Retinal Spectral Domain Optical Coherence Research Articles

Retinal nerve fibre layer thinning and corneal nerve loss in patients with Bardet-Biedl syndrome

BackgroundBardet-Biedl syndrome (BBS) is an autosomal recessive, genetically heterogeneous, pleiotropic disorder caused by variants in genes involved in the function of the primary cilium. We have harnessed genomics to identify BBS and ophthalmic technologies to describe novel features of BBS.Case presentationA patient with an unclear diagnosis of syndromic type 2 diabetes mellitus, another affected sibling and unaffected siblings and parents were sequenced using DNA extracted from saliva samples. Corneal confocal microscopy (CCM) and retinal spectral domain optical coherence tomography (SD-OCT) were used to identify novel ophthalmic features in these patients. The two affected individuals had a homozygous variant in C8orf37 (p.Trp185*). SD-OCT and CCM demonstrated a marked and patchy reduction in the retinal nerve fiber layer thickness and loss of corneal nerve fibers, respectively.ConclusionThis report highlights the use of ophthalmic imaging to identify novel retinal and corneal abnormalities that extend the phenotype of BBS in a patient with syndromic type 2 diabetes.

Comparison of image quality of corneal and retinal optical coherence tomography using sedation and general anesthesia protocols with or without retrobulbar anesthesia in horses.

To compare image quality and acquisition time of corneal and retinal spectral domain optical coherence tomography (SD-OCT) under 3 different sedation-anesthesia conditions in horses. 6 middle-aged geldings free of ocular disease. 1 randomly selected eye of each horse was evaluated via SD-OCT under the following 3 conditions: standing sedation without retrobulbar anesthetic block (RB), standing sedation with RB, and general anesthesia with RB. Five regions of interest were evaluated in the cornea (axial and 12, 3, 6, and 9 o'clock positions) and fundus (optic nerve head). Three diagnostic scans of predetermined quality were obtained per anatomical region. Image acquisition times and total scans per site were recorded. Corneal and retinal SD-OCT image quality was graded on a subjective scale from 0 (nondiagnostic) to 4 (excellent). Mean values for the standing sedation without RB, standing sedation with RB, and general anesthesia conditions were 24, 23, and 17, respectively, for total cornea scan attempts; 23, 19, and 19 for total retina-scan attempts; 14.6, 13.2, and 9.2 minutes for total cornea scan time; 19.1, 9.2, and 13.0 for total retina scan time; 2.0, 2.3, and 2.5 for cornea grade; and 2.7, 2.9, and 2.5 for retina grade. The RB facilitated globe akinesia and improved the percentage of scans in frame and region of interest accuracy for retinal imaging via OCT in horses. Retrobulbar blocks improved clinical image acquisition while minimizing motion artifact.

Enhanced Objective Detection of Retinal Nerve Fiber Bundle Defects in Glaucoma With a Novel Method for En Face OCT Slab Image Construction and Analysis.

PurposeTo introduce and evaluate the performance in detecting glaucomatous abnormalities of a novel method for extracting en face slab images (SMAS), which considers varying individual anatomy and configuration of retinal nerve fiber bundles.MethodsDense central retinal spectral domain optical coherence tomography scans were acquired in 16 participants with glaucoma and 19 age-similar controls. Slab images were generated by averaging reflectivity over different depths below the inner limiting membrane according to several methods. SMAS considered multiple 16 µm thick slabs from 8 to 116 µm below the inner limiting membrane, whereas 5 alternative methods considered single summary slabs of various thicknesses and depths. Superpixels in eyes with glaucoma were considered abnormal if below the first percentile of distributions fitted to control data for each method. The ability to detect glaucoma defects was measured by the proportion of abnormal superpixels. Proportion of superpixels below the fitted first percentile in controls was used as a surrogate false-positive rate. The effects of slab methods on performance measures were evaluated with linear mixed models.ResultsThe ability to detect glaucoma defects varied between slab methods, χ2(5) = 120.9, P < 0.0001, with SMAS showing proportion of abnormal superpixels 0.05 to 0.09 greater than alternatives (all P < 0.0001). No slab method found abnormal superpixels in controls.ConclusionsSMAS outperformed alternatives in detecting abnormalities in eyes with glaucoma. SMAS evaluates all depths with potential retinal nerve fiber bundle presence by combining multiple slabs, resulting in greater detection of reflectance abnormalities with no increase in surrogate false positives.Translational RelevanceSMAS may be used to objectively detect glaucoma defects in en face optical coherence tomography images.

