Shape analysis has become of increasing interest to the neuroimaging community due to its potential to precisely locate morphological changes between healthy and pathological structures. This manuscript presents a comprehensive set of tools for the computation of 3D structural statistical shape analysis. It has been applied in several studies on brain morphometry, but can potentially be employed in other 3D shape problems. Its main limitations is the necessity of spherical topology.The input of the proposed shape analysis is a set of binary segmentation of a single brain structure, such as the hippocampus or caudate. These segmentations are converted into a corresponding spherical harmonic description (SPHARM), which is then sampled into a triangulated surfaces (SPHARM-PDM). After alignment, differences between groups of surfaces are computed using the Hotelling T2 two sample metric. Statistical p-values, both raw and corrected for multiple comparisons, result in significance maps. Additional visualization of the group tests are provided via mean difference magnitude and vector maps, as well as maps of the group covariance information.The correction for multiple comparisons is performed via two separate methods that each have a distinct view of the problem. The first one aims to control the family-wise error rate (FWER) or false-positives via the extrema histogram of non-parametric permutations. The second method controls the false discovery rate and results in a less conservative estimate of the false-negatives.
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机译:形状分析由于其在健康和病理结构之间精确定位形态变化的潜力,已引起神经影像界的越来越多的兴趣。该手稿介绍了一套用于计算3D结构统计形状分析的综合工具。它已在有关脑形态测量的多项研究中得到了应用,但也有可能用于其他3D形状问题。它的主要局限性是球形拓扑的必要性。所提出的形状分析的输入是单个大脑结构(例如海马体或尾状体)的二进制分割集。将这些分段转换为相应的球谐描述(SPHARM),然后将其采样到三角表面(SPHARM-PDM)中。对齐后,使用Hotelling T 2 sup>两个样本度量标准来计算表面组之间的差异。统计的p值,包括原始值和经过多次比较校正的p值,都会生成重要性图。通过均值差大小和向量图以及组协方差信息图提供了组检验的其他可视化功能。通过两种单独的方法进行多次比较的校正,每种方法都有不同的问题视图。第一个目标是通过非参数排列的极值直方图来控制家庭错误率(FWER)或假阳性。第二种方法控制错误发现率,并导致错误阴性的保守程度较低。
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