Retinal Boundary Segmentation in Stargardt Disease Optical Coherence Tomography Images Using Automated Deep Learning

Deep learning aided measurement of outer retinal layer metrics as biomarkers for inherited retinal degenerations: opportunities and challenges

PURPOSE OF REVIEW

The purpose of this review was to provide a summary of currently available retinal imaging and visual function testing methods for assessing inherited retinal degenerations (IRDs), with the emphasis on the application of deep learning (DL) approaches to assist the determination of structural biomarkers for IRDs.

RECENT FINDINGS

(clinical trials for IRDs; discover effective biomarkers as endpoints; DL applications in processing retinal images to detect disease-related structural changes).

SUMMARY

Assessing photoreceptor loss is a direct way to evaluate IRDs. Outer retinal layer structures, including outer nuclear layer, ellipsoid zone, photoreceptor outer segment, RPE, are potential structural biomarkers for IRDs. More work may be needed on structure and function relationship.

Recurrent and Concurrent Prediction of Longitudinal Progression of Stargardt Atrophy and Geographic Atrophy

Stargardt disease and age-related macular degeneration are the leading causes of blindness in the juvenile and geriatric populations, respectively. The formation of atrophic regions of the macula is a hallmark of the end-stages of both diseases. The progression of these diseases is tracked using various imaging modalities, two of the most common being fundus autofluorescence (FAF) imaging and spectral-domain optical coherence tomography (SD-OCT). This study seeks to investigate the use of longitudinal FAF and SD-OCT imaging (month 0, month 6, month 12, and month 18) data for the predictive modelling of future atrophy in Stargardt and geographic atrophy. To achieve such an objective, we develop a set of novel deep convolutional neural networks enhanced with recurrent network units for longitudinal prediction and concurrent learning of ensemble network units (termed ReConNet) which take advantage of improved retinal layer features beyond the mean intensity features. Using FAF images, the neural network presented in this paper achieved mean (± standard deviation, SD) and median Dice coefficients of 0.895 (± 0.086) and 0.922 for Stargardt atrophy, and 0.864 (± 0.113) and 0.893 for geographic atrophy. Using SD-OCT images for Stargardt atrophy, the neural network achieved mean and median Dice coefficients of 0.882 (± 0.101) and 0.906, respectively. When predicting only the interval growth of the atrophic lesions with FAF images, mean (± SD) and median Dice coefficients of 0.557 (± 0.094) and 0.559 were achieved for Stargardt atrophy, and 0.612 (± 0.089) and 0.601 for geographic atrophy. The prediction performance in OCT images is comparably good to that using FAF which opens a new, more efficient, and practical door in the assessment of atrophy progression for clinical trials and retina clinics, beyond widely used FAF. These results are highly encouraging for a high-performance interval growth prediction when more frequent or longer-term longitudinal data are available in our clinics. This is a pressing task for our next step in ongoing research.

Artificial intelligence in retinal disease: clinical application, challenges, and future directions

Retinal diseases are a leading cause of blindness in developed countries, accounting for the largest share of visually impaired children, working-age adults (inherited retinal disease), and elderly individuals (age-related macular degeneration). These conditions need specialised clinicians to interpret multimodal retinal imaging, with diagnosis and intervention potentially delayed. With an increasing and ageing population, this is becoming a global health priority. One solution is the development of artificial intelligence (AI) software to facilitate rapid data processing. Herein, we review research offering decision support for the diagnosis, classification, monitoring, and treatment of retinal disease using AI. We have prioritised diabetic retinopathy, age-related macular degeneration, inherited retinal disease, and retinopathy of prematurity. There is cautious optimism that these algorithms will be integrated into routine clinical practice to facilitate access to vision-saving treatments, improve efficiency of healthcare systems, and assist clinicians in processing the ever-increasing volume of multimodal data, thereby also liberating time for doctor-patient interaction and co-development of personalised management plans.

