Styner Martin, Oguz Ipek, Xu Shun, Brechbühler Christian, Pantazis Dimitrios, Levitt James J, Shenton Martha E, Gerig Guido
Insight J. 2006(1071):242-250.
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 T(2) 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.
由于形状分析能够精确地定位健康结构与病理结构之间的形态变化，因此它在神经成像领域越来越受到关注。本文介绍了一套用于三维结构统计形状分析计算的综合工具。它已应用于多项脑形态测量学研究，但也有可能用于其他三维形状问题。其主要局限性在于需要球形拓扑结构。所提出的形状分析的输入是单个脑结构（如海马体或尾状核）的一组二进制分割。这些分割被转换为相应的球谐描述（SPHARM），然后采样为三角化曲面（SPHARM-PDM）。对齐后，使用Hotelling T(2)双样本度量计算曲面组之间的差异。原始的和经过多重比较校正的统计p值会生成显著性图。通过平均差异幅度图、向量图以及组协方差信息图，对组测试进行额外的可视化展示。通过两种不同的方法进行多重比较校正，每种方法对该问题都有不同的视角。第一种方法旨在通过非参数置换的极值直方图控制家族性错误率（FWER）或假阳性。第二种方法控制错误发现率，并对假阴性给出不太保守的估计。
