Construction of a 3D probabilistic atlas of human cortical structures
Construction of a 3D probabilistic atlas of human cortical structures
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DOI：
10.1016/j.neuroimage.2007.09.031
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发表时间：
2008-02-01
期刊：
影响因子：
5.7
通讯作者：
Toga, Arthur W.
中科院分区：
文献类型：
--
作者：
Shattuck, David W.;Mirza, Mubeena;Toga, Arthur W.
我们描述了由手动描述的MRI数据中数据组成的数字大脑图集的构建。总共56个结构在40位健康的正常志愿者的MRI中标记。该标签是根据为该项目开发的一组协议执行的。将评估者对分配给每个结构，并根据该结构的协议进行培训。对40个大脑中的6个进行了测试，每对评估者对一致的一致性；一旦达到可靠性标准，他们就将划定剩余34个大脑的任务分开。然后，使用3种流行算法将数据归一化为众所周知的模板：AIR5.2.5的非线性经线（Woods等，1998）与ICBM452 Warp 5 Atlas配对（Rex等，2003），FSL的Flirt（FSL的调情（ Smith等人，2004年）与自己的模板配对，这是ICBM152 T1平均值的颅骨剥离版本； SPM5的统一分割方法（Ashburner和Friston，2005年）与其规范大脑（整个头部ICBM152 T1平均值）配对。因此，我们生产了3个Atlas的变体，其中每个变体都是由一个可能用于分析的数据处理流的40个代表性样本构建的。对于每种归一化算法，然后根据计算的转换重新采样单个结构描述。接下来，我们在每个体素位置计算平均值，以估计属于56个结构中每个体素的体素的概率。每个版本的ATLA都包含每个区域的每个体素的概率密度，因此为注册在标准空间中的外部数据类型的自动概率标签提供了资源；我们还根据三种方法和目标空间计算了平均强度图像和组织密度图。这些地图酶将作为各种应用程序的资源，包括对功能和结构成像数据的荟萃分析以及其他生物信息学应用程序，在这些应用程序中，在概率定义的解剖空间中显示任意标记将有助于基于知识的解剖空间和来自多个学科的发现的可视化。 （c）2007 Elsevier Inc.保留所有权利。
We describe the construction of a digital brain atlas composed of data from manually delineated MRI data. A total of 56 structures were labeled in MRI of 40 healthy, normal volunteers. This labeling was performed according to a set of protocols developed for this project. Pairs of raters were assigned to each structure and trained on the protocol for that structure. Each rater pair was tested for concordance on 6 of the 40 brains; once they had achieved reliability standards, they divided the task of delineating the remaining 34 brains. The data were then spatially normalized to well-known templates using 3 popular algorithms: AIR5.2.5's nonlinear warp (Woods et al., 1998) paired with the ICBM452 Warp 5 atlas (Rex et al., 2003), FSL's FLIRT (Smith et al., 2004) was paired with its own template, a skull-stripped version of the ICBM152 T1 average; and SPM5's unified segmentation method (Ashburner and Friston, 2005) was paired with its canonical brain, the whole head ICBM152 T1 average. We thus produced 3 variants of our atlas, where each was constructed from 40 representative samples of a data processing stream that one might use for analysis. For each normalization algorithm, the individual structure delineations were then resampled according to the computed transformations. We next computed averages at each voxel location to estimate the probability of that voxel belonging to each of the 56 structures. Each version of the atlas contains, for every voxel, probability densities for each region, thus providing a resource for automated probabilistic labeling of external data types registered into standard spaces; we also computed average intensity images and tissue density maps based on the three methods and target spaces. These atlases will serve as a resource for diverse applications including meta-analysis of functional and structural imaging data and other bioinformatics applications where display of arbitrary labels in probabilistically defined anatomic space will facilitate both knowledge-based development and visualization of findings from multiple disciplines. (c) 2007 Elsevier Inc. All rights reserved.