Spectral-domain Optical Coherence Tomography Volumes Research Articles

Pegcetacoplan Treatment and Consensus Features of Geographic Atrophy Over 24 Months

Despite widespread availability and consensus on its advantages for detailed imaging of geographic atrophy (GA), spectral-domain optical coherence tomography (SD-OCT) might benefit from automated quantitative OCT analyses in GA diagnosis, monitoring, and reporting of its landmark clinical trials. To analyze the association between pegcetacoplan and consensus GA SD-OCT end points. This was a post hoc analysis of 11 614 SD-OCT volumes from 936 of the 1258 participants in 2 parallel phase 3 studies, the Study to Compare the Efficacy and Safety of Intravitreal APL-2 Therapy With Sham Injections in Patients With Geographic Atrophy (GA) Secondary to Age-Related Macular Degeneration (OAKS) and Study to Compare the Efficacy and Safety of Intravitreal APL-2 Therapy With Sham Injections in Patients With Geographic Atrophy (GA) Secondary to Age-Related Macular Degeneration (DERBY). OAKS and DERBY were 24-month, multicenter, randomized, double-masked, sham-controlled studies conducted from August 2018 to July 2020 among adults with GA with total area 2.5 to 17.5 mm2 on fundus autofluorescence imaging (if multifocal, at least 1 lesion ≥1.25 mm2). This analysis was conducted from September to December 2023. Study participants received pegcetacoplan, 15 mg per 0.1-mL intravitreal injection, monthly or every other month, or sham injection monthly or every other month. The primary end point was the least squares mean change from baseline in area of retinal pigment epithelium and outer retinal atrophy in each of the 3 treatment arms (pegcetacoplan monthly, pegcetacoplan every other month, and pooled sham [sham monthly and sham every other month]) at 24 months. Feature-specific area analysis was conducted by Early Treatment Diabetic Retinopathy Study (ETDRS) regions of interest (ie, foveal, parafoveal, and perifoveal). Among 936 participants, the mean (SD) age was 78.5 (7.22) years, and 570 participants (60.9%) were female. Pegcetacoplan, but not sham treatment, was associated with reduced growth rates of SD-OCT biomarkers for GA for up to 24 months. Reductions vs sham in least squares mean (SE) change from baseline of retinal pigment epithelium and outer retinal atrophy area were detectable at every time point from 3 through 24 months (least squares mean difference vs pooled sham at month 24, pegcetacoplan monthly: -0.86 mm2; 95% CI, -1.15 to -0.57; P < .001; pegcetacoplan every other month: -0.69 mm2; 95% CI, -0.98 to -0.39; P < .001). This association was more pronounced with more frequent dosing (pegcetacoplan monthly vs pegcetacoplan every other month at month 24: -0.17 mm2; 95% CI, -0.43 to 0.08; P = .17). Stronger associations were observed in the parafoveal and perifoveal regions for both pegcetacoplan monthly and pegcetacoplan every other month. These findings offer additional insight into the potential effects of pegcetacoplan on the development of GA, including potential effects on the retinal pigment epithelium and photoreceptors. ClinicalTrials.gov Identifiers: NCT03525600 and NCT03525613.

Intraretinal Hyper-Reflective Foci Are Almost Universally Present and Co-Localize With Intraretinal Fluid in Diabetic Macular Edema.

In diabetic macular edema (DME), hyper-reflective foci (HRF) has been linked to disease severity and progression. Using an automated approach, we aimed to investigate the baseline distribution of HRF in DME and their co-localization with cystoid intraretinal fluid (IRF). Baseline spectral-domain optical coherence tomography (SD-OCT) volume scans (N = 1527) from phase III clinical trials YOSEMITE (NCT03622580) and RHINE (NCT03622593) were segmented using a deep-learning-based algorithm (developed using B-scans from BOULEVARD NCT02699450) to detect HRF. The HRF count and volume were assessed. HRF distributions were analyzed in relation to best-corrected visual acuity (BCVA), central subfield thickness (CST), and IRF volume in quartiles, and Diabetic Retinopathy Severity Scores (DRSS) in groups. Co-localization of HRF with IRF was calculated in the central 3-mm diameter using the en face projection. HRF were present in most patients (up to 99.7%). Median (interquartile range [IQR]) HRF volume within the 3-mm diameter Early Treatment Diabetic Retinopathy Study ring was 1964.3 (3325.2) pL, and median count was 64.0 (IQR = 96.0). Median HRF volumes were greater with decreasing BCVA (nominal P = 0.0109), and increasing CST (nominal P < 0.0001), IRF (nominal P < 0.0001), and DRSS up to very severe nonproliferative diabetic retinopathy (nominal P < 0.0001). HRF co-localized with IRF in the en face projection. Using automated HRF segmentation of full SD-OCT volumes, we observed that HRF are a ubiquitous feature in DME and exhibit relationships with BCVA, CST, IRF, and DRSS, supporting a potential link to disease severity. The spatial distribution of HRF closely followed that of IRF.

