Comparison of retinal thickness by Fourier-domain optical coherence tomography and OCT retinal image analysis software segmentation analysis derived from Stratus optical coherence tomography images.
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ABSTRACT:
SUBMITTER: Tatrai E
PROVIDER: S-EPMC3104045 | biostudies-other | 2011 May
REPOSITORIES: biostudies-other
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Project description:To develop and test an algorithm to segment intraretinal layers irrespectively of the actual Optical Coherence Tomography (OCT) device used.The developed algorithm is based on the graph theory optimization. The algorithm's performance was evaluated against that of three expert graders for unsigned boundary position difference and thickness measurement of a retinal layer group in 50 and 41 B-scans, respectively. Reproducibility of the algorithm was tested in 30 C-scans of 10 healthy subjects each with the Spectralis and the Stratus OCT. Comparability between different devices was evaluated in 84 C-scans (volume or radial scans) obtained from 21 healthy subjects, two scans per subject with the Spectralis OCT, and one scan per subject each with the Stratus OCT and the RTVue-100 OCT. Each C-scan was segmented and the mean thickness for each retinal layer in sections of the early treatment of diabetic retinopathy study (ETDRS) grid was measured.The algorithm was able to segment up to 11 intraretinal layers. Measurements with the algorithm were within the 95% confidence interval of a single grader and the difference was smaller than the interindividual difference between the expert graders themselves. The cross-device examination of ETDRS-grid related layer thicknesses highly agreed between the three OCT devices. The algorithm correctly segmented a C-scan of a patient with X-linked retinitis pigmentosa.The segmentation software provides device-independent, reliable, and reproducible analysis of intraretinal layers, similar to what is obtained from expert graders.Potential application of the software includes routine clinical practice and multicenter clinical trials.
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Project description:Over the past two decades a significant number of OCT segmentation approaches have been proposed in the literature. Each methodology has been conceived for and/or evaluated using specific datasets that do not reflect the complexities of the majority of widely available retinal features observed in clinical settings. In addition, there does not exist an appropriate OCT dataset with ground truth that reflects the realities of everyday retinal features observed in clinical settings. While the need for unbiased performance evaluation of automated segmentation algorithms is obvious, the validation process of segmentation algorithms have been usually performed by comparing with manual labelings from each study and there has been a lack of common ground truth. Therefore, a performance comparison of different algorithms using the same ground truth has never been performed. This paper reviews research-oriented tools for automated segmentation of the retinal tissue on OCT images. It also evaluates and compares the performance of these software tools with a common ground truth.
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Project description:This study describes a segmentation-free deep learning (DL) algorithm for measuring retinal nerve fibre layer (RNFL) thickness on spectral-domain optical coherence tomography (SDOCT). The study included 25,285 B-scans from 1,338 eyes of 706 subjects. Training was done to predict RNFL thickness from raw unsegmented scans using conventional RNFL thickness measurements from good quality images as targets, forcing the DL algorithm to learn its own representation of RNFL. The algorithm was tested in three different sets: (1) images without segmentation errors or artefacts, (2) low-quality images with segmentation errors, and (3) images with other artefacts. In test set 1, segmentation-free RNFL predictions were highly correlated with conventional RNFL thickness (r = 0.983, P < 0.001). In test set 2, segmentation-free predictions had higher correlation with the best available estimate (tests with good quality taken in the same date) compared to those from the conventional algorithm (r = 0.972 vs. r = 0.829, respectively; P < 0.001). Segmentation-free predictions were also better in test set 3 (r = 0.940 vs. r = 0.640, P < 0.001). In conclusion, a novel segmentation-free algorithm to extract RNFL thickness performed similarly to the conventional method in good quality images and better in images with errors or other artefacts.