Semi-Automated Labelling of Cystoid Macular Edema in OCT Scans

The analysis of retinal Spectral-Domain Optical Coherence Tomography (SD-OCT) images by trained medical professionals can be used to provide useful insights into various diseases. It is the most popular method of retinal imaging due to its non-invasive nature and the useful information it provides for making an accurate diagnosis. A deep learning approach for automating the segmentation of Cystoid Macular Edema (fluid) in retinal OCT B-Scan images was developed that is consequently used for volumetric analysis of OCT scans. This solution is a fast and accurate semantic segmentation network that makes use of a shortened encoder-decoder UNet like architecture with an integrated Dense ASPP module and Attention Gate for producing an accurate and refined retinal fluid segmentation map. Our system was evaluated against both publicly and privately available datasets; on the former the network achieved a Dice coefficient of 0.804, thus making it the current best performing approach on this dataset, and on the very small and challenging private dataset, it achieved a score of 0.691. Due to the lack of publicly available data in this domain, a Graphical User Interface that aims to semi-automate the labelling process of OCT images was also created, thus greatly simplifying the process of the dataset creation and potentially leading to an increase in labelled data production.

Spotlight on the April 20 Issue

#### Notable in Neurology This Week This issue features an article that investigates whether long-term disability in people with multiple sclerosis can be predicted by a retinal spectral-domain optical coherence tomography assessment at baseline; another describes the impact of coronavirus disease 2019 (COVID-19) among patients with myasthenia gravis. A featured Views & Reviews article examines neurologic immune-related adverse events of immune checkpoint inhibitors and their clinical …

Association of Spectral-Domain OCT With Long-term Disability Worsening in Multiple Sclerosis

To evaluate whether a retinal spectral-domain optical coherence tomography (SD-OCT) assessment at baseline is associated with long-term disability worsening in people with multiple sclerosis (PwMS), we performed SD-OCT and Expanded Disability Status Scale (EDSS) assessments among 132 PwMS at baseline and at a median of 10 years later. In this prospective, longitudinal study, participants underwent SD-OCT, EDSS, and visual acuity (VA) assessments at baseline and at follow-up. Statistical analyses were performed using generalized linear regression models, adjusted for age, sex, race, multiple sclerosis (MS) subtype, and baseline disability. We defined clinically meaningful EDSS worsening as an increase of ≥2.0 if baseline EDSS score was <6.0 or an increase of ≥1.0 if baseline EDSS score was ≥6.0. A total of 132 PwMS (mean age 43 years; 106 patients with relapsing-remitting MS) were included in analyses. Median duration of follow-up was 10.4 years. In multivariable models excluding eyes with prior optic neuritis, relative to patients with an average baseline ganglion cell + inner plexiform layer (GCIPL) thickness ≥70 µm (the mean GCIPL thickness of all eyes at baseline), an average baseline GCIPL thickness <70 µm was associated with a 4-fold increased odds of meaningful EDSS worsening (adjusted odds ratio [OR] 3.97, 95% confidence interval [CI] 1.24-12.70; p = 0.02) and an almost 3-fold increased odds of low-contrast VA worsening (adjusted OR 2.93, 95% CI 1.40-6.13; p = 0.04). Lower baseline GCIPL thickness on SD-OCT is independently associated with long-term disability worsening in MS. Accordingly, SD-OCT at a single time point may help guide therapeutic decision-making among individual PwMS. This study provides Class I evidence that lower baseline GCIPL thickness on SD-OCT is independently associated with long-term disability worsening in MS.