Deep Learning-Facilitated Study of the Rate of Change in Photoreceptor Outer Segment Metrics in RPGR-Related X-Linked Retinitis Pigmentosa.

The aim of this retrospective cohort study was to obtain three-dimensional (3D) photoreceptor outer segment (OS) metrics measurements with the assistance of a deep learning model (DLM) and to evaluate the longitudinal change in OS metrics and associated factors in retinitis pigmentosa GTPase regulator (RPGR) X-linked retinitis pigmentosa (XLRP). The study included 34 male patients with RPGR-associated XLRP who had preserved ellipsoid zone (EZ) within their spectral-domain optical coherence tomography volume scans and an approximate 2-year or longer follow-up. Volume scans were segmented using a DLM with manual correction for EZ and apical retinal pigment epithelium (RPE). OS metrics were measured from 3D EZ-RPE layers of volume scans. Linear mixed-effects models were used to calculate the rate of change in OS metrics and the associated factors, including baseline age, baseline OS metrics, and follow-up duration. The mean (standard deviation) of progression rates were -0.28 (0.43) µm/y, -0.73 (0.61) mm2/y, and -0.014 (0.012) mm3/y for OS thickness, EZ area, and OS volume, respectively. In multivariable analysis, the progression rates of EZ area and OS volume were strongly associated with their baseline values, with faster decline in eyes with larger baseline values (P ≤ 0.003), and nonlinearly associated with the baseline age (P ≤ 0.003). OS thickness decline was not associated with its baseline value (P = 0.32). These results provide evidence to support using OS metrics as biomarkers to assess the progression of XLRP and as the outcome measures of clinical trials. Given that their progression rates are dependent on their baseline values, the baseline EZ area and OS volume should be considered in the design and statistical analysis of future clinical trials. Deep learning may provide a useful tool to reduce the burden of human graders to analyze OCT scan images and to facilitate the assessment of disease progression and treatment trials for retinitis pigmentosa.

A Deep-Learning Algorithm to Predict Short-Term Progression to Geographic Atrophy on Spectral-Domain Optical Coherence Tomography

The identification of patients at risk of progressing from intermediate age-related macular degeneration (iAMD) to geographic atrophy (GA) is essential for clinical trials aimed at preventing disease progression. DeepGAze is a fully automated and accurate convolutional neural network-based deep learning algorithm for predicting progression from iAMD to GA within 1 year from spectral-domain optical coherence tomography (SD-OCT) scans. To develop a deep-learning algorithm based on volumetric SD-OCT scans to predict the progression from iAMD to GA during the year following the scan. This retrospective cohort study included participants with iAMD at baseline and who either progressed or did not progress to GA within the subsequent 13 months. Participants were included from centers in 4 US states. Data set 1 included patients from the Age-Related Eye Disease Study 2 AREDS2 (Ancillary Spectral-Domain Optical Coherence Tomography) A2A study (July 2008 to August 2015). Data sets 2 and 3 included patients with imaging taken in routine clinical care at a tertiary referral center and associated satellites between January 2013 and January 2023. The stored imaging data were retrieved for the purpose of this study from July 1, 2022, to February 1, 2023. Data were analyzed from May 2021 to July 2023. A position-aware convolutional neural network with proactive pseudointervention was trained and cross-validated on Bioptigen SD-OCT volumes (data set 1) and validated on 2 external data sets comprising Heidelberg Spectralis SD-OCT scans (data sets 2 and 3). Prediction of progression to GA within 13 months was evaluated with area under the receiver-operator characteristic curves (AUROC) as well as area under the precision-recall curve (AUPRC), sensitivity, specificity, positive predictive value, negative predictive value, and accuracy. The study included a total of 417 patients: 316 in data set 1 (mean [SD] age, 74 [8]; 185 [59%] female), 53 in data set 2, (mean [SD] age, 83 [8]; 32 [60%] female), and 48 in data set 3 (mean [SD] age, 81 [8]; 32 [67%] female). The AUROC for prediction of progression from iAMD to GA within 1 year was 0.94 (95% CI, 0.92-0.95; AUPRC, 0.90 [95% CI, 0.85-0.95]; sensitivity, 0.88 [95% CI, 0.84-0.92]; specificity, 0.90 [95% CI, 0.87-0.92]) for data set 1. The addition of expert-annotated SD-OCT features to the model resulted in no improvement compared to the fully autonomous model (AUROC, 0.95; 95% CI, 0.92-0.95; P = .19). On an independent validation data set (data set 2), the model predicted progression to GA with an AUROC of 0.94 (95% CI, 0.91-0.96; AUPRC, 0.92 [0.89-0.94]; sensitivity, 0.91 [95% CI, 0.74-0.98]; specificity, 0.80 [95% CI, 0.63-0.91]). At a high-specificity operating point, simulated clinical trial recruitment was enriched for patients progressing to GA within 1 year by 8.3- to 20.7-fold (data sets 2 and 3). The fully automated, position-aware deep-learning algorithm assessed in this study successfully predicted progression from iAMD to GA over a clinically meaningful time frame. The ability to predict imminent GA progression could facilitate clinical trials aimed at preventing the condition and could guide clinical decision-making regarding screening frequency or treatment initiation.

