单一的操作员执行所有的手动跟踪，以产生事实上的“地面真相”标签。所有的方法区分正常老年人，轻度认知障碍（MCI），和AD在预期的方向，并表现出可比的相关性与情节记忆表现的措施。只有更具包容性的协议区分稳定MCI和MCI到AD转换器，并与情节记忆有更好的关联。此外，我们证明，协议，包括更多的后部解剖结构和背侧白色物质隔间提供最好的体素重叠的准确性（骰子相似系数= 0.87-0.89），与专家手动描记相比，并实现所需的最小样本量的权力在MCI和AD的临床试验。最大的错误分布是本地化的尾部海马和肺泡伞室时，这些地区被排除在外。在更全面的协议中，内侧体的定义并没有显著改变准确性。与ADNI-1样本相比，Sunnybrook研究中自动和手动标记之间的体素重叠准确度较低。最后，协议之间的准确性似乎显着不同的最AD主题相比，MCI和正常的老年人。总之，这些结果表明，选择用于AD中基于全自动多模板的分割的候选方案可以影响与专家手动标记相比的分割准确性以及作为MCI和AD中的生物标志物的性能。
Hippocampal volumetry derived from structural MRI is increasingly used to delineate regions of interest for functional measurements, assess efficacy in therapeutic trials of Alzheimer’s disease (AD) and has been endorsed by the new AD diagnostic guidelines as a radiological marker of disease progression. Unfortunately, morphological heterogeneity in AD can prevent accurate demarcation of the hippocampus. Recent developments in automated volumetry commonly use multitemplate fusion driven by expert manual labels, enabling highly accurate and reproducible segmentation in disease and healthy subjects. However, there are several protocols to define the hippocampus anatomically in vivo, and the method used to generate atlases may impact automatic accuracy and sensitivity – particularly in pathologically heterogeneous samples. Here we report a fully automated segmentation technique that provides a robust platform to directly evaluate both technical and biomarker performance in AD among anatomically unique labeling protocols. For the first time we test head-to-head the performance of five common hippocampal labeling protocols for multi-atlas based segmentation, using both the Sunnybrook Longitudinal Dementia Study and the entire Alzheimer’s Disease Neuroimaging Initiative 1 (ADNI-1) baseline and 24-month dataset. We based these atlas libraries on the protocols of, and a single operator performed all manual tracings to generate de facto “ground truth” labels. All methods distinguished between normal elders, mild cognitive impairment (MCI), and AD in the expected directions, and showed comparable correlations with measures of episodic memory performance. Only more inclusive protocols distinguished between stable MCI and MCI-to-AD converters, and had slightly better associations with episodic memory. Moreover, we demonstrate that protocols including more posterior anatomy and dorsal white matter compartments furnish the best voxel-overlap accuracies (Dice Similarity Coefficient = 0.87–0.89), compared to expert manual tracings, and achieve the smallest sample sizes required to power clinical trials in MCI and AD. The greatest distribution of errors was localized to the caudal hippocampus and alveus-fimbria compartment when these regions were excluded. The definition of the medial body did not significantly alter accuracy among more comprehensive protocols. Voxel-overlap accuracies between automatic and manual labels were lower for the more pathologically heterogeneous Sunnybrook study in comparison to the ADNI-1 sample. Finally, accuracy among protocols appears to significantly differ the most in AD subjects compared to MCI and normal elders. Together, these results suggest that selection of a candidate protocol for fully automatic multi-template based segmentation in AD can influence both segmentation accuracy when compared to expert manual labels and performance as a biomarker in MCI and AD.
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影响因子：
5.7
作者：
Costafreda, Sergi G.;Dinov, Ivo D.;Tu, Zhuowen;Shi, Yonggang;Liu, Cheng-Yi;Kloszewska, Iwona;Mecocci, Patrizia;Soininen, Hilkka;Tsolaki, Magda;Vellas, Bruno;Wahlund, Lars-Olof;Spenger, Christian;Toga, Arthur W.;Lovestone, Simon;Simmons, Andrew
通讯作者：
Simmons, Andrew
影响因子：
5.7
作者：
Collins, D. Louis;Pruessner, Jens C.
通讯作者：
Pruessner, Jens C.
影响因子：
4.8
作者：
Collins, DL;Holmes, CJ;Evans, AC
通讯作者：
Evans, AC
影响因子：
5.7
作者：
Fonov V;Evans AC;Botteron K;Almli CR;McKinstry RC;Collins DL;Brain Development Cooperative Group
通讯作者：
Brain Development Cooperative Group
DOI：
10.3233/jad-2011-0004
发表时间：
2011
期刊：
Journal of Alzheimer's disease : JAD
影响因子：
--
作者：
Boccardi M;Ganzola R;Bocchetta M;Pievani M;Redolfi A;Bartzokis G;Camicioli R;Csernansky JG;de Leon MJ;deToledo-Morrell L;Killiany RJ;Lehéricy S;Pantel J;Pruessner JC;Soininen H;Watson C;Duchesne S;Jack CR Jr;Frisoni GB
通讯作者：
Frisoni GB