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Project description:To analyse the morphological features and diagnostic ability of eight macular retinal layers using a new segmentation software Heidelberg's Spectralis Optical Coherence Tomography (SD-OCT) in healthy, ocular hypertensive and primary open angle glaucoma patients.Single-center, cross-sectional, non-interventional study. 193 eyes from 193 consecutive patients (56 controls, 63 ocular hypertensives, 32 early primary open glaucoma patients and 42 moderate-advanced primary open glaucoma patients). Those patients presenting any retinal disease were excluded. Macular segmentation of the retinal layers was automatically performed using the new segmentation Heidelberg's Spectralis OCT software providing measurements for eight retinal layers. The software provides thickness maps divided into nine subfields.Statistically significant differences in inner layers' thickness was found between all 4 four groups. Superior and inferior sectors of macular retinal nerve fiber layer; nasal, temporal, superior and inferior sectors of ganglion cell layer and inner plexiform layer were significantly different when comparing ocular hypertensive patients and early glaucoma patients. Areas under the ROC curves for early glaucoma diagnosis were 0.781±0.052 for macular retinal nerve fiber layer outer inferior sector, 0.760±0.050 for ganglion cell layer outer temporal sector, 0.767±0.049 for the inner plexiform layer outer temporal sector and 0.807±0.048 for the combination of all three. No differences were found between groups when considering outer retinal layers.The automated segmentation software from Heidelberg's Spectralis OCT provides a new diagnostic tool for the diagnosis of ocular hypertensive and glaucoma patients.
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Project description:PurposeTo investigate the thickness of retinal layers and association with final visual acuity using spectral-domain optical coherence tomography (SD-OCT) in macular area of macula-off rhegmatogenous retinal detachment (RRD) patients after a successful macular re-attachment.MethodsIn retrospective study, a total 24 eyes with macula-off RRD were enrolled. All patients underwent vitrectomy to repair RRD. Outer plexiform layer (OPL), outer nuclear layer (ONL), photoreceptor layer (PR), retinal pigment epithelium (RPE) thicknesses were measured by the Spectralis (Heidelberg Engineering, Heidelberg, Germany) SD-OCT with automated segmentation software. The relationship between the thicknesses of each retinal layer and postoperative logarithm of the minimum angle of resolution scale (LogMAR) visual acuity was analyzed.ResultsOPL and RPE thicknesses were not significantly different between the retinal detachment eyes and fellow eyes (P = 0.839, 0.999, respectively). The ONL and photoreceptor thickness were significantly thinner in the retinal detachment eyes (P <0.001 and 0.001, respectively). In the univariate regression analysis, preoperative best corrected visual acuity (BCVA), ONL thickness and photoreceptor thickness showed association with the postoperative BCVA (P = 0.003, <0.001 and 0.024, respectively). In final multiple linear regression model, ONL thickness was the only variable significantly associated with postoperative BCVA (P = 0.044).ConclusionsSegmented ONL and photoreceptor thickness of retinal detachment eyes were significantly thinner than fellow eyes. Segmental analysis of the retinal layer in macular region may provide valuable information for evaluation RRD. And ONL thickness can be used as a potential biomarker to predict visual outcome after RRD repair.
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Project description:PurposeWe investigate which spectral domain-optical coherence tomography (SD-OCT) setting is superior when measuring subfoveal choroidal thickness (CT) and compared results to an automated segmentation software.MethodsThirty patients underwent enhanced depth imaging (EDI)-OCT. B-scans were extracted in six different settings (W+N = white background/normal contrast 9; W+H = white background/maximum contrast 16; B+N = black background/normal contrast 12; B+H = black background/maximum contrast 16; C+N = Color-encoded image on black background at predefined contrast of 9, and C+H = Color-encoded image on black background at high/maximal contrast of 16), resulting in 180 images. Subfoveal CT was manually measured by nine graders and by automated segmentation software. Intraclass correlation (ICC) was assessed.ResultsICC was higher in normal than in high contrast images, and better for achromatic black than for white background images. Achromatic images were better than color images. Highest ICC was achieved in B+N (ICC = 0.64), followed by B+H (ICC = 0.54), W+N, and W+H (ICC = 0.5 each). Weakest ICC was obtained with Spectral-color (ICC = 0.47). Mean manual CT versus mean computer estimated CT showed a correlation of r = 0.6 (P = 0.001).ConclusionBlack background with white image at normal contrast (B+N) seems the best setting to manually assess subfoveal CT. Automated assessment of CT seems to be a reliable tool for CT assessment.Translational relevanceTo define optimized OCT analysis settings to improve the evaluation of in vivo imaging.