The Double Layer Sign Is Highly Predictive of Progression to Exudation in Age-Related Macular Degeneration

The presences of a double layer sign (DLS) and a shallow irregular retinal pigment epithelium (RPE) elevation (SIRE) were investigated using spectral domain-OCT (SD-OCT) imaging to determine their ability to predict progression to exudative macular neovascularization (eMNV) in the unaffected fellow eyes (study eye) of participants with age-related macular degeneration (AMD) with newly diagnosed unilateral eMNV. Retrospective, reanalysis of SD-OCT scans of study eyes from the Early Detection of Neovascular AMD (EDNA) study with 3 years follow-up (FU). The EDNA study repository of SD-OCT scans was assessed for inclusion. Cases with incomplete data sets, low quality scans, or exhibiting other pathology were excluded, which resulted in 459 eligible cases. Spectral domain-OCT volume scans of study eyes were graded for irregular elevation of the RPE (IE), with length, and height measurements made on the most affected B-scan. Eyes with heterogeneous reflectivity within the IE were classified as exhibiting the DLS. Eyes with DLS where the length of separation between RPE and Bruch's membrane was ≥ 1000 μm in length and < 100 μm in height were subclassified as SIRE. Hazard of progression to eMNV for DLS and SIRE. Of the 459 eyes, 268 had IE, of which 101 were DLS-like and 51 also fulfilled criteria for SIRE. Over the 3 years FU period, 104 (23%) eyes progressed to eMNV. After an FU of 18 months, a significantly higher proportion of study eyes (P < 0.001) with IE, DLS, and SIRE developed eMNV compared with those without these features (IE: 17% vs. no IE 6.3%; DLS: 23% vs. no DLS 9.9%; SIRE: 22% vs. no SIRE 11%). In the adjusted Cox regression models, a significantly greater hazard of progression (P < 0.001) was associated with the presence of IE (adjusted hazard ratio [HR], 3.01; 95% confidence interval [CI], 1.88-4.82), DLS (adjusted HR, 3.41; 95% CI, 2.26-5.14), or SIRE (adjusted HR, 2.83; 95% CI, 1.68-4.75). The DLS is a highly sensitive predictor of progression to eMNV, and the use of SIRE does not improve predictability. Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Monitoring Lesion Area Progression in Stargardt Disease: A Comparison of En Face Optical Coherence Tomography and Fundus Autofluorescence.

To compare longitudinal changes in en face spectral domain-optical coherence tomography (SD-OCT) measurements of ellipsoid zone (EZ) and retinal pigment epithelium (RPE) loss to changes in the hypoautofluorescent and hyperautofluorescent (AF) areas detected with short-wavelength (SW)-AF in ABCA4-associated retinopathy. SD-OCT volume scans were obtained from 20 patients (20 eyes) over 2.6 ± 1.2 years (range 1-5 years). The EZ, and RPE/Bruch's membrane boundaries were segmented, and en face slab images generated. SubRPE and EZ slab images were used to measure areas of atrophic RPE and EZ loss. These were compared to longitudinal measurements of the hypo- and abnormal AF (hypoAF and surrounding hyperAF) areas. At baseline, the en face area of EZ loss was significantly larger than the subRPE atrophic area, and the abnormal AF area was significantly larger than the hypoAF area. The median rate of EZ loss was significantly greater than the rate of increase in the subRPE atrophic area (1.2 mm2/yr compared to 0.5 mm2/yr). The median rate of increase in the abnormal AF area was significantly greater than the increase in the hypoAF area (1.6 mm2/yr compared to 0.6 mm2/yr). En face SD-OCT can be used to quantify changes in RPE atrophy and photoreceptor integrity. It can be a complementary or alternative technique to SW-AF with the advantage of monitoring EZ loss. The SW-AF results emphasize the importance of measuring changes in the hypo- and abnormal AF areas. The findings are relevant to the selection of outcome measures for monitoring ABCA4-associated retinopathy.

