Intraretinal Layer Segmentation Research Articles

A Novel Intraretinal Layer Semantic Segmentation Method of Fundus OCT Images Based on the TransUNet Network Model

Optical coherence tomography (OCT) is used to obtain retinal images and stratify them to obtain the thickness of each intraretinal layer, which plays an important role in the clinical diagnosis of many ophthalmic diseases. In order to overcome the difficulties of layer segmentation caused by uneven distribution of retinal pixels, fuzzy boundaries, unclear texture, and irregular lesion structure, a novel lightweight TransUNet deep network model was proposed for automatic semantic segmentation of intraretinal layers in OCT images. First, ResLinear-Transformer was introduced into TransUNet to replace Transformer in TransUNet, which can enhance the receptive field and improve the local segmentation effect. Second, Dense Block was used as the decoder of TransUNet, which can strengthen feature reuse through dense connections, reduce feature parameter learning, and improve network computing efficiency. Finally, the proposed method was compared with the state-of-the-art on the public SD-OCT dataset of diabetic macular edema (DME) patients released by Duke University and POne dataset. The proposed method not only improves the overall semantic segmentation accuracy of retinal layer segmentation, but also reduces the amount of computation, achieves better effect on the intraretinal layer segmentation, and can better assist ophthalmologists in clinical diagnosis of patients.

Intraretinal Layer Segmentation Using Cascaded Compressed U-Nets.

Reliable biomarkers quantifying neurodegeneration and neuroinflammation in central nervous system disorders such as Multiple Sclerosis, Alzheimer’s dementia or Parkinson’s disease are an unmet clinical need. Intraretinal layer thicknesses on macular optical coherence tomography (OCT) images are promising noninvasive biomarkers querying neuroretinal structures with near cellular resolution. However, changes are typically subtle, while tissue gradients can be weak, making intraretinal segmentation a challenging task. A robust and efficient method that requires no or minimal manual correction is an unmet need to foster reliable and reproducible research as well as clinical application. Here, we propose and validate a cascaded two-stage network for intraretinal layer segmentation, with both networks being compressed versions of U-Net (CCU-INSEG). The first network is responsible for retinal tissue segmentation from OCT B-scans. The second network segments eight intraretinal layers with high fidelity. At the post-processing stage, we introduce Laplacian-based outlier detection with layer surface hole filling by adaptive non-linear interpolation. Additionally, we propose a weighted version of focal loss to minimize the foreground–background pixel imbalance in the training data. We train our method using 17,458 B-scans from patients with autoimmune optic neuropathies, i.e., multiple sclerosis, and healthy controls. Voxel-wise comparison against manual segmentation produces a mean absolute error of 2.3 μm, outperforming current state-of-the-art methods on the same data set. Voxel-wise comparison against external glaucoma data leads to a mean absolute error of 2.6 μm when using the same gold standard segmentation approach, and 3.7 μm mean absolute error in an externally segmented data set. In scans from patients with severe optic atrophy, 3.5% of B-scan segmentation results were rejected by an experienced grader, whereas this was the case in 41.4% of B-scans segmented with a graph-based reference method. The validation results suggest that the proposed method can robustly segment macular scans from eyes with even severe neuroretinal changes.

Modular deep neural networks for automatic quality control of retinal optical coherence tomography scans

Retinal optical coherence tomography (OCT) with intraretinal layer segmentation is increasingly used not only in ophthalmology but also for neurological diseases such as multiple sclerosis (MS). Signal quality influences segmentation results, and high-quality OCT images are needed for accurate segmentation and quantification of subtle intraretinal layer changes. Among others, OCT image quality depends on the ability to focus, patient compliance and operator skills. Current criteria for OCT quality define acceptable image quality, but depend on manual rating by experienced graders and are time consuming and subjective. In this paper, we propose and validate a standardized, grader-independent, real-time feedback system for automatic quality assessment of retinal OCT images. We defined image quality criteria for scan centering, signal quality and image completeness based on published quality criteria and typical artifacts identified by experienced graders when inspecting OCT images. We then trained modular neural networks on OCT data with manual quality grading to analyze image quality features. Quality analysis by a combination of these trained networks generates a comprehensive quality report containing quantitative results. We validated the approach against quality assessment according to the OSCAR-IB criteria by an experienced grader. Here, 100 OCT files with volume, circular and radial scans, centered on optic nerve head and macula, were analyzed and classified. A specificity of 0.96, a sensitivity of 0.97 and an accuracy of 0.97 as well as a Matthews correlation coefficient of 0.93 indicate a high rate of correct classification. Our method shows promising results in comparison to manual OCT grading and may be useful for real-time image quality analysis or analysis of large data sets, supporting standardized application of image quality criteria.

