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Model selection for spatiotemporal modeling of early childhood sub-cortical development
Author(s): James Fishbaugh; Beatriz Paniagua; Mahmoud Mostapha; Martin Styner; Veronica Murphy; John Gilmore; Guido Gerig
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Paper Abstract

Spatiotemporal shape models capture the dynamics of shape change over time and are an essential tool for monitoring and measuring anatomical growth or degeneration. In this paper we evaluate non-parametric shape regression on the challenging problem of modeling early childhood sub-cortical development starting from birth. Due to the flexibility of the model, it can be challenging to choose parameters which lead to a good model fit yet does not overfit. We systematically test a variety of parameter settings to evaluate model fit as well as the sensitivity of the method to specific parameters, and we explore the impact of missing data on model estimation.

Paper Details

Date Published: 15 March 2019
PDF: 6 pages
Proc. SPIE 10949, Medical Imaging 2019: Image Processing, 109490L (15 March 2019); doi: 10.1117/12.2513030
Show Author Affiliations
James Fishbaugh, New York Univ. (United States)
Beatriz Paniagua, Kitware, Inc. (United States)
Mahmoud Mostapha, The Univ. of North Carolina at Chapel Hill (United States)
Martin Styner, The Univ. of North Carolina at Chapel Hill (United States)
Veronica Murphy, The Univ. of North Carolina at Chapel Hill (United States)
John Gilmore, The Univ. of North Carolina at Chapel Hill (United States)
Guido Gerig, New York Univ. (United States)

Published in SPIE Proceedings Vol. 10949:
Medical Imaging 2019: Image Processing
Elsa D. Angelini; Bennett A. Landman, Editor(s)

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