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Updated: Jun 8, 2026

Three-Dimensional Shape Modeling and Analysis of Brain Structures
Published on: November 14, 2019
A bayesian generative model for surface template estimation
Jun Ma1, Michael I Miller, Laurent Younes
1Center for Imaging Science, Department of Biomedical Engineering, The Johns Hopkins University, 320 Clark Hall, Baltimore, MD 21218, USA.
Abstract:
3D surfaces are important geometric models for many objects of interest in image analysis and Computational Anatomy. In this paper, we describe a Bayesian inference scheme for estimating a template surface from a set of observed surface data. In order to achieve this, we use the geodesic shooting approach to construct a statistical model for the generation and the observations of random surfaces. We develop a mode approximation EM algorithm to infer the maximum a posteriori estimation of initial momentum μ, which determines the template surface. Experimental results of caudate, thalamus, and hippocampus data are presented.
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