Endpoints for clinical trials in ophthalmology

With the identification of novel targets, the number of interventional clinical trials in ophthalmology has increased. Visual acuity has for a long time been considered the gold standard endpoint for clinical trials, but in the recent years it became evident that other endpoints are required for many indications including geographic atrophy and inherited retinal disease. In glaucoma the currently available drugs were approved based on their IOP lowering capacity. Some recent findings do, however, indicate that at the same level of IOP reduction, not all drugs have the same effect on visual field progression. For neuroprotection trials in glaucoma, novel surrogate endpoints are required, which may either include functional or structural parameters or a combination of both. A number of potential surrogate endpoints for ophthalmology clinical trials have been identified, but their validation is complicated and requires solid scientific evidence. In this article we summarize candidates for clinical endpoints in ophthalmology with a focus on retinal disease and glaucoma. Functional and structural biomarkers, as well as quality of life measures are discussed, and their potential to serve as endpoints in pivotal trials is critically evaluated.

Using Ensemble OCT-Derived Features beyond Intensity Features for Enhanced Stargardt Atrophy Prediction with Deep Learning

Stargardt disease is the most common form of juvenile-onset macular dystrophy. Spectral-domain optical coherence tomography (SD-OCT) imaging provides an opportunity to directly measure changes to retinal layers due to Stargardt atrophy. Generally, atrophy segmentation and prediction can be conducted using mean intensity feature maps generated from the relevant retinal layers. In this paper, we report an approach using advanced OCT-derived features to augment and enhance data beyond the commonly used mean intensity features for enhanced prediction of Stargardt atrophy with an ensemble deep learning neural network. With all the relevant retinal layers, this neural network architecture achieves a median Dice coefficient of 0.830 for six-month predictions and 0.828 for twelve-month predictions, showing a significant improvement over a neural network using only mean intensity, which achieved Dice coefficients of 0.744 and 0.762 for six-month and twelve-month predictions, respectively. When using feature maps generated from different layers of the retina, significant differences in performance were observed. This study shows promising results for using multiple OCT-derived features beyond intensity for assessing the prognosis of Stargardt disease and quantifying the rate of progression.

Choroidal vascularity index (CVI)-Net-based automatic assessment of diabetic retinopathy severity using CVI in optical coherence tomography images

A deep learning model called choroidal vascularity index (CVI)-Net is proposed to automatically segment the choroid layer and its vessels in overall optical coherence tomography (OCT) scans. Clinical parameters are then automatically quantified to determine structural and vascular changes in the choroid with the progression of diabetic retinopathy (DR) severity. The study includes 65 eyes consisting of 34 with proliferative DR (PDR), 17 with nonproliferative DR (NPDR), and 14 healthy controls from two OCT systems. On a dataset of 396 OCT B-scan images with manually annotated ground truths, overall Dice coefficients of 96.6 ± 1.5 and 89.1 ± 3.1 are obtained by CVI-Net for the choroid layer and vessel segmentation, respectively. The mean CVI values among the normal, NPDR, and PDR groups are consistent with reported outcomes. Statistical results indicate that CVI shows a significant negative correlation with DR severity level, and this correlation is independent of changes in other physiological parameters.

Estimation of Visual Function Using Deep Learning From Ultra-Widefield Fundus Images of Eyes With Retinitis Pigmentosa

Importance

There is no widespread effective treatment to halt the progression of retinitis pigmentosa. Consequently, adequate assessment and estimation of residual visual function are important clinically.

Objective

To examine whether deep learning can accurately estimate the visual function of patients with retinitis pigmentosa by using ultra-widefield fundus images obtained on concurrent visits.

Design, Setting, and Participants

Data for this multicenter, retrospective, cross-sectional study were collected between January 1, 2012, and December 31, 2018. This study included 695 consecutive patients with retinitis pigmentosa who were examined at 5 institutions. Each of the 3 types of input images-ultra-widefield pseudocolor images, ultra-widefield fundus autofluorescence images, and both ultra-widefield pseudocolor and fundus autofluorescence images-was paired with 1 of the 31 types of ensemble models constructed from 5 deep learning models (Visual Geometry Group-16, Residual Network-50, InceptionV3, DenseNet121, and EfficientNetB0). We used 848, 212, and 214 images for the training, validation, and testing data, respectively. All data from 1 institution were used for the independent testing data. Data analysis was performed from June 7, 2021, to December 5, 2022.

Main Outcomes and Measures

The mean deviation on the Humphrey field analyzer, central retinal sensitivity, and best-corrected visual acuity were estimated. The image type-ensemble model combination that yielded the smallest mean absolute error was defined as the model with the best estimation accuracy. After removal of the bias of including both eyes with the generalized linear mixed model, correlations between the actual values of the testing data and the estimated values by the best accuracy model were examined by calculating standardized regression coefficients and P values.

