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Hierarchical Bayesian Modeling, Estimation, and Sampling for Multigroup Shape Aanalysis

Institution:
Scientific Computing and Imaging (SCI) Institute, School of Computing, University of Utah, UT, USA.
Publisher:
Int Conf Med Image Comput Comput Assist Interv. MICCAI 2014
Publication Date:
Sep-2014
Journal:
Int Conf Med Image Comput Comput Assist Interv.
Volume Number:
17
Issue Number:
Pt 3
Pages:
9-16
Citation:
Med Image Comput Comput Assist Interv. 2014 Sep;17(Pt 3):9-16.
PubMed ID:
25320776
PMCID:
PMC4872874
Appears in Collections:
NA-MIC
Sponsors:
R01 EB007688/EB/NIBIB NIH HHS/United States
P41 RR12553/RR/NCRR NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
Generated Citation:
Yu Y-Y., Fletcher P.T., Awate S.P. Hierarchical Bayesian Modeling, Estimation, and Sampling for Multigroup Shape Aanalysis. Med Image Comput Comput Assist Interv. 2014 Sep;17(Pt 3):9-16. PMID: 25320776. PMCID: PMC4872874.
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This paper proposes a novel method for the analysis of anatomical shapes present in biomedical image data. Motivated by the natural organization of population data into multiple groups, this paper presents a novel hierarchical generative statistical model on shapes. The proposed method represents shapes using pointsets and defines a joint distribution on the population's (i) shape variables and (ii) object-boundary data. The proposed method solves for optimal (i) point locations, (ii) correspondences, and (iii) model-parameter values as a single optimization problem. The optimization uses expectation maximization relying on a novel Markov-chain Monte-Carlo algorithm for sampling in Kendall shape space. Results on clinical brain images demonstrate advantages over the state of the art.

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