Incorporation of gradient vector flow field in a multimodal graph-theoretic approach for segmenting the internal limiting membrane from glaucomatous optic nerve head-centered SD-OCT volumes

Mohammad Saleh Miri; Victor A Robles; Michael D Abràmoff; Young H Kwon; Mona K Garvin

doi:10.1016/j.compmedimag.2016.06.007

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Incorporation of gradient vector flow field in a multimodal graph-theoretic approach for segmenting the internal limiting membrane from glaucomatous optic nerve head-centered SD-OCT volumes

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Incorporation of gradient vector flow field in a multimodal graph-theoretic approach for segmenting the internal limiting membrane from glaucomatous optic nerve head-centered SD-OCT volumes

Mohammad Saleh Miri, Victor A Robles, Michael D Abràmoff, Young H Kwon and Mona K Garvin

Computerized Medical Imaging and Graphics, Vol.55, pp.87-94

01/2017

DOI: 10.1016/j.compmedimag.2016.06.007

PMCID: PMC5219948

PMID: 27507325

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https://www.ncbi.nlm.nih.gov/pmc/articles/5219948View

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Abstract

•We present a multimodal approach for segmenting the surface of the optic nerve head.•A graph-based approach is extended to utilize gradient-vector-flow-based columns.•Issues related to the presence of blood vessels and deep cups are overcome.•The approach will enable more accurate computation of glaucomatous measures. The internal limiting membrane (ILM) separates the retina and optic nerve head (ONH) from the vitreous. In the optical coherence tomography volumes of glaucoma patients, while current approaches for the segmentation of the ILM in the peripapillary and macular regions are considered robust, current approaches commonly produce ILM segmentation errors at the ONH due to the presence of blood vessels and/or characteristic glaucomatous deep cupping. Because a precise segmentation of the ILM surface at the ONH is required for computing several newer structural measurements including Bruch's membrane opening-minimum rim width (BMO-MRW) and cup volume, in this study, we propose a multimodal multiresolution graph-based method to precisely segment the ILM surface within ONH-centered spectral-domain optical coherence tomography (SD-OCT) volumes. In particular, the gradient vector flow (GVF) field, which is computed from a multiresolution initial segmentation, is employed for calculating a set of non-overlapping GVF-based columns perpendicular to the initial segmentation. The GVF columns are utilized to resample the volume and also serve as the columns to the graph construction. The ILM surface in the resampled volume is fairly smooth and does not contain the steep slopes. This prior shape knowledge along with the blood vessel information, obtained from registered fundus photographs, are incorporated in a graph-theoretic approach in order to identify the location of the ILM surface. The proposed method is tested on the SD-OCT volumes of 44 subjects with various stages of glaucoma and significantly smaller segmentation errors were obtained than that of current approaches.

Optic nerve head

Gradient vector flow

SD-OCT

Fundus

Segmentation

Graph-based segmentation

Multimodal segmentation

Ophthalmology

Internal limiting membrane

Details

Title: Subtitle: Incorporation of gradient vector flow field in a multimodal graph-theoretic approach for segmenting the internal limiting membrane from glaucomatous optic nerve head-centered SD-OCT volumes
Creators: Mohammad Saleh Miri - Department of Electrical and Computer Engineering, The University of Iowa, Iowa City 52242, IA, United States
Victor A Robles - Department of Electrical and Computer Engineering, The University of Iowa, Iowa City 52242, IA, United States
Michael D Abràmoff - Department of Electrical and Computer Engineering, The University of Iowa, Iowa City 52242, IA, United States
Young H Kwon - Department of Ophthalmology and Visual Sciences, The University of Iowa, Iowa City 52242, IA, United States
Mona K Garvin - Department of Electrical and Computer Engineering, The University of Iowa, Iowa City 52242, IA, United States
Resource Type: Journal article
Publication Details: Computerized Medical Imaging and Graphics, Vol.55, pp.87-94
DOI: 10.1016/j.compmedimag.2016.06.007
PMID: 27507325
PMCID: PMC5219948
NLM abbreviation: Comput Med Imaging Graph
ISSN: 0895-6111
eISSN: 1879-0771
Publisher: Elsevier Ltd
Grant note: DOI: 10.13039/100000002, name: National Institutes of Health, award: R01 EY018853 and R01 EY023279
Language: English
Date published: 01/2017
Academic Unit: Roy J. Carver Department of Biomedical Engineering; Electrical and Computer Engineering; Ophthalmology and Visual Sciences
Record Identifier: 9983806280602771

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