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      Open-source algorithm for automatic choroid segmentation of OCT volume reconstructions

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          Abstract

          The use of optical coherence tomography (OCT) to study ocular diseases associated with choroidal physiology is sharply limited by the lack of available automated segmentation tools. Current research largely relies on hand-traced, single B-Scan segmentations because commercially available programs require high quality images, and the existing implementations are closed, scarce and not freely available. We developed and implemented a robust algorithm for segmenting and quantifying the choroidal layer from 3-dimensional OCT reconstructions. Here, we describe the algorithm, validate and benchmark the results, and provide an open-source implementation under the General Public License for any researcher to use ( https://www.mathworks.com/matlabcentral/fileexchange/61275-choroidsegmentation).

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          A pilot study of enhanced depth imaging optical coherence tomography of the choroid in normal eyes.

          Ron Margolis, Richard Spaide (2009)
          To measure macular choroidal thickness in normal eyes at different points using enhanced depth imaging (EDI) optical coherence tomography (OCT) and to evaluate the association of choroidal thickness and age. Retrospective, observational case series. EDI OCT images were obtained in patients without significant retinal or choroidal pathologic features. The images were obtained by positioning a spectral-domain OCT device close enough to the eye to acquire an inverted image. Seven sections were obtained within a 5 x 30-degree area centered at the fovea, with 100 scans averaged for each section. The choroid was measured from the outer border of the retinal pigment epithelium to the inner scleral border at 500-microm intervals of a horizontal section from 3 mm temporal to the fovea to 3 mm nasal to the fovea. Statistical analysis was performed to evaluate variations of choroidal thickness at each location and to correlate choroidal thickness and patient age. The mean age of the 30 patients (54 eyes) was 50.4 years (range, 19 to 85 years), and 14 patients (46.7%) were female. The choroid was thickest underneath the fovea (mean, 287 microm; standard deviation, +/- 76 microm). Choroidal thickness decreased rapidly in the nasal direction and averaged 145 microm (+/- 57 microm) at 3 mm nasal to the fovea. Increasing age was correlated significantly with decreasing choroidal thickness at all points measured. Regression analysis suggested that the subfoveal choroidal thickness decreased by 15.6 microm for each decade of life. Choroidal thickness seems to vary topographically within the posterior pole. The thickness of the choroid showed a negative correlation with age. The decrease in the thickness of the choroid may play a role in the pathophysiologic features of various age-related ocular conditions.
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            Mapping choroidal and retinal thickness variation in type 2 diabetes using three-dimensional 1060-nm optical coherence tomography.

            Boris Hermann, Nik Sheen, Wolfgang Drexler … (2011)
            To map choroidal (ChT) and retinal thickness (RT) in healthy subjects and patients with diabetes with and without maculopathy using three dimensional 1060-nm optical coherence tomography (3D-1060nm-OCT). Sixty-three eyes from 42 diabetic subjects (41-82 years of age; 11 females) grouped according to a custom scheme using Early Treatment Diabetic Retinopathy Study definitions for pathology within 1 disc-diameter of fovea (without pathology [NDR], microaneurysms [M1], exudates [M2], clinically significant macular edema [CSME]) and 16 eyes from 16 healthy age matched subjects (38-79 years of age; 11 females) were imaged by 3D-1060nm-OCT performed over a 36° × 36° field of view. Axial length, 45° fundus photographs, body mass index, plasma glucose, and blood pressure measurements were recorded. The ChT at the subfoveal location and ChT maps between RPE and the choroidal-scleral interface were generated and statistically analyzed. RT maps show thinning in the NDR group but an increase in thickness with increasing maculopathy in the temporal and central regions (unpaired t-test; P < 0.05). ChT mapping of all diabetic patients revealed central and inferior thinning compared to healthy eyes (unpaired t-test; P < 0.001). Subfoveal ChT (mean ± SD) for healthy eyes was 327 ± 74 μm, which was significantly thicker than all diabetic groups (214 ± 55 μm for NDR, 208 ± 49 μm for M1, 205 ± 54 μm for M2, and 211 ± 76 μm for CSME (ANOVA P < 0.001; Tukey P < 0.001). 3D-1060nm-OCT has shown that the central choroid is thinner in all type 2 diabetic eyes regardless of disease stage. The choroidal thinning may exceed the magnitude of possible choriocapillaris atrophy. In contrast to the conventional assessment of pathologic thickness change in several locations, thickness maps allow investigation of the choroid over the extent of affected areas.
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              Validated automatic segmentation of AMD pathology including drusen and geographic atrophy in SD-OCT images.

              C Toth, Joseph A. Izatt, Gabriel Chiu … (2011)
              To automatically segment retinal spectral domain optical coherence tomography (SD-OCT) images of eyes with age-related macular degeneration (AMD) and various levels of image quality to advance the study of retinal pigment epithelium (RPE)+drusen complex (RPEDC) volume changes indicative of AMD progression. A general segmentation framework based on graph theory and dynamic programming was used to segment three retinal boundaries in SD-OCT images of eyes with drusen and geographic atrophy (GA). A validation study for eyes with nonneovascular AMD was conducted, forming subgroups based on scan quality and presence of GA. To test for accuracy, the layer thickness results from two certified graders were compared against automatic segmentation results for 220 B-scans across 20 patients. For reproducibility, automatic layer volumes were compared that were generated from 0° versus 90° scans in five volumes with drusen. The mean differences in the measured thicknesses of the total retina and RPEDC layers were 4.2 ± 2.8 and 3.2 ± 2.6 μm for automatic versus manual segmentation. When the 0° and 90° datasets were compared, the mean differences in the calculated total retina and RPEDC volumes were 0.28% ± 0.28% and 1.60% ± 1.57%, respectively. The average segmentation time per image was 1.7 seconds automatically versus 3.5 minutes manually. The automatic algorithm accurately and reproducibly segmented three retinal boundaries in images containing drusen and GA. This automatic approach can reduce time and labor costs and yield objective measurements that potentially reveal quantitative RPE changes in longitudinal clinical AMD studies. (ClinicalTrials.gov number, NCT00734487.).
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                Author and article information

                Journal
                Journal ID (nlm-ta): Sci Rep
                Journal ID (iso-abbrev): Sci Rep
                Title: Scientific Reports
                Publisher: Nature Publishing Group
                ISSN (Electronic): 2045-2322
                Publication date (Electronic): 09 February 2017
                Publication date Collection: 2017
                Volume: 7
                Electronic Location Identifier: 42112
                Affiliations
                [1 ]Centre de Recherche Hôpital Maisonneuve-Rosemont , Montréal, QC, Canada
                [2 ]Département d’Ophtalmologie, Université de Montréal , Montréal, QC, Canada
                Author notes
                Article
                Publisher Item ID: srep42112
                DOI: 10.1038/srep42112
                PMC ID: 5299605
                PubMed ID: 28181546
                SO-VID: 791672dc-8aa5-4871-9aec-f84d8a943dee
                Copyright © Copyright © 2017, The Author(s)
                License:

                This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

                History
                Date received : 19 September 2016
                Date accepted : 30 December 2016
                Categories
                Subject: Article

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