Choroidal vessel segmentation in SD-OCT with 3D Shape-aware Adversarial Networks

Background and Objective:Quantitative analysis of choroidal vessels is important for the diagnosis of choroidal related diseases and the cognition of the choroid. Previous studies focused on segmenting choroidal vessels in enhanced depth imaging optical coherence tomography (OCT) and swept-source OCT modalities. There is still a blank for the segmentation of 3D choroidal vessels in the spectral-domain OCT (SD-OCT) modality. Quantitative analysis of choroidal vessels is crucial for diagnosing choroidal-related diseases and understanding the choroid. Previous studies have focused on segmenting choroidal vessels in enhanced depth imaging optical coherence tomography (OCT) and swept-source OCT modalities. There is still a gap in the segmentation of 3D choroidal vessels in the spectral-domain OCT (SD-OCT) modality. Method:We propose a novel 3D CNN-based method, called Shape-aware Adversarial Networks (SAN), for segmenting choroidal vessels in SD-OCT images. To learn the 3D mapping of SD-OCT images and labels, we built a 3D U-shape network backbone with anisotropic down-sampling and up-sampling operations. To learn the morphological characteristics of choroidal vessels, we constructed an auxiliary output branch to predict a newly designed choroidal vessel shape representation generated by labels. We further introduced a label-conditional adversarial loss to enforce shape learning in a patch-based pipeline. The code for SAN is publicly available at: https://github.com/nicetomeetu21/SAN. Results:The effectiveness of SAN has been verified in data with myopia. The proposed model reported as measures of segmentation performance a mean dice similarity coefficient of 85.96% and a mean Hausdorff distance of 2.54, outperforming other 2D and 3D segmentation methods. We further applied our method in the statistical analysis of the choroidal feature between 100 eyes with low to moderate myopia and 108 eyes with high myopia. The results show that in all Early Treatment of Diabetic Retinopathy Study subfields, the choroid volumes and vascular volumes of the two groups are significantly different (P<0.01). The choroidal vascular indexes (CVI) of two groups are significantly different in most subfields (P<0.01) except for central and nasal inner subfields. The CVI of the high myopia group is higher than that of the low to moderate myopia group in most subfields except for the nasal inner subfield. Conclusions:The results demonstrate that the proposed method can accurately segment choroidal vessels from SD-OCT volumes. The quantitative analysis of choroidal biomarkers in a total of 208 myopia eyes shows the applicability of the proposed method in helping to study the choroid.

Characteristics of intermediate age-related macular degeneration with hyperreflective foci

Hyperreflective foci (HRF) are the findings observed in optical coherence tomography (OCT) in several retinal diseases and are believed to be associated with the increased risk of atrophy in eyes with age-related macular degeneration (AMD). In this study, we investigated the clinical and genetic characteristics of intermediate AMD with HRF. We reviewed the medical charts for 155 patients with intermediate AMD, in whom macular neovascularization (MNV) was observed in the contralateral eye. The presence or absence of an HRF was evaluated using a spectral-domain OCT volume scan spanning the macular region. Patients were followed longitudinally for at least 12 months, and the maximum follow-up period was 60 months. Genotyping of ARMS2 A69S and CFH I62V was performed in all participants. Of the 155 patients (mean age: 77.8 ± 7.6 years, male/female: 103/52), HRF was observed in 53 eyes (34.2%) and was significantly associated with type-3 MNV (p = 1.0 × 10−5) in the contralateral eye, pseudodrusen (p = 5.0 × 10−4), thinner subfoveal choroidal thickness (p = 0.013), and risk of ARMS2 A69S (p = 0.023). During follow-up (40.8 ± 17.5), 38 eyes (24.5%) developed advanced AMD. The mean time to the onset of advanced AMD was 29.8 ± 12.9 months in eyes with intermediate AMD. HRF was associated with MNV (p = 1.0 × 10−3), but not with atrophy.

Photoreceptor degeneration in ABCA4-associated retinopathy and its genetic correlates.

