Mixed-Effects Shape Models for Estimating Longitudinal Changes in Anatomy.

Spatio-temporal image analysis for longitudinal and time-series image data : Second International Workshop, STIA 2012, held in conjunction with MICCAI 2012, Nice, France, October 1, 2012, proceedings. STIA (Conference) (2nd : 2012 : Nic... Pub Date : 2012-10-01 DOI:10.1007/978-3-642-33555-6_7

Manasi Datar, Prasanna Muralidharan, Abhishek Kumar, Sylvain Gouttard, Joseph Piven, Guido Gerig, Ross Whitaker, P Thomas Fletcher

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引用次数: 18

Abstract

In this paper, we propose a new method for longitudinal shape analysis that fits a linear mixed-effects model, while simultaneously optimizing correspondences on a set of anatomical shapes. Shape changes are modeled in a hierarchical fashion, with the global population trend as a fixed effect and individual trends as random effects. The statistical significance of the estimated trends are evaluated using specifically designed permutation tests. We also develop a permutation test based on the Hotelling T² statistic to compare the average shapes trends between two populations. We demonstrate the benefits of our method on a synthetic example of longitudinal tori and data from a developmental neuroimaging study.

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用于估计解剖学纵向变化的混合效应形状模型。

在本文中，我们提出了一种新的纵向形状分析方法，该方法适合线性混合效应模型，同时优化了一组解剖形状的对应关系。形状变化以分层方式建模，全球人口趋势是固定效应，个体趋势是随机效应。使用专门设计的排列检验评估估计趋势的统计显著性。我们还开发了基于Hotelling T2统计量的排列检验，以比较两个种群之间的平均形状趋势。我们在纵向环面和发育神经影像学研究数据的综合例子上展示了我们的方法的好处。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Spatio-temporal image analysis for longitudinal and time-series image data : Second International Workshop, STIA 2012, held in conjunction with MICCAI 2012, Nice, France, October 1, 2012, proceedings. STIA (Conference) (2nd : 2012 : Nic...

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