Results

The study included 1274 eyes of 695 patients. A total of 385 patients were female (55.4%), and the mean (SD) age was 53.9 (17.2) years. Among the 3 types of images, the model using ultra-widefield fundus autofluorescence images alone provided the best estimation accuracy for mean deviation, central sensitivity, and visual acuity. Standardized regression coefficients were 0.684 (95% CI, 0.567-0.802) for the mean deviation estimation, 0.697 (95% CI, 0.590-0.804) for the central sensitivity estimation, and 0.309 (95% CI, 0.187-0.430) for the visual acuity estimation (all P < .001).

Conclusions and Relevance

Results of this study suggest that the visual function estimation in patients with retinitis pigmentosa from ultra-widefield fundus autofluorescence images using deep learning might help assess disease progression objectively. Findings also suggest that deep learning models might monitor the progression of retinitis pigmentosa efficiently during follow-up.

Longitudinal Analysis of Functional and Structural Outcome Measures in PRPH2-Associated Retinal Dystrophy

PURPOSE

To establish disease progression rates in total lesion size (TLS), decreased autofluorescence (DAF) area, total macular volume (TMV), and mean macular sensitivity (MMS) in PRPH2-associated retinal dystrophy.

DESIGN

Single-center, retrospective chart review.

PARTICIPANTS

Patients with heterozygous pathogenic or likely pathogenic PRPH2 variants.

METHODS

Patients who underwent serial ultrawide-field (UWF) fundus autofluorescence (FAF), OCT, and Macular Integrity Assessment microperimetry with at least 1 year of follow-up were included. Linear correlation was performed in eyes of all patients to determine the rate of change over time.

MAIN OUTCOME MEASURES

Outcome measures included changes in TLS, DAF area, TMV, and MMS.

RESULTS

Twelve patients (mean age, 55) from 10 unrelated families attended 100 clinic visits, which spanned over a mean (SD) of 4.7 (2.0) years. Mean (SD) TLS and DAF radius expansion were 0.14 (0.12) and 0.10 (0.08) mm/year, respectively. Mean (SD) TMV change was -0.071 (0.040) mm³/year with no interocular difference (P = 0.20) and strong interocular correlation (r² = 0.88, P < 0.01). Mean (SD) MMS change was -0.10 (1.25) dB/year. Mean macular sensitivity declined in 4 and improved in 6 patients. Mean macular sensitivity was subnormal despite a TMV within the normal range.

CONCLUSIONS

Serial measurements of UWF-FAF-derived TLS and DAF showed slow expansion. Total macular volume might be a more sensitive measure than MMS in detecting disease progression.

LOCTseg: A lightweight fully convolutional network for end-to-end optical coherence tomography segmentation

This article presents a novel end-to-end automatic solution for semantic segmentation of optical coherence tomography (OCT) images. OCT is a non-invasive imaging technology widely used in clinical practice due to its ability to acquire high-resolution cross-sectional images of the ocular fundus. Due to the large variability of the retinal structures, OCT segmentation is usually carried out manually and requires expert knowledge. This study introduces a novel fully convolutional network (FCN) architecture designated by LOCTSeg, for end-to-end automatic segmentation of diagnostic markers in OCT b-scans. LOCTSeg is a lightweight deep FCN optimized for balancing performance and efficiency. Unlike state-of-the-art FCNs used in image segmentation, LOCTSeg achieves competitive inference speed without sacrificing segmentation accuracy. The proposed LOCTSeg is evaluated on two publicly available benchmarking datasets: (1) annotated retinal OCT image database (AROI) comprising 1136 images, and (2) healthy controls and multiple sclerosis lesions (HCMS) consisting of 1715 images. Moreover, we evaluated the proposed LOCTSeg with a private dataset of 250 OCT b-scans acquired from epiretinal membrane (ERM) and healthy patients. Results of the evaluation demonstrate empirically the effectiveness of the proposed algorithm, which improves the state-of-the-art Dice score from 69% to 73% and from 91% to 92% on AROI and HCMS datasets, respectively. Furthermore, LOCTSeg outperforms comparable lightweight FCNs' Dice score by margins between 4% and 15% on ERM segmentation.