BACKGROUNDOutcome measures sensitive to disease progression are needed for ATP-binding cassette, sub-family A, member 4–associated (ABCA4-associated) retinopathy. We aimed to quantify ellipsoid zone (EZ) loss and photoreceptor degeneration beyond EZ-loss in ABCA4-associated retinopathy and investigate associations between photoreceptor degeneration, genotype, and age.METHODSWe analyzed 132 eyes from 66 patients (of 67 enrolled) with molecularly confirmed ABCA4-associated retinopathy from a prospective natural history study with a median [IQR] follow-up of 4.2 years [3.1, 5.1]. Longitudinal spectral-domain optical coherence tomography volume scans (37 B-scans, 30° × 15°) were segmented using a deep learning (DL) approach. For genotype-phenotype analysis, a model of ABCA4 variants was applied with the age of criterion EZ-loss (6.25 mm2) as the dependent variable.RESULTSPatients exhibited an average (square-root-transformed) EZ-loss progression rate of [95% CI] 0.09 mm/y [0.06, 0.11]. Outer nuclear layer (ONL) thinning extended beyond the area of EZ-loss. The average distance from the EZ-loss boundary to normalization of ONL thickness (to ±2 z score units) was 3.20° [2.53, 3.87]. Inner segment (IS) and outer segment (OS) thinning was less pronounced, with an average distance from the EZ-loss boundary to layer thickness normalization of 1.20° [0.91, 1.48] for the IS and 0.60° [0.49, 0.72] for the OS. An additive model of allele severity explained 52.7% of variability in the age of criterion EZ-loss.CONCLUSIONPatients with ABCA4-associated retinopathy exhibited significant alterations of photoreceptors outside of EZ-loss. DL-based analysis of photoreceptor laminae may help monitor disease progression and estimate the severity of ABCA4 variants.TRIAL REGISTRATIONClinicalTrials.gov identifier: NCT01736293.FUNDINGNational Eye Institute Intramural Research Program and German Research Foundation grant PF950/1-1.

Fundus autofluorescence and optical coherence tomography biomarkers associated with the progression of geographic atrophy secondary to age-related macular degeneration.

To investigate the impact of qualitatively graded and deep learning quantified imaging biomarkers on growth of geographic atrophy (GA) secondary to age-related macular degeneration. This prospective study included 1062 visits of 181 eyes of 100 patients with GA. Spectral-domain optical coherence tomography (SD-OCT) and fundus autofluorescence (FAF) images were acquired at each visit. Hyperreflective foci (HRF) were quantitatively assessed in SD-OCT volumes using a validated deep learning algorithm. FAF images were graded for FAF patterns, subretinal drusenoid deposits (SDD), GA lesion configuration and atrophy enlargement. Linear mixed models were calculated to investigate associations between all parameters and GA progression. FAF patterns were significantly associated with GA progression (p < 0.001). SDD was associated with faster GA growth (p = 0.005). Eyes with higher HRF concentrations showed a trend towards faster GA progression (p = 0.072) and revealed a significant impact on GA enlargement in interaction with FAF patterns (p = 0.01). The fellow eye status had no significant effect on lesion enlargement (p > 0.05). The diffuse-trickling FAF pattern exhibited significantly higher HRF concentrations than any other pattern (p < 0.001). Among a wide range of investigated biomarkers, SDD and FAF patterns, particularly in interaction with HRF, significantly impact GA progression. Fully automated quantification of retinal imaging biomarkers such as HRF is both reliable and merited as HRF are indicators of retinal pigment epithelium dysmorphia, a central pathogenetic mechanism in GA. Identifying disease markers using the combination of FAF and SD-OCT is of high prognostic value and facilitates individualized patient management in a clinical setting.

Deep Learning-Based Retinal Nerve Fiber Layer Thickness Measurement of Murine Eyes.

PurposeTo design a robust and automated estimation method for measuring the retinal nerve fiber layer (RNFL) thickness using spectral domain optical coherence tomography (SD-OCT).MethodsWe developed a deep learning–based image segmentation network for automated segmentation of the RNFL in SD-OCT B-scans of mouse eyes. In total, 5500 SD-OCT B-scans (5200 B-scans were used as training data with the remaining 300 B-scans used as testing data) were used to develop this segmentation network. Postprocessing operations were then applied on the segmentation results to fill any discontinuities or remove any speckles in the RNFL. Subsequently, a three-dimensional retina thickness map was generated by z-stacking 100 segmentation processed thickness B-scan images together. Finally, the average absolute difference between algorithm predicted RNFL thickness compared to the ground truth manual human segmentation was calculated.ResultsThe proposed method achieves an average dice similarity coefficient of 0.929 in the SD-OCT segmentation task and an average absolute difference of 0.0009 mm in thickness estimation task on the basis of the testing dataset. We also evaluated our segmentation algorithm on another biological dataset with SD-OCT volumes for RNFL thickness after the optic nerve crush injury. Results were shown to be comparable between the predicted and manually measured retina thickness values.ConclusionsExperimental results demonstrate that our automated segmentation algorithm reliably predicts the RNFL thickness in SD-OCT volumes of mouse eyes compared to laborious and more subjective manual SD-OCT RNFL segmentation.Translational RelevanceAutomated segmentation using a deep learning–based algorithm for murine eye OCT effectively and rapidly produced nerve fiber layer thicknesses comparable to manual segmentation.

