Bayesian Analysis of Diffeomorphic Shape Variability

Speaker: Miaomiao Zhang , University of Utah

Date: Thursday, April 30, 2015

Time: 4:00 PM to 5:00 PM Note: all times are in the Eastern Time Zone

Public: Yes

Location: 32-D463 (Star)

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Host: Polina Golland, CSAIL

Contact: Polina Golland, x38005,

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Reminder Subject: TALK: Bayesian Analysis of Diffeomorphic Shape Variability

Quantifying diffeomorphic shape variability is an important tool for
relating brain shape to disease processes and changes in cognitive and
behavioral measures. The common template-based approach to statistical
shape analysis is to use deformable image registration to map input images to
a template, and then compute statistics of resulting transformations. In
this talk, I will first present a Bayesian framework of atlas building that
estimates the parameters that control the diffeomorphic transformation
regularity. A Monte Carlo Expectation Maximization algorithm (MCEM) is
developed for inference where the expectation step is approximated via
sampling on the manifold of diffeomorphisms. Based on this setting, I will
then introduce how to encode shape variability directly in a Bayesian model
through diffeomorphic deformations. To overcome the challenge of high
dimensionality of the deformations, combined with a relatively small number
of image samples, we extract intrinsic low-dimensional, second-order
statistics of anatomical shape variability. Bayesian inference with Markov
Chain Monte Carlo (MCMC) is intractable due to the large computational cost
of diffeomorphic image registration. Therefore, I will propose a fast
geodesic shooting algorithm, which breaks through the prohibitive time and
memory requirements of the Bayesian inference. This is achieved by
introducing a novel finite-dimensional Lie algebra structure on the space
of bandlimited velocity fields of diffeomorphisms.

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See other events that are part of the Biomedical Imaging and Analysis 2014/2015.

Created by Polina Golland Email at Thursday, April 16, 2015 at 9:48 PM.