A comparison of deep learning U-Net architectures for posterior segment OCT retinal layer segmentation

Deep learning methods have enabled a fast, accurate and automated approach for retinal layer segmentation in posterior segment OCT images. Due to the success of semantic segmentation methods adopting the U-Net, a wide range of variants and improvements have been developed and applied to OCT segmentation. Unfortunately, the relative performance of these methods is difficult to ascertain for OCT retinal layer segmentation due to a lack of comprehensive comparative studies, and a lack of proper matching between networks in previous comparisons, as well as the use of different OCT datasets between studies. In this paper, a detailed and unbiased comparison is performed between eight U-Net architecture variants across four different OCT datasets from a range of different populations, ocular pathologies, acquisition parameters, instruments and segmentation tasks. The U-Net architecture variants evaluated include some which have not been previously explored for OCT segmentation. Using the Dice coefficient to evaluate segmentation performance, minimal differences were noted between most of the tested architectures across the four datasets. Using an extra convolutional layer per pooling block gave a small improvement in segmentation performance for all architectures across all four datasets. This finding highlights the importance of careful architecture comparison (e.g. ensuring networks are matched using an equivalent number of layers) to obtain a true and unbiased performance assessment of fully semantic models. Overall, this study demonstrates that the vanilla U-Net is sufficient for OCT retinal layer segmentation and that state-of-the-art methods and other architectural changes are potentially unnecessary for this particular task, especially given the associated increased complexity and slower speed for the marginal performance gains observed. Given the U-Net model and its variants represent one of the most commonly applied image segmentation methods, the consistent findings across several datasets here are likely to translate to many other OCT datasets and studies. This will provide significant value by saving time and cost in experimentation and model development as well as reduced inference time in practice by selecting simpler models.

Deep learning: applications in retinal and optic nerve diseases

Deep learning (DL) represents a paradigm-shifting, burgeoning field of research with emerging clinical applications in optometry. Unlike traditional programming, which relies on human-set specific rules, DL works by exposing the algorithm to a large amount of annotated data and allowing the software to develop its own set of rules (i.e. learn) by adjusting the parameters inside the model (network) during a training process in order to complete the task on its own. One major limitation of traditional programming is that, with complex tasks, it may require an extensive set of rules to accurately complete the assignment. Additionally, traditional programming can be susceptible to human bias from programmer experience. With the dramatic increase in the amount and the complexity of clinical data, DL has been utilised to automate data analysis and thus to assist clinicians in patient management. This review will present the latest advances in DL, for managing posterior eye diseases as well as DL-based solutions for patients with vision loss.

OCT Retinal and Choroidal Layer Instance Segmentation Using Mask R-CNN

Optical coherence tomography (OCT) of the posterior segment of the eye provides high-resolution cross-sectional images that allow visualization of individual layers of the posterior eye tissue (the retina and choroid), facilitating the diagnosis and monitoring of ocular diseases and abnormalities. The manual analysis of retinal OCT images is a time-consuming task; therefore, the development of automatic image analysis methods is important for both research and clinical applications. In recent years, deep learning methods have emerged as an alternative method to perform this segmentation task. A large number of the proposed segmentation methods in the literature focus on the use of encoder–decoder architectures, such as U-Net, while other architectural modalities have not received as much attention. In this study, the application of an instance segmentation method based on region proposal architecture, called the Mask R-CNN, is explored in depth in the context of retinal OCT image segmentation. The importance of adequate hyper-parameter selection is examined, and the performance is compared with commonly used techniques. The Mask R-CNN provides a suitable method for the segmentation of OCT images with low segmentation boundary errors and high Dice coefficients, with segmentation performance comparable with the commonly used U-Net method. The Mask R-CNN has the advantage of a simpler extraction of the boundary positions, especially avoiding the need for a time-consuming graph search method to extract boundaries, which reduces the inference time by 2.5 times compared to U-Net, while segmenting seven retinal layers.

Application of Deep Learning Methods for Binarization of the Choroid in Optical Coherence Tomography Images

Purpose

The purpose of this study was to develop a deep learning model for automatic binarization of the choroidal tissue, separating choroidal blood vessels from nonvascular stromal tissue, in optical coherence tomography (OCT) images from healthy young subjects.