Probabilistic Forecasting of Anti-VEGF Treatment Frequency in Neovascular Age-Related Macular Degeneration.

PurposeTo probabilistically forecast needed anti-vascular endothelial growth factor (anti-VEGF) treatment frequency using volumetric spectral domain–optical coherence tomography (SD-OCT) biomarkers in neovascular age-related macular degeneration from real-world settings.MethodsSD-OCT volume scans were segmented with a custom deep-learning-based analysis pipeline. Retinal thickness and reflectivity values were extracted for the central and the four inner Early Treatment Diabetic Retinopathy Study (ETDRS) subfields for six retinal layers (inner retina, outer nuclear layer, inner segments [IS], outer segments [OS], retinal pigment epithelium-drusen complex [RPEDC] and the choroid). Machine-learning models were probed to predict the anti-VEGF treatment frequency within the next 12 months. Probabilistic forecasting was performed using natural gradient boosting (NGBoost), which outputs a full probability distribution. The mean absolute error (MAE) between the predicted versus actual anti-VEGF treatment frequency was the primary outcome measure.ResultsIn a total of 138 visits of 99 eyes with neovascular AMD (96 patients) from two clinical centers, the prediction of future anti-VEGF treatment frequency was observed with an accuracy (MAE [95% confidence interval]) of 2.60 injections/year [2.25–2.96] (R2 = 0.390) using random forest regression and 2.66 injections/year [2.31–3.01] (R2 = 0.094) using NGBoost, respectively. Prediction intervals were well calibrated and reflected the true uncertainty of NGBoost-based predictions. Standard deviation of RPEDC-thickness in the central ETDRS-subfield constituted an important predictor across models.ConclusionsThe proposed, fully automated pipeline enables probabilistic forecasting of future anti-VEGF treatment frequency in real-world settings.Translational RelevancePrediction of a probability distribution allows the physician to inspect the underlying uncertainty. Predictive uncertainty estimates are essential to highlight cases where human-inspection and/or reversion to a fallback alternative is warranted.

Automated segmentation of choroidal layers from 3-dimensional macular optical coherence tomography scans

BackgroundChanges in choroidal thickness are associated with various ocular diseases, and the choroid can be imaged using spectral-domain optical coherence tomography (SD-OCT) and enhanced depth imaging OCT (EDI-OCT). New methodEighty macular SD-OCT volumes from 80 patients were obtained using the Zeiss Cirrus machine. Eleven additional control subjects had two Cirrus scans done in one visit along with enhanced depth imaging (EDI-OCT) using the Heidelberg Spectralis machine. To automatically segment choroidal layers from the OCT volumes, our graph-theoretic approach was utilized. The segmentation results were compared with reference standards from two independent graders, and the accuracy of automated segmentation was calculated using unsigned/signed border positioning/thickness errors and Dice similarity coefficient (DSC). The repeatability and reproducibility of our choroidal thicknesses were determined by intraclass correlation coefficient (ICC), coefficient of variation (CV), and repeatability coefficient (RC). ResultsThe mean unsigned/signed border positioning errors for the choroidal inner and outer surfaces are 3.39 ± 1.26 µm (mean ± standard deviation)/− 1.52 ± 1.63 µm and 16.09 ± 6.21 µm/4.73 ± 9.53 µm, respectively. The mean unsigned/signed choroidal thickness errors are 16.54 ± 6.47 µm/6.25 ± 9.91 µm, and the mean DSC is 0.949 ± 0.025. The ICC (95% confidence interval), CV, RC values are 0.991 (0.977–0.997), 2.48%, 14.25 µm for the repeatability and 0.991 (0.977–0.997), 2.49%, 14.30 µm for the reproducibility studies, respectively. Comparison with existing method(s)The proposed method outperformed our previous method using choroidal vessel segmentation and inter-grader variability. ConclusionsThis automated segmentation method can reliably measure choroidal thickness using different OCT platforms.

Validating Ellipsoid Zone Area Measurement With Multimodal Imaging in Choroideremia.

