Spatiotemporal modeling of anatomical shape complexes

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Publication Type dissertation
School or College School of Computing
Department Computing (School of)
Author Fishbaugh, James
Title Spatiotemporal modeling of anatomical shape complexes
Date 2015-05
Description Statistical analysis of time dependent imaging data is crucial for understanding normal anatomical development as well as disease progression. The most promising studies are of longitudinal design, where repeated observations are obtained from the same subjects. Analysis in this case is challenging due to the difficulty in modeling longitudinal changes, such as growth, and comparing changes across different populations. In any case, the study of anatomical change over time has the potential to further our understanding of many dynamic processes. What is needed are accurate computational models to capture, describe, and quantify anatomical change over time. Anatomical shape is encoded in a variety of representations, such as medical imaging data and derived geometric information extracted as points, curves, and/or surfaces. By considering various shape representations embedded into the same ambient space as a shape complex, either in 2D or 3D, we obtain a more comprehensive description of the anatomy than provided by an single isolated shape. In this dissertation, we develop spatiotemporal models of anatomical change designed to leverage multiple shape representations simultaneously. Rather than study directly the geometric changes to a shape itself, we instead consider how the ambient space deforms, which allows all embedded shapes to be included simultaneously in model estimation. Around this idea, we develop two complementary spatiotemporal models: a flexible nonparametric model designed to capture complex anatomical trajectories, and a generative model designed as a compact statistical representation of anatomical change. We present several ways spatiotemporal models can support the statistical analysis of scalar measurements, such as volume, extracted from shape. Finally, we cover the statistical analysis of higher dimensional shape features to take better advantage of the rich morphometric information provided by shape, as well as the trajectory of change captured by spatiotemporal models.
Type Text
Publisher University of Utah
Subject Shape analysis; Shape regression; Spatiotemporal
Dissertation Institution University of Utah
Dissertation Name Doctor of Philosophy
Language eng
Rights Management Copyright © James Fishbaugh 2015
Format Medium application/pdf
Format Extent 2,567,105 Bytes
Identifier etd3/id/3709
ARK ark:/87278/s6qv6vvg
Setname ir_etd
Date Created 2016-02-29
Date Modified 2017-07-12
ID 197260
Reference URL