Methods

OCT images from an observational longitudinal study of 100 children were used for training, validation, and testing of 5 fully semantic networks, which provided a binarized output of the choroid. These outputs were compared with ground truth images, generated from a local binarization technique after manually optimizing the analysis window size for each individual image. The performance was evaluated using accuracy and repeatability metrics. The methods were also compared with a fixed window size local binarization technique, which has been commonly used previously.

Results

The tested deep learning methods provided a good performance in terms of accuracy and repeatability. With the U-Net and SegNet networks showing >96% accuracy. All methods displayed a high level of repeatability relative to the ground truth. For analysis of the choroidal vascularity index (a commonly used metric derived from the binarized image), SegNet showed the closest agreement with the ground truth and high repeatability. The fixed window size showed a reduced accuracy compared to other methods.

Conclusions

Fully semantic networks such as U-Net and SegNet displayed excellent performance for the binarization task. These methods provide a useful approach for clinical and research applications of deep learning tools for the binarization of the choroid in OCT images.

Translational Relevance

Deep learning models provide a novel, robust solution to automatically binarize the choroidal tissue in OCT images.

Genotype-Specific Lesion Growth Rates in Stargardt Disease

Reported growth rates (GR) of atrophic lesions in Stargardt disease (STGD1) vary widely. In the present study, we report the longitudinal natural history of patients with confirmed biallelic ABCA4 mutations from five genotype groups: c.6079C>T, c.[2588G>C;5603A>T], c.3113C>T, c.5882G>A and c.5603A>T. Fundus autofluorescence (AF) 30° × 30° images were manually segmented for boundaries of definitely decreased autofluorescence (DDAF). The primary outcome was the effective radius GR across five genotype groups. The age of DDAF formation in each eye was calculated using the x-intercept of the DDAF effective radius against age. Discordance between age at DDAF formation and symptom onset was compared. A total of 75 eyes from 39 STGD1 patients (17 male [44%]; mean ± SD age 45 ± 19 years; range 21-86) were recruited. Patients with c.3113C>T or c.6079C>T had a significantly faster effective radius GR at 0.17 mm/year (95% CI 0.12 to 0.22; p < 0.001 and 0.14 to 0.21; p < 0.001) respectively, as compared to those patients harbouring c.5882G>A at 0.06 mm/year (95% CI 0.03-0.09), respectively. Future clinical trial design should consider the effect of genotype on the effective radius GR and the timing of DDAF formation relative to symptom onset.

A Hybrid Model Composed of Two Convolutional Neural Networks (CNNs) for Automatic Retinal Layer Segmentation of OCT Images in Retinitis Pigmentosa (RP)

Purpose

We propose and evaluate a hybrid model composed of two convolutional neural networks (CNNs) with different architectures for automatic segmentation of retina layers in spectral domain optical coherence tomography (SD-OCT) B-scans of retinitis pigmentosa (RP).

Methods

The hybrid model consisted of a U-Net for initial semantic segmentation and a sliding-window (SW) CNN for refinement by correcting the segmentation errors of U-Net. The U-Net construction followed Ronneberger et al. (2015) with an input image size of 256 × 32. The SW model was similar to our previously reported approach. Training image patches were generated from 480 horizontal midline B-scans obtained from 220 patients with RP and 20 normal participants. Testing images were 160 midline B-scans from a separate group of 80 patients with RP. The Spectralis segmentation of B-scans was manually corrected for the boundaries of the inner limiting membrane, inner nuclear layer, ellipsoid zone (EZ), retinal pigment epithelium, and Bruch's membrane by one grader for the training set and two for the testing set. The trained U-Net and SW, as well as the hybrid model, were used to classify all pixels in the testing B-scans. Bland–Altman and correlation analyses were conducted to compare layer boundary lines, EZ width, and photoreceptor outer segment (OS) length and area determined by the models to those by human graders.

Results

The mean times to classify a B-scan image were 0.3, 65.7, and 2.4 seconds for U-Net, SW, and the hybrid model, respectively. The mean ± SD accuracies to segment retinal layers were 90.8% ± 4.8% and 90.7% ± 4.0% for U-Net and SW, respectively. The hybrid model improved mean ± SD accuracy to 91.5% ± 4.8% (P < 0.039 vs. U-Net), resulting in an improvement in layer boundary segmentation as revealed by Bland–Altman analyses. EZ width, OS length, and OS area measured by the models were highly correlated with those measured by the human graders (r > 0.95 for EZ width; r > 0.83 for OS length; r > 0.97 for OS area; P < 0.05). The hybrid model further improved the performance of measuring retinal layer thickness by correcting misclassification of retinal layers from U-Net.

