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Proceedings Paper

A framework for longitudinal data analysis via shape regression
Author(s): James Fishbaugh; Stanley Durrleman; Joseph Piven; Guido Gerig
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Paper Abstract

Traditional longitudinal analysis begins by extracting desired clinical measurements, such as volume or head circumference, from discrete imaging data. Typically, the continuous evolution of a scalar measurement is estimated by choosing a 1D regression model, such as kernel regression or fitting a polynomial of fixed degree. This type of analysis not only leads to separate models for each measurement, but there is no clear anatomical or biological interpretation to aid in the selection of the appropriate paradigm. In this paper, we propose a consistent framework for the analysis of longitudinal data by estimating the continuous evolution of shape over time as twice differentiable flows of deformations. In contrast to 1D regression models, one model is chosen to realistically capture the growth of anatomical structures. From the continuous evolution of shape, we can simply extract any clinical measurements of interest. We demonstrate on real anatomical surfaces that volume extracted from a continuous shape evolution is consistent with a 1D regression performed on the discrete measurements. We further show how the visualization of shape progression can aid in the search for significant measurements. Finally, we present an example on a shape complex of the brain (left hemisphere, right hemisphere, cerebellum) that demonstrates a potential clinical application for our framework.

Paper Details

Date Published: 24 February 2012
PDF: 7 pages
Proc. SPIE 8314, Medical Imaging 2012: Image Processing, 83143K (24 February 2012); doi: 10.1117/12.911721
Show Author Affiliations
James Fishbaugh, The Univ. of Utah (United States)
Stanley Durrleman, The Univ. of Utah (United States)
Joseph Piven, The Univ. of North Carolina at Chapel Hill (United States)
Guido Gerig, The Univ. of Utah (United States)


Published in SPIE Proceedings Vol. 8314:
Medical Imaging 2012: Image Processing
David R. Haynor; Sébastien Ourselin, Editor(s)

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