PurposeTo assess en face ellipsoid zone (EZ) maps of remaining retinal structure as outcome measures for the future clinical research in patients with choroideremia.MethodsTwenty eyes from 12 patients with a confirmed genetic diagnosis of choroideremia were included retrospectively from a single site. From spectral domain-optical coherence tomography volume scans, slabs including the EZ were manually segmented to create the en face EZ maps. The preserved EZ area was measured by two graders. Lengths of the EZ were recorded at 0°, 45°, 90°, and 135°. The intraclass correlation coefficients and Bland–Altman plots were used to show intergrader agreement. The Pearson correlation coefficient evaluated the correlation between length and area. A Bland–Altman plot compared en face EZ and the preserved fundus autofluorescence area.ResultsMeasurements of EZ area by two graders showed excellent agreement with an intraclass correlation coefficient of 0.992 (95% confidence interval, 0.980–0.997). A Pearson correlation analysis showed that the existing marker for preserved photoreceptor (horizontal EZ length) was correlated with the area (r = 0.722). The average EZ length in four meridians showed a much better correlation with the EZ area (r = 0.929). The fundus autofluorescence area was found to be a mean of 0.45 ± 0.99 mm2 greater than the EZ area.ConclusionsEZ area measurement provides excellent intergrader reliability, although the process is time consuming. We propose a less time-consuming alternative to estimate the EZ by using the average EZ band length in meridians. Our data also suggest that the loss of photoreceptor inner segments is an early change in choroideremia and may happen before the loss of the retinal pigment epithelium.Translational RelevanceEn face EZ mapping is a potential tool for future clinical trials to quantify preserved photoreceptor structure in choroideremia.

Noise Reduction for SD-OCT Using a Structure-Preserving Domain Transfer Approach.

Spectral-domain optical coherence tomography (SD-OCT) images inevitably suffer from multiplicative speckle noise caused by random interference. This study proposes an unsupervised domain adaptation approach for noise reduction by translating the SD-OCT to the corresponding high-quality enhanced depth imaging (EDI)-OCT. We propose a structure-persevered cycle-consistent generative adversarial network for unpaired image-to-image translation, which can be applied to imbalanced unpaired data, and can effectively preserve retinal details based on a structure-specific cross-domain description. It also imposes smoothness by penalizing the intensity variation of the low reflective region between consecutive slices. Our approach was tested on a local data set that consisted of 268 SD-OCT volumes and two public independent validation datasets including 20 SD-OCT volumes and 17 B-scans, respectively. Experimental results show that our method can effectively suppress noise and maintain the retinal structure, compared with other traditional approaches and deep learning methods in terms of qualitative and quantitative assessments. Our proposed method shows good performance for speckle noise reduction and can assist downstream tasks of OCT analysis.

Automated retinal layer segmentation on optical coherence tomography image by combination of structure interpolation and lateral mean filtering

Segmentation of layers in retinal images obtained by optical coherence tomography (OCT) has become an important clinical tool to diagnose ophthalmic diseases. However, due to the susceptibility to speckle noise and shadow of blood vessels etc., the layer segmentation technology based on a single image still fail to reach a satisfactory level. We propose a combination method of structure interpolation and lateral mean filtering (SI-LMF) to improve the signal-to-noise ratio based on one retinal image. Before performing one-dimensional lateral mean filtering to remove noise, structure interpolation was operated to eliminate thickness fluctuations. Then, we used boundary growth method to identify boundaries. Compared with existing segmentations, the method proposed in this paper requires less data and avoids the influence of microsaccade. The automatic segmentation method was verified on the spectral domain OCT volume images obtained from four normal objects, which successfully identified the boundaries of 10 physiological layers, consistent with the results based on the manual determination.

Glaucoma Detection from Raw SD-OCT Volumes: A Novel Approach Focused on Spatial Dependencies