Regional analysis of segmented-macular structure in patients with myopic anisometropia.

To compare intraretinal layers between the eyes in patients with myopic anisometropia, and evaluate the relationship between the layers with spherical equivalent (SE) and axial length (AL). In this retrospective study, the more myopic (MM) and fellow (F) eyes of 41 patients with myopic anisometropia, and 38 emmetropic (± 0.50 diopter) control (C) eyes were inclueded. Intraretinal layer segmentation was performed by optical coherence tomography. Global volumes of retinal layers and their thicknesses in nine macular regions were compared. Correlation analysis was used to determine the relationship with SE and AL in each layer. Total retinal, ganglion cell (GCL), inner nuclear (INL), and outer plexiform (OPL) layer volumes in MM eyes were less than in C eyes, while INL and OPL were less than in F eyes. There was no difference in the fovea, except for the retinal pigment epithelium. In MM eyes, only INL and OPL were thin in at least one perifoveal and parafoveal quadrant compared to F eyes. Only INL and OPL thicknesses were significantly correlated with both SE and AL in all perifoveal quadrants. In contrast to the thinning found in MM eyes, the only layer in which thickening was detected to compare to C eye was nerve fiber layer (NFL), which correlated positively with SE and negatively with AL. While the fovea is less affected by myopia, thinning becomes remarkable in the perifoveal quadrants. Despite thinning in many layers, especially INL and OPL, NFL thickening may be seen due to myopia.

Relationship Between Retinal Layer Thickness and Disability Worsening in Relapsing-Remitting and Progressive Multiple Sclerosis.

Data regarding the predictive value of optical coherence tomography (OCT)-derived measures are lacking, especially in progressive multiple sclerosis (PMS). Accordingly, we aimed at investigating whether a single OCT assessment can predict a disability risk in both relapsing-remitting MS (RRMS) and PMS. One hundred one patients with RRMS and 79 patients with PMS underwent Spectral-Domain OCT, including intraretinal layer segmentation. All patients had at least 1 Expanded Disability Status Scale (EDSS) measurement during the subsequent follow-up (FU). Differences in terms of OCT metrics and their association with FU disability were assessed by analysis of covariance and linear regression models, respectively. The median FU was 2 years (range 1-5.5 years). The baseline peripapillary retinal nerve fiber layer (pRNFL) and ganglion cell + inner plexiform layer (GCIPL) were thinner in PMS compared with RRMS (P = 0.02 and P = 0.003, respectively). In the RRMS population, multivariable models showed that the GCIPL significantly correlated with FU disability (0.04 increase in the EDSS for each 1-μm decrease in the baseline GCIPL, 95% confidence interval: 0.006-0.08; P = 0.02). The baseline GCIPL was thinner in patients with RRMS with FU-EDSS >4 compared with those with FU-EDSS ≤4, and individuals in the highest baseline GCIPL tertile had a significantly lower FU-EDSS score than those in the middle and lowest tertile (P = 0.01 and P = 0.001, respectively). These findings were not confirmed in analyses restricted to patients with PMS. Among OCT-derived metrics, GCIPL thickness had the strongest association with short-medium term disability in patients with RRMS. The predictive value of OCT metrics in the longer term will have to be further investigated, especially in PMS.

