NDLI: Improved Inference in Bayesian Segmentation Using Monte Carlo Sampling: Application to Hippocampal Subfield Volumetry

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Improved Inference in Bayesian Segmentation Using Monte Carlo Sampling: Application to Hippocampal Subfield Volumetry

Content Provider	PubMed Central
Author	Iglesias, Juan Eugenio Sabuncu, Mert Rory Leemput, Koen Van
Copyright Year	2013
Abstract	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.
Related Links	http://dx.doi.org/10.1016/j.media.2013.04.005
Ending Page	778
Page Count	13
Starting Page	766
File Format	PDF
ISSN	13618415
e-ISSN	13618423
Journal	Medical image analysis
Issue Number	7
Volume Number	17
Language	English
Publisher Date	2013-10-01
Access Restriction	Open
Subject Keyword	Radiological and Ultrasound Technology Health Informatics Radiology Nuclear Medicine and imaging Computer Vision and Pattern Recognition Computer Graphics and Computer-Aided Design Research in Higher Education
Content Type	Text
Resource Type	Article
Subject	Computer Graphics and Computer-Aided Design Radiology, Nuclear Medicine and Imaging Health Informatics Computer Vision and Pattern Recognition Radiological and Ultrasound Technology

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