The effect of contact lens wear on retinal spectral domain optical coherence tomography

BackgroundThis study assessed the impact of contact lens wear on retinal spectral domain optical coherence tomography (SD‐OCT) image quality and macular thickness measurements, among subjects with myopia.MethodsThis was a prospective study including 34 subjects (26.59 ± 3.19-years) with myopia or myopic astigmatism. Twelve were imaged wearing spherical soft contact lenses, eight non‐contact lens wearers were imaged with a plano soft contact lens, and 14 with significant astigmatism were fitted with a rigid gas permeable (RGP) contact lens. For each group of contact lens types, the average image quality index (Q‐index), and the average macular thickness measurements were compared between macular OCT scans obtained from the same eyes with and without a contact lens.ResultsAmong the subjects assessed with their habitual spherical soft lenses, the average Q‐index was similar for scans acquired with and without a contact lens (30.10 ± 1.94 versus 31.03 ± 2.55; p = 0.18). Among non‐contact lens wearers, the average Q‐index was slightly higher for scans acquired without a contact lens, compared to scans with a plano contact lens (31.99 ± 2.06 versus 29.51 ± 1.56; p = 0.006). Among 14 subjects imaged wearing a fitted RGP contact lens, the Q‐index was similar for scans acquired with and without a contact lens (29.04 ± 2.73 versus 28.75 ± 2.86; p = 0.78). In all groups, there were no correlations between the power of the sphere and change in the Q‐index (that is, post‐ minus pre‐contact lens Q‐index), and no differences were found between OCT‐derived macular thickness measurements from scans with and without a contact lens. The magnitude of cylinder was not correlated with the change in the Q‐index in the habitual and RGP contact lens groups. However, an inverse correlation between cylinder power and change in the Q‐index was found in the plano contact lens group.ConclusionIn low to intermediate levels of myopia, with or without regular astigmatism, macular SD‐OCT imaging does not merit placement of a soft or rigid contact lens, nor is there an added benefit from removing a habitual spherical soft lens prior to scanning.

Numerical method for axial motion artifact correction in retinal spectral-domain optical coherence tomography.

A numerical method that compensates image distortions caused by random fluctuations of the distance to an object in spectral-domain optical coherence tomography (SD OCT) has been proposed and verified experimentally. The proposed method is based on the analysis of the phase shifts between adjacent scans that are caused by micrometer-scale displacements and the subsequent compensation for the displacements through phase-frequency correction in the spectral space. The efficiency of the method is demonstrated in model experiments with harmonic and random movements of a scattering object as well as during in vivo imaging of the retina of the human eye.

An approach to automated retinal layer segmentation in SDOCT images

The optical coherence tomography (OCT) imaging technique is a precise and well-known approach to the diagnosis of retinal layers. The pathological changes in the retina challenge the accuracy of computational segmentation approaches in the evaluation and identification of defects in the boundary layer. The layer segmentations and boundary detections are distorted by noise in the computation. In this work, we propose a fully automated segmentation algorithm using a denoising technique called the Boisterous Obscure Ratio (BOR) for human and mammal retina. First, the BOR is derived using noise detection, i.e., from the Robust Outlyingness Ratio (ROR). It is then applied to edge and layer detection using a gradient-based deformable contour model. Second, the image is vectorised. In this method, a cluster and column intensity grid is applied to identify and determine the unsegmented layers. Using the layer intensity and a region growth seed point algorithm, segmentation of the prominent layers is achieved. The automatic BOR method is an image segmentation process that determines the eight layers in retinal spectral domain optical coherence tomography images. The highlight of the BOR method is that the results produced are accurate, highly substantial, and effective, although time consuming.