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Project description:To characterize the error of optical coherence tomography (OCT) measurements of retinal nerve fiber layer (RNFL) thickness when using automated retinal layer segmentation algorithms without manual refinement.Cross-sectional study.This study was set in a glaucoma clinical practice, and the dataset included 3490 scans from 412 eyes of 213 individuals with a diagnosis of glaucoma or glaucoma suspect. We used spectral domain OCT (Spectralis) to measure RNFL thickness in a 6-degree peripapillary circle, and exported the native "automated segmentation only" results. In addition, we exported the results after "manual refinement" to correct errors in the automated segmentation of the anterior (internal limiting membrane) and the posterior boundary of the RNFL. Our outcome measures included differences in RNFL thickness and glaucoma classification (i.e., normal, borderline, or outside normal limits) between scans with automated segmentation only and scans using manual refinement.Automated segmentation only resulted in a thinner global RNFL thickness (1.6 μm thinner, P < .001) when compared to manual refinement. When adjusted by operator, a multivariate model showed increased differences with decreasing RNFL thickness (P < .001), decreasing scan quality (P < .001), and increasing age (P < .03). Manual refinement changed 298 of 3486 (8.5%) of scans to a different global glaucoma classification, wherein 146 of 617 (23.7%) of borderline classifications became normal. Superior and inferior temporal clock hours had the largest differences.Automated segmentation without manual refinement resulted in reduced global RNFL thickness and overestimated the classification of glaucoma. Differences increased in eyes with a thinner RNFL thickness, older age, and decreased scan quality. Operators should inspect and manually refine OCT retinal layer segmentation when assessing RNFL thickness in the management of patients with glaucoma.
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Project description:Examining the retinal tissue has the potential to provide a unique method and technique to quantify Alzheimer's disease-related changes in participants at various stages of the disease. In this meta-analysis, we aimed to investigate the association of various optical coherence tomography parameters with Alzheimer's disease and whether retinal measurements can be used to differentiate between Alzheimer's disease and control subjects. Scientific databases including Google Scholar, Web of Science, and PubMed were systematically searched for published articles that evaluated retinal nerve fiber layer thickness and retinal microvascular network in Alzheimer's disease and control subjects. Seventy-three studies (5850 participants, including 2249 Alzheimer's disease patients and 3601 controls) were included in this meta-analysis. Relative to controls, Alzheimer's disease patients had a significantly lower global retinal nerve fiber layer thickness (standardized mean difference [SMD] = -0.79, 95% confidence intervals [CI]: -1.03 to -0.54, P < 0.00001) as well as each quadrant being thinner in Alzheimer's disease versus controls. Regarding macular parameters, values measured by optical coherence tomography were significantly lower in Alzheimer's disease than controls for macular thickness (pooled SMD: -0.44, 95% CI: -0.67 to -0.20, P = 0.0003), foveal thickness (pooled SMD = -0.39, 95% CI: -0.58 to -0.19, P < 0.0001), ganglion cell inner plexiform layer (SMD = -1.26, 95% CI: -2.24 to -0.27, P = 0.01) and macular volume (pooled SMD = -0.41, 95% CI -0.76 to -0.07, P = 0.02). Analysis using optical coherence tomography angiography parameters revealed mixed results between Alzheimer's disease and controls. Superficial vessel density (pooled SMD = -0.42, 95% CI: -0.68 to -0.17, P = 0.0001) and deep vessel density (pooled SMD = -0.46, 95% CI: -0.75 to -0.18, P = 0.001) were found to be thinner in Alzheimer's disease patients whereas the foveal avascular zone (SMD = 0.84, 95% CI: 0.17-1.51, P = 0.01) was larger in controls. Vascular density and thickness of various retinal layers were decreased in Alzheimer's disease patients compared to controls. Our results provide evidence for optical coherence tomography technology having the potential to detect retinal and microvascular changes in patients diagnosed with Alzheimer's disease and aid in monitoring and early diagnosis methods.
