Bayesian Parameter Estimation and Segmentation in the Multi-Atlas Random Orbit Model.

This paper examines the multiple atlas random diffeomorphic orbit model in Computational Anatomy (CA) for parameter estimation and segmentation of subcortical and ventricular neuroanatomy in magnetic resonance imagery. We assume that there exist multiple magnetic resonance image (MRI) atlases, each...

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Autores principales:	Xiaoying Tang, Kenichi Oishi, Andreia V Faria, Argye E Hillis, Marilyn S Albert, Susumu Mori, Michael I Miller
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/ac57048decb24e1381337bcdb0104d91
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spelling	oai:doaj.org-article:ac57048decb24e1381337bcdb0104d912021-11-18T07:41:23ZBayesian Parameter Estimation and Segmentation in the Multi-Atlas Random Orbit Model.1932-620310.1371/journal.pone.0065591https://doaj.org/article/ac57048decb24e1381337bcdb0104d912013-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0065591https://doaj.org/toc/1932-6203This paper examines the multiple atlas random diffeomorphic orbit model in Computational Anatomy (CA) for parameter estimation and segmentation of subcortical and ventricular neuroanatomy in magnetic resonance imagery. We assume that there exist multiple magnetic resonance image (MRI) atlases, each atlas containing a collection of locally-defined charts in the brain generated via manual delineation of the structures of interest. We focus on maximum a posteriori estimation of high dimensional segmentations of MR within the class of generative models representing the observed MRI as a conditionally Gaussian random field, conditioned on the atlas charts and the diffeomorphic change of coordinates of each chart that generates it. The charts and their diffeomorphic correspondences are unknown and viewed as latent or hidden variables. We demonstrate that the expectation-maximization (EM) algorithm arises naturally, yielding the likelihood-fusion equation which the a posteriori estimator of the segmentation labels maximizes. The likelihoods being fused are modeled as conditionally Gaussian random fields with mean fields a function of each atlas chart under its diffeomorphic change of coordinates onto the target. The conditional-mean in the EM algorithm specifies the convex weights with which the chart-specific likelihoods are fused. The multiple atlases with the associated convex weights imply that the posterior distribution is a multi-modal representation of the measured MRI. Segmentation results for subcortical and ventricular structures of subjects, within populations of demented subjects, are demonstrated, including the use of multiple atlases across multiple diseased groups.Xiaoying TangKenichi OishiAndreia V FariaArgye E HillisMarilyn S AlbertSusumu MoriMichael I MillerPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 6, p e65591 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Xiaoying Tang Kenichi Oishi Andreia V Faria Argye E Hillis Marilyn S Albert Susumu Mori Michael I Miller Bayesian Parameter Estimation and Segmentation in the Multi-Atlas Random Orbit Model.
description	This paper examines the multiple atlas random diffeomorphic orbit model in Computational Anatomy (CA) for parameter estimation and segmentation of subcortical and ventricular neuroanatomy in magnetic resonance imagery. We assume that there exist multiple magnetic resonance image (MRI) atlases, each atlas containing a collection of locally-defined charts in the brain generated via manual delineation of the structures of interest. We focus on maximum a posteriori estimation of high dimensional segmentations of MR within the class of generative models representing the observed MRI as a conditionally Gaussian random field, conditioned on the atlas charts and the diffeomorphic change of coordinates of each chart that generates it. The charts and their diffeomorphic correspondences are unknown and viewed as latent or hidden variables. We demonstrate that the expectation-maximization (EM) algorithm arises naturally, yielding the likelihood-fusion equation which the a posteriori estimator of the segmentation labels maximizes. The likelihoods being fused are modeled as conditionally Gaussian random fields with mean fields a function of each atlas chart under its diffeomorphic change of coordinates onto the target. The conditional-mean in the EM algorithm specifies the convex weights with which the chart-specific likelihoods are fused. The multiple atlases with the associated convex weights imply that the posterior distribution is a multi-modal representation of the measured MRI. Segmentation results for subcortical and ventricular structures of subjects, within populations of demented subjects, are demonstrated, including the use of multiple atlases across multiple diseased groups.
format	article
author	Xiaoying Tang Kenichi Oishi Andreia V Faria Argye E Hillis Marilyn S Albert Susumu Mori Michael I Miller
author_facet	Xiaoying Tang Kenichi Oishi Andreia V Faria Argye E Hillis Marilyn S Albert Susumu Mori Michael I Miller
author_sort	Xiaoying Tang
title	Bayesian Parameter Estimation and Segmentation in the Multi-Atlas Random Orbit Model.
title_short	Bayesian Parameter Estimation and Segmentation in the Multi-Atlas Random Orbit Model.
title_full	Bayesian Parameter Estimation and Segmentation in the Multi-Atlas Random Orbit Model.
title_fullStr	Bayesian Parameter Estimation and Segmentation in the Multi-Atlas Random Orbit Model.
title_full_unstemmed	Bayesian Parameter Estimation and Segmentation in the Multi-Atlas Random Orbit Model.
title_sort	bayesian parameter estimation and segmentation in the multi-atlas random orbit model.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/ac57048decb24e1381337bcdb0104d91
work_keys_str_mv	AT xiaoyingtang bayesianparameterestimationandsegmentationinthemultiatlasrandomorbitmodel AT kenichioishi bayesianparameterestimationandsegmentationinthemultiatlasrandomorbitmodel AT andreiavfaria bayesianparameterestimationandsegmentationinthemultiatlasrandomorbitmodel AT argyeehillis bayesianparameterestimationandsegmentationinthemultiatlasrandomorbitmodel AT marilynsalbert bayesianparameterestimationandsegmentationinthemultiatlasrandomorbitmodel AT susumumori bayesianparameterestimationandsegmentationinthemultiatlasrandomorbitmodel AT michaelimiller bayesianparameterestimationandsegmentationinthemultiatlasrandomorbitmodel
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Bayesian Parameter Estimation and Segmentation in the Multi-Atlas Random Orbit Model.

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