A novel benchmark model for intelligent annotation of spectral-domain optical coherence tomography scans using the example of cyst annotation

Background and objectivesThe lack of benchmark data in computational ophthalmology contributes to the challenging task of applying disease assessment and evaluate performance of machine learning based methods on retinal spectral domain optical coherence tomography (SD-OCT) scans. Presented here is a general framework for constructing a benchmark dataset for retinal image processing tasks such as cyst, vessel, and subretinal fluid segmentation and as a result, a benchmark dataset for cyst segmentation has been developed. MethodFirst, a dataset captured by different SD-OCT vendors with different numbers of scans and pathology qualities are selected. Then a robust and intelligent method is used to evaluate performance of readers, partitioning the dataset into subsets. Subsets are then assigned to complementary readers for annotation with respect to a novel confidence based annotation protocol. Finally, reader annotations are combined based on their performance to generate final annotations. ResultThe generated benchmark dataset for cyst segmentation comprises 26 SD-OCT scans with differing cyst qualities, collected from 4 different SD-OCT vendors to cover a wide variety of data. The dataset is partitioned into three subsets which are annotated by complementary readers based on a confidence based annotation protocol. Experimental results show annotations of complementary readers are combined efficiently with respect to their performance, generating accurate annotations. ConclusionOur results facilitate the process of generating benchmark datasets. Moreover the generated benchmark data set for cyst segmentation can be used reliably to train and test machine learning based methods.

Nonvascular retinal imaging markers of preclinical Alzheimer's disease

IntroductionIn patients with Alzheimer's disease (AD) and mild cognitive impairment, structural changes in the retina (i.e., reduced thicknesses of the ganglion cell and retinal nerve fiber layers and inclusion bodies that appear to contain beta-amyloid protein [Ab]) have been previously reported. We sought to explore whether anatomic retinal changes are detectable in the preclinical stage of AD. MethodsA cross-sectional study (as part of an ongoing longitudinal cohort study) involving 63 cognitively normal adults, all of whom have a parent with AD and subjective memory complaints. We compared neocortical amyloid aggregation (florbetapir PET imaging) to retinal spectral domain optical coherence tomography (SD-OCT) markers of possible disease burden. Retinal biomarkers, including the number and surface area of retinal inclusion bodies and the thickness of retinal neuronal layers, were compared across groups with high vs. low neocortical beta-amyloid load. ResultsThe surface area of inclusion bodies increased as a function of cortical amyloid burden. Additionally, there was a trend toward a selective volume increase in the inner plexiform layer (IPL; a layer rich in cholinergic activity) of the retina in Aβ+ relative to Aβ− participants, and IPL volume was correlated with the surface area of retinal inclusion bodies. DiscussionThese initial results suggest that retinal imaging may be a potential cost-effective and noninvasive technique that can be used to identify those at-risk for AD. Layer-specific changes in the IPL and their association with surface area of inclusion bodies are discussed as a possible reflection of early inflammatory processes associated with cholinergic disruption and concurrent Ab accumulation in the neocortex.

Multivendor Spectral-Domain Optical Coherence Tomography Dataset, Observer Annotation Performance Evaluation, and Standardized Evaluation Framework for Intraretinal Cystoid Fluid Segmentation.

Development of image analysis and machine learning methods for segmentation of clinically significant pathology in retinal spectral-domain optical coherence tomography (SD-OCT), used in disease detection and prediction, is limited due to the availability of expertly annotated reference data. Retinal segmentation methods use datasets that either are not publicly available, come from only one device, or use different evaluation methodologies making them difficult to compare. Thus we present and evaluate a multiple expert annotated reference dataset for the problem of intraretinal cystoid fluid (IRF) segmentation, a key indicator in exudative macular disease. In addition, a standardized framework for segmentation accuracy evaluation, applicable to other pathological structures, is presented. Integral to this work is the dataset used which must be fit for purpose for IRF segmentation algorithm training and testing. We describe here a multivendor dataset comprised of 30 scans. Each OCT scan for system training has been annotated by multiple graders using a proprietary system. Evaluation of the intergrader annotations shows a good correlation, thus making the reproducibly annotated scans suitable for the training and validation of image processing and machine learning based segmentation methods. The dataset will be made publicly available in the form of a segmentation Grand Challenge.