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Project description:BACKGROUND:Optical Coherence Tomography (OCT) imaging is being increasingly used in clinical practice for the monitoring of papilloedema. The aim is to characterise the extent and location of the Retinal Nerve Fibre Layer (RNFL) Thickness automated segmentation error (SegE) by manual refinement, in a cohort of Idiopathic Intracranial Hypertension (IIH) patients with papilloedema and compare this to controls. METHODS:Baseline Spectral Domain OCT (SDOCT) scans from patients with IIH, and controls with no retinal or optic nerve pathology, were examined. The internal limiting membrane and RNFL thickness of the most severely affected eye was examined for SegE and re-segmented. Using ImageJ, the total area of the RNFL thickness was calculated pre and post re-segmentation and the percentage change was determined. The distribution of RNFL thickness error was qualitatively assessed. RESULTS:Significantly greater SegE (p =?0.009) was present in RNFL thickness total area, assessed using ImageJ, in IIH patients (n =?46, 5%?±?0-58%) compared to controls (n =?14, 1%?±?0-6%). This was particularly evident in moderate to severe optic disc swelling (n =?23, 10%?±?0-58%, p <?0.001). RNFL thickness was unable to be quantified using SDOCT in patients with severe papilloedema. CONCLUSIONS:SegE remain a concern for clinicians using SDOCT to monitor papilloedema in IIH, particularly in the assessment of eyes with moderate to severe oedema. Systematic assessment and manual refinement of SegE is therefore important to ensure the accuracy in longitudinal monitoring of patients.
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Project description:ObjectiveTo map the corneal epithelial thickness with Fourier-domain optical coherence tomography (OCT) and to develop epithelial thickness-based variables for keratoconus detection.DesignCross-sectional observational study.ParticipantsOne hundred forty-five eyes from 76 normal subjects and 35 keratoconic eyes from 22 patients.MethodsA 26,000-Hz Fourier-domain OCT system with 5-?m axial resolution was used. The cornea was imaged with a Pachymetry + Cpwr scan pattern (6-mm scan diameter, 8 radials, 1024 axial-scans each, repeated 5 times) centered on the pupil. Three scans were obtained at a single visit in a prospective study. A computer algorithm was developed to map the corneal epithelial thickness automatically. Zonal epithelial thicknesses and 5 diagnostic variables, including minimum, superior-inferior (S-I), minimum-maximum (MIN-MAX), map standard deviation (MSD), and pattern standard deviation (PSD), were calculated. Repeatability of the measurements was assessed by the pooled standard deviation. The area under the receiver operating characteristic curve (AUC) was used to evaluate diagnostic accuracy.Main outcome measuresDescriptive statistics, repeatability, and AUC of the zonal epithelial thickness and diagnostic variables.ResultsThe central, superior, and inferior epithelial thickness averages were 52.3 ± 3.6 ?m, 49.6 ± 3.5 ?m, and 51.2 ± 3.4 ?m in normal eyes and 51.9 ± 5.3 ?m, 51.2 ± 4.2 ?m, and 49.1 ± 4.3 ?m in keratoconic eyes. Compared with normal eyes, keratoconic eyes had significantly lower inferior (P = 0.03) and minimum (P<0.0001) corneal epithelial thickness, greater S-I (P = 0.013), more negative MIN-MAX (P<0.0001), greater MSD (P<0.0001), and larger PSD (P<0.0001). The repeatability of the zonal average, minimum, S-I, and MIN-MAX epithelial thickness variables were between 0.7 and 1.9 ?m. The repeatability of MSD was better than 0.4 ?m. The repeatability of PSD was 0.02 or better. Among all epithelial thickness-based variables investigated, PSD provided the best diagnostic power (AUC = 1.00). Using an PSD cutoff value of 0.057 alone gave 100% specificity and 100% sensitivity.ConclusionsHigh-resolution Fourier-domain OCT mapped corneal epithelial thickness with good repeatability in both normal and keratoconic eyes. Keratoconus was characterized by apical epithelial thinning. The resulting deviation from the normal epithelial pattern could be detected with very high accuracy using the PSD variable.Financial disclosure(s)Proprietary or commercial disclosure may be found after the references.
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