.png){kind=icon}
返回
Hierarchical Geodesic Models in Diffeomorphisms
.png){kind=icon}
PRE
AI
DOI：10.1007/s11263-015-0849-2.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Hierarchical linear models (HLMs) are a standard approach for analyzing data where individuals are measured repeatedly over time. However, such models are only applicable to longitudinal studies of Euclidean data. This paper develops the theory of hierarchical geodesic models (HGMs), which generalize HLMs to the manifold setting. Our proposed model quantifies longitudinal trends in shapes as a hierarchy of geodesics in the group of diffeomorphisms. First, individuallevel geodesics represent the trajectory of shape changes within individuals. Second, a grouplevel geodesic represents the average trajectory of shape changes for the population. Our proposed HGM is applicable to longitudinal data from unbalanced designs, i.e., varying numbers of timepoints for individuals, which is typical in medical studies. We derive the solution of HGMs on diffeomorphisms to estimate individuallevel geodesics, the group geodesic, and the residual diffeomorphisms. We also propose an efficient parallel algorithm that easily scales to solve HGMs on a large collection of 3D images of several individuals. Finally, we present an effective model selection procedure based on cross validation. We demonstrate the effectiveness of HGMs for longitudinal analysis of synthetically generated shapes and 3D MRI brain scans.
Keyword:
Longitudinal modeling
Diffeomorphisms
Mixed-effects modeling
LDDMM
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
9.3
论文数:
3.9K
被引数:
2.8W
机构
引用论文
The Detection of Impact Damage to the Edges of CFRP Plates Using Extensional Ultrasonic Edge Waves
OAAI
OAAI分享收藏分享收藏Tuning the water permeability of ultra-high molecular weight polyethylene microporous membrane by molecular self-assembly and flow field.png)
Polymer
PolymerIF0
PREAI
分享收藏
查看更多内容