Optical Coherence Tomography to Assess Neurodegeneration in Multiple Sclerosis.

Retinal spectral domain optical coherence tomography (OCT) has emerged as a clinical and research tool in multiple sclerosis (MS) and optic neuritis (ON). This chapter summarizes a short OCT protocol as included in international consensus guidelines. The protocol was written for hands-on style such that both clinicians and OCT technicians can make use of it. The protocol is suitable for imaging of the optic nerve head and macular regions as a baseline for follow-up investigations, individual layer segmentation, and diagnostic assessment.

Fast Acquisition and Reconstruction of Optical Coherence Tomography Images via Sparse Representation

In this paper, we present a novel technique, based on compressive sensing principles, for reconstruction and enhancement of multi-dimensional image data. Our method is a major improvement and generalization of the multi-scale sparsity based tomographic denoising (MSBTD) algorithm we recently introduced for reducing speckle noise. Our new technique exhibits several advantages over MSBTD, including its capability to simultaneously reduce noise and interpolate missing data. Unlike MSBTD, our new method does not require an a priori high-quality image from the target imaging subject and thus offers the potential to shorten clinical imaging sessions. This novel image restoration method, which we termed sparsity based simultaneous denoising and interpolation (SBSDI), utilizes sparse representation dictionaries constructed from previously collected datasets. We tested the SBSDI algorithm on retinal spectral domain optical coherence tomography images captured in the clinic. Experiments showed that the SBSDI algorithm qualitatively and quantitatively outperforms other state-of-the-art methods.

Retinal Spectral Domain Optical Coherence Tomography in Early Atrophic Age-Related Macular Degeneration (AMD) and a New Metric for Objective Evaluation of the Efficacy of Ocular Nutrition

Purpose: A challenge in ocular preventive medicine is identification of patients with early pathological retinal damage that might benefit from nutritional intervention. The purpose of this study is to evaluate retinal thinning (RT) in early atrophic age-related macular degeneration (AMD) against visual function data from the Zeaxanthin and Visual Function (ZVF) randomized double masked placebo controlled clinical trial (FDA IND #78973). Methods: Retrospective, observational case series of medical center veterans with minimal visible AMD retinopathy (AREDS Report #18 simplified grading 1.4/4.0 bilateral retinopathy). Foveal and extra-foveal four quadrant SDOCT RT measurements were evaluated in n = 54 clinical and ZVF AMD patients. RT by age was determined and compared to the OptoVue SD OCT normative database. RT by quadrant in a subset of n = 29 ZVF patients was correlated with contrast sensitivity and parafoveal blue cone increment thresholds. Results: Foveal RT in AMD patients and non-AMD patients was preserved with age. Extrafoveal regions, however, showed significant slope differences between AMD patients and non-AMD patients, with the superior and nasal quadrants most vulnerable to retinal thinning (sup quad: −5.5 μm/decade thinning vs. Non-AMD: −1.1 μm/decade, P < 0.02; nasal quad: −5.0 μm/decade thinning vs. Non-AMD: −1.0 μm/decade, P < 0.04). Two measures of extrafoveal visual deterioration were correlated: A significant inverse correlation between % RT and contrast sensitivity (r = −0.33, P = 0.01, 2 Tailed Paired T) and an elevated extrafoveal increment blue cone threshold (r = +0.34, P = 0.01, 2 Tailed T). Additional SD OCT RT data for the non-AMD oldest age group (ages 82–91) is needed to fully substantiate the model. Conclusion: A simple new SD OCT clinical metric called “% extra-foveal RT” correlates well with functional visual loss in early AMD patients having minimal visible retinopathy. This metric can be used to follow the effect of repleting ocular nutrients, such as zinc, antioxidants, carotenoids, n-3 essential fats , resveratrol and vitamin D.

Validated Automatic Segmentation of AMD Pathology Including Drusen and Geographic Atrophy in SD-OCT Images

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.).