Background and objective:Glaucoma is the leading cause of blindness worldwide. Many studies based on fundus image and optical coherence tomography (OCT) imaging have been developed in the literature to help ophthalmologists through artificial-intelligence techniques. Currently, 3D spectral-domain optical coherence tomography (SD-OCT) samples have become more important since they could enclose promising information for glaucoma detection.To analyse the hidden knowledge of the 3D scans for glaucoma detection, we have proposed, for the first time, a deep-learning methodology based on leveraging the spatial dependencies of the features extracted from the B-scans.Methods:The experiments were performed on a database composed of 176 healthy and 144 glaucomatous SD-OCT volumes centred on the optic nerve head (ONH). The proposed methodology consists of two well-differentiated training stages: a slide-level feature extractor and a volume-based predictive model. The slide-level discriminator is characterised by two new, residual and attention, convolutional modules which are combined via skip-connections with other fine-tuned architectures. Regarding the second stage, we first carried out a data-volume conditioning before extracting the features from the slides of the SD-OCT volumes. Then, Long Short-Term Memory (LSTM) networks were used to combine the recurrent dependencies embedded in the latent space to provide a holistic feature vector, which was generated by the proposed sequential-weighting module (SWM).Results:The feature extractor reports AUC values higher than 0.93 both in the primary and external test sets. Otherwise, the proposed end-to-end system based on a combination of CNN and LSTM networks achieves an AUC of 0.8847 in the prediction stage, which outperforms other state-of-the-art approaches intended for glaucoma detection. Additionally, Class Activation Maps (CAMs) were computed to highlight the most interesting regions per B-scan when discerning between healthy and glaucomatous eyes from raw SD-OCT volumes.Conclusions:The proposed model is able to extract the features from the B-scans of the volumes and combine the information of the latent space to perform a volume-level glaucoma prediction. Our model, which combines residual and attention blocks with a sequential weighting module to refine the LSTM outputs, surpass the results achieved from current state-of-the-art methods focused on 3D deep-learning architectures.

Resolution enhancement and realistic speckle recovery with generative adversarial modeling of micro-optical coherence tomography.

A resolution enhancement technique for optical coherence tomography (OCT), based on Generative Adversarial Networks (GANs), was developed and investigated. GANs have been previously used for resolution enhancement of photography and optical microscopy images. We have adapted and improved this technique for OCT image generation. Conditional GANs (cGANs) were trained on a novel set of ultrahigh resolution spectral domain OCT volumes, termed micro-OCT, as the high-resolution ground truth (∼1 μm isotropic resolution). The ground truth was paired with a low-resolution image obtained by synthetically degrading resolution 4x in one of (1-D) or both axial and lateral axes (2-D). Cross-sectional image (B-scan) volumes obtained from in vivo imaging of human labial (lip) tissue and mouse skin were used in separate feasibility experiments. Accuracy of resolution enhancement compared to ground truth was quantified with human perceptual accuracy tests performed by an OCT expert. The GAN loss in the optimization objective, noise injection in both the generator and discriminator models, and multi-scale discrimination were found to be important for achieving realistic speckle appearance in the generated OCT images. The utility of high-resolution speckle recovery was illustrated by an example of micro-OCT imaging of blood vessels in lip tissue. Qualitative examples applying the models to image data from outside of the training data distribution, namely human retina and mouse bladder, were also demonstrated, suggesting potential for cross-domain transferability. This preliminary study suggests that deep learning generative models trained on OCT images from high-performance prototype systems may have potential in enhancing lower resolution data from mainstream/commercial systems, thereby bringing cutting-edge technology to the masses at low cost.

Automated quantification of macular fluid in retinal diseases and their response to anti-VEGF therapy

AimTo objectively assess disease activity and treatment response in patients with retinal vein occlusion (RVO), neovascular age-related macular degeneration (nAMD) and centre-involved diabetic macular oedema (DME), using artificial intelligence–based fluid...

Fast and Automated Hyperreflective Foci Segmentation Based on Image Enhancement and Improved 3D U-Net in SD-OCT Volumes with Diabetic Retinopathy.

PurposeTo design a robust and automated hyperreflective foci (HRF) segmentation framework for spectral-domain optical coherence tomography (SD-OCT) volumes, especially volumes with low HRF-background contrast.MethodsHRF in retinal SD-OCT volumes appear with low-contrast characteristics that results in the difficulty of HRF segmentation. Therefore to effectively segment the HRF we proposed a fully automated method for HRF segmentation in SD-OCT volumes with diabetic retinopathy (DR). First, we generated the enhanced SD-OCT images from the denoised SD-OCT images with an enhancement method. Then the enhanced images were cascaded with the denoised images as the two-channel input to the network against the low-contrast HRF. Finally, we replaced the standard convolution with slice-wise dilated convolution in the last layer of the encoder path of 3D U-Net to obtain long-range information.ResultsWe evaluated our method using two-fold cross-validation on 33 SD-OCT volumes from 27 patients. The average dice similarity coefficient was 70.73%, which was higher than that of the existing methods with significant difference (P < 0.01).ConclusionsExperimental results demonstrated that the proposed method is faster and achieves more reliable segmentation results than the current HRF segmentation algorithms. We expect that this method will contribute to clinical diagnosis and disease surveillance.Translational RelevanceOur framework for the automated HRF segmentation of SD-OCT volumes may improve the clinical diagnosis of DR.