Conclusions

While the performances of U-Net and the SW model were comparable in delineating various retinal layers, U-Net was much faster than the SW model to segment B-scan images. The hybrid model that combines the two improves automatic retinal layer segmentation from OCT images in RP.

Translational Relevance

A hybrid deep machine learning model composed of CNNs with different architectures can be more effective than either model separately for automatic analysis of SD-OCT scan images, which is becoming increasingly necessary with current high-resolution, high-density volume scans.

Stargardt disease and progress in therapeutic strategies

Background: Stargardt disease (STGD1) is an autosomal recessive retinal dystrophy due to mutations in ABCA4, characterized by subretinal deposition of lipofuscin-like substances and bilateral centrifugal vision loss. Despite the tremendous progress made in the understanding of STGD1, there are no approved treatments to date. This review examines the challenges in the development of an effective STGD1 therapy.Materials and Methods: A literature review was performed through to June 2021 summarizing the spectrum of retinal phenotypes in STGD1, the molecular biology of ABCA4 protein, the in vivo and in vitro models used to investigate the mechanisms of ABCA4 mutations and current clinical trials.Results: STGD1 phenotypic variability remains an challenge for clinical trial design and patient selection. Pre-clinical development of therapeutic options has been limited by the lack of animal models reflecting the diverse phenotypic spectrum of STDG1. Patient-derived cell lines have facilitated the characterization of splice mutations but the clinical presentation is not always predicted by the effect of specific mutations on retinoid metabolism in cellular models. Current therapies primarily aim to delay vision loss whilst strategies to restore vision are less well developed.Conclusions: STGD1 therapy development can be accelerated by a deeper understanding of genotype-phenotype correlations.

Stargardt disease: Multimodal imaging: A review

Stargardt disease (STGD1) is an autosomal recessive retinal dystrophy, characterised by bilateral progressive central vision loss and subretinal deposition of lipofuscin-like substances. Recent advances in molecular diagnosis and therapeutic options are complemented by the increasing recognition of new multimodal imaging biomarkers that may predict genotype and disease progression. Unique non-invasive imaging features of STDG1 are useful for gene variant interpretation and may even provide insight into the underlying molecular pathophysiology. In addition, pathognomonic imaging features of STGD1 have been used to train neural networks to improve time efficiency in lesion segmentation and disease progression measurements. This review will discuss the role of key imaging modalities, correlate imaging signs across varied STGD1 presentations and illustrate the use of multimodal imaging as an outcome measure in determining the efficacy of emerging STGD1 specific therapies.

Automatic Segmentation in Multiple OCT Layers For Stargardt Disease Characterization Via Deep Learning

Purpose

This study sought to perform automated segmentation of 11 retinal layers and Stargardt-associated features on spectral-domain optical coherence tomography (SD-OCT) images and to analyze differences between normal eyes and eyes diagnosed with Stargardt disease.

Methods

Automated segmentation was accomplished through application of the deep learning-shortest path (DL-SP) framework, a shortest path segmentation approach that is enhanced by a deep learning fully convolutional neural network. To compare normal eyes and eyes diagnosed with Stargardt disease, various retinal layer thickness and intensity feature maps associated with the outer retinal layers were generated.

Results

The automated DL-SP approach achieved a mean difference within a subpixel accuracy range for all layers when compared to manually traced layers by expert graders. The algorithm achieved mean and absolute mean differences in border positions for Stargardt features of -0.11 ± 4.17 pixels and 1.92 ± 3.71 pixels, respectively. In several of the feature maps generated, the characteristic Stargardt features of flecks and atrophic-appearing lesions were readily visualized.

Conclusions

To the best of our knowledge, this is the first automated algorithm for 11 retinal layer segmentation on OCT in eyes with Stargardt disease, and, furthermore, the feature differences found between eyes diagnosed with Stargardt disease and normal eyes may inform new insights and the better understanding of retinal characteristic morphologic changes caused by Stargardt disease.

Translational Relevance

The automated algorithm's performance and the feature differences found using the algorithm's segmentation support the future applications of SD-OCT for the quantitative monitoring of Stargardt disease.