Optimal Multiple Surface Segmentation With Shape and Context Priors

Qi Song; Junjie Bai; Mona K Garvin; Milan Sonka; John M Buatti; Xiaodong Wu

doi:10.1109/TMI.2012.2227120

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Optimal Multiple Surface Segmentation With Shape and Context Priors

Journal article

Open access

Peer reviewed

Optimal Multiple Surface Segmentation With Shape and Context Priors

Qi Song, Junjie Bai, Mona K Garvin, Milan Sonka, John M Buatti and Xiaodong Wu

IEEE transactions on medical imaging, Vol.32(2), pp.376-386

02/2013

DOI: 10.1109/TMI.2012.2227120

PMCID: PMC4076846

PMID: 23193309

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Abstract

Segmentation of multiple surfaces in medical images is a challenging problem, further complicated by the frequent presence of weak boundary evidence, large object deformations, and mutual influence between adjacent objects. This paper reports a novel approach to multi-object segmentation that incorporates both shape and context prior knowledge in a 3-D graph-theoretic framework to help overcome the stated challenges. We employ an arc-based graph representation to incorporate a wide spectrum of prior information through pair-wise energy terms. In particular, a shape-prior term is used to penalize local shape changes and a context-prior term is used to penalize local surface-distance changes from a model of the expected shape and surface distances, respectively. The globally optimal solution for multiple surfaces is obtained by computing a maximum flow in a low-order polynomial time. The proposed method was validated on intraretinal layer segmentation of optical coherence tomography images and demonstrated statistically significant improvement of segmentation accuracy compared to our earlier graph-search method that was not utilizing shape and context priors. The mean unsigned surface positioning errors obtained by the conventional graph-search approach (6.30 ± 1.58 μ m) was improved to 5.14 ± 0.99 μ m when employing our new method with shape and context priors.

image segmentation

retina

optical coherence tomography (OCT)

graph search

Context prior

global optimization

shape prior

Details

Title: Subtitle: Optimal Multiple Surface Segmentation With Shape and Context Priors
Creators: Qi Song - Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA 52242 USA. He is now with the Biomedical Image Analysis Lab, GE Global Research Center, Niskayuna, NY 12309 USA
Junjie Bai - Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA 52242 USA
Mona K Garvin - Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA 52242 USA and also with the VA Center for the Prevention and Treatment of Visual Loss, Department of Veteran Affairs, Iowa City, IA 52240 USA
Milan Sonka - Department of Electrical and Computer Engineering, the Department of Radiation Oncology, and the Department of Ophthalmology and Visual Sciences, The University of Iowa, Iowa City, IA 52242 USA
John M Buatti - Department of Radiation Oncology, The University of Iowa, Iowa City, IA 52242 USA
Xiaodong Wu - Department of Electrical and Computer Engineering and the Department of Radiation Oncology, The University of Iowa, Iowa City, IA 52242 USA
Resource Type: Journal article
Publication Details: IEEE transactions on medical imaging, Vol.32(2), pp.376-386
DOI: 10.1109/TMI.2012.2227120
PMID: 23193309
PMCID: PMC4076846
NLM abbreviation: IEEE Trans Med Imaging
ISSN: 0278-0062
eISSN: 1558-254X
Publisher: Institute of Electrical and Electronics Engineers
Language: English
Date published: 02/2013
Academic Unit: Roy J. Carver Department of Biomedical Engineering; Electrical and Computer Engineering; Radiation Oncology; Injury Prevention Research Center; Neurosurgery; Otolaryngology; Ophthalmology and Visual Sciences
Record Identifier: 9984040396902771

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