Improved inference in Bayesian segmentation using Monte Carlo sampling: Application to hippocampal subfield volumetry

Juan Eugenio Iglesias; Mert Rory Sabuncu; Koen Van Leemput; Alzheimer’s Disease Neuroimaging Initiative

doi:10.1016/j.media.2013.04.005

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Improved inference in Bayesian segmentation using Monte Carlo sampling: Application to hippocampal subfield volumetry

Journal article

Peer reviewed

Improved inference in Bayesian segmentation using Monte Carlo sampling: Application to hippocampal subfield volumetry

Juan Eugenio Iglesias, Mert Rory Sabuncu, Koen Van Leemput and Alzheimer’s Disease Neuroimaging Initiative

Medical image analysis, Vol.17(7), pp.766-778

10/2013

DOI: 10.1016/j.media.2013.04.005

PMCID: PMC3719857

PMID: 23773521

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Abstract

[Display omitted] •An improved inference method for Bayesian segmentation using MCMC sampling.•The sampling is used to approximate the integral over model parameters.•We tested the method in a AD classification task using hippocampal subfield volumes.•The method outperforms using point estimates of the parameters in the classification.•The framework also provides informative error bars on the volume estimates. Many segmentation algorithms in medical image analysis use Bayesian modeling to augment local image appearance with prior anatomical knowledge. Such methods often contain a large number of free parameters that are first estimated and then kept fixed during the actual segmentation process. However, a faithful Bayesian analysis would marginalize over such parameters, accounting for their uncertainty by considering all possible values they may take. Here we propose to incorporate this uncertainty into Bayesian segmentation methods in order to improve the inference process. In particular, we approximate the required marginalization over model parameters using computationally efficient Markov chain Monte Carlo techniques. We illustrate the proposed approach using a recently developed Bayesian method for the segmentation of hippocampal subfields in brain MRI scans, showing a significant improvement in an Alzheimer’s disease classification task. As an additional benefit, the technique also allows one to compute informative “error bars” on the volume estimates of individual structures.

Hippocampal subfields

Segmentation

Monte Carlo sampling

Bayesian modeling

Details

Title: Subtitle: Improved inference in Bayesian segmentation using Monte Carlo sampling: Application to hippocampal subfield volumetry
Creators: Juan Eugenio Iglesias - Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, USA
Mert Rory Sabuncu - Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, USA
Koen Van Leemput - Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, USA
Alzheimer’s Disease Neuroimaging Initiative
Contributors: Laura L Boles-Ponto (Contributor) - University of Iowa, Radiology
Resource Type: Journal article
Publication Details: Medical image analysis, Vol.17(7), pp.766-778
DOI: 10.1016/j.media.2013.04.005
PMID: 23773521
PMCID: PMC3719857
NLM abbreviation: Med Image Anal
ISSN: 1361-8415
eISSN: 1361-8423
Publisher: Elsevier B.V
Language: English
Date published: 10/2013
Academic Unit: Radiology; Pharmaceutical Sciences and Experimental Therapeutics
Record Identifier: 9984051776302771

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