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Bayesian mixed effect atlas estimation with a diffeomorphic deformation model

Stéphanie Allassonnière 1 Stanley Durrleman 2, 3 Estelle Kuhn 4, *
* Corresponding author
3 ARAMIS - Algorithms, models and methods for images and signals of the human brain
Inria Paris-Rocquencourt, UPMC - Université Pierre et Marie Curie - Paris 6, ICM - Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute
Abstract : In this paper we introduce a diffeomorphic constraint on the deformations considered in the deformable Bayesian Mixed Effect (BME) Template model. Our approach is built on a generic group of diffeomorphisms, which is parametrized by an arbitrary set of control point positions and momentum vectors. This enables to estimate the optimal positions of control points together with a template image and parameters of the deformation distribution which compose the atlas. We propose to use a stochastic version of the Expectation-Maximization (EM) algorithm where the simulation is performed using the Anisotropic Metropolis Adjusted Langevin Algorithm (AMALA). We propose also an extension of the model including a sparsity constraint to select an optimal number of control points with relevant positions. Experiments are carried out on the USPS database and on mandibles of mice.
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Submitted on : Friday, June 5, 2020 - 9:10:38 PM
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  • HAL Id : hal-02801668, version 1
  • PRODINRA : 264348


Stéphanie Allassonnière, Stanley Durrleman, Estelle Kuhn. Bayesian mixed effect atlas estimation with a diffeomorphic deformation model. [Technical Report] R2014-2, Inra. 2014. ⟨hal-02801668⟩



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