The Effect of Software Versions on the Measurement of Retinal Vascular Densities Using Optical Coherence Tomography Angiography

ABSTRACT Background The goal of the study was to determine the effect of different software versions on the measurement of retinal vessel densities using optical coherence tomography angiography (OCTA) in normal subjects. Methods Thirty-two eyes of eighteen healthy subjects were imaged using two OCTA devices: the Optovue RTVue and the Zeiss Cirrus. The macular 3 × 3 mm scan protocol was used. The images acquired using the Optovue OCTA device were exported using two different software versions in the system and compared to the images acquired through the Zeiss OCTA. In addition, the Optovue OCTA images were exported after manual adjustment of the segmentation boundaries according to the intraretinal layer definition. The densities of the superficial vascular plexus (SVP) and deep vascular plexus (DVP) were measured using fractal analysis by box-counting (Dbox). Results Both the vessel densities of the SVP and DVP acquired using the Optovue OCTA device were significantly different when compared to those from the Zeiss OCTA device (all, P <.05). No significant difference was found between the vessel densities of the SVP exported using both the new and old versions of Optovue (P >.05). However, the DVP exported using the new Optovue software version was significantly different compared to those exported using the old version (P <.05). The vessel densities of the SVP and DVP were related among the Optovue OCTA software versions and manual adjustment method (r ranged from 0.55 to 0.77; all P <.05). Conclusion This is the first study to determine that different software versions with various intraretinal layer segmentation methods affect the vessel density measurements of the SVP and DVP.

Diagnostic Ability of Individual Macular Layers by Spectral-Domain OCT in Different Stages of Glaucoma

To compare the diagnostic ability of macular intraretinal layer thickness with circumpapillary retinal nerve fiber layer (cpRNFL) thickness, either when used individually or in combination with cpRNFL for detecting early, moderate, and advanced glaucoma. Cross-sectional study. A total of 423 glaucoma participants and 423 age- and gender-matched normal participants. Participants underwent Cirrus spectral-domain OCT (SD-OCT) imaging (Carl Zeiss Meditec, Dublin, CA) using the optic disc and macular scanning protocols. Iowa Reference Algorithms (version 3.8.0) were used for intraretinal layer segmentation, and mean thickness of intraretinal layers was rescaled with magnification correction using axial length value. Thickness measurements of each layer/sector and their corresponding areas under the receiver operating characteristic curve (AUCs) were obtained. Glaucoma eyes were subdivided based on of their visual field severity (early, n= 234; moderate, n= 107; advanced, n= 82). Intraretinal layers. Some 67% of participants were male, their average ± standard deviation age was 65±9 years. Circumpapillary retinal nerve fiber layer, macular ganglion cell layer (mGCL), and macular inner plexiform layer (mIPL) were significantly thinner in the glaucoma groups (P < 0.0005). The 2 best parameters for detecting normal eyes from early glaucoma was cpRNFL (AUC= 0.861) and mGCL (AUC= 0.842), from moderate glaucoma was mGCL combined with inner plexiform layer (IPL) (AUC= 0.915) and cpRNFL (AUC= 0 .914), and from advanced glaucoma was mGCL-IPL (AUC= 0.984) and cpRNFL (AUC= 0.977). There was no statistical significance between AUCs for the macular parameter and cpRNFL thickness measurement at any of the severities (P > 0.05). Combining macular and cpRNFL parameters improved the diagnostic performance for early glaucoma (AUC= 0.908; P= 0.002) and moderate glaucoma (AUC= 0.944; P= 0.031) but not for advanced glaucoma (AUC= 0.991; P > 0.05). Single-layer mGCL thickness is comparable to the traditional cpRNFL thickness for the diagnosis of early/moderate glaucoma, whereas cpRNFL thickness remains the most efficient for advanced glaucoma. Combining macular measurements (GCL and GCL-IPL) and cpRNFL improved the discrimination of early/moderate glaucoma but not of advanced glaucoma. For the diagnosis of early glaucoma, both macular and optic disc scans should be used.

Age-related changes of individual macular retinal layers among Asians

We characterized the age-related changes of the intra-retinal layers measured with spectral-domain optical coherence tomography (SD-OCT; Cirrus high-definition OCT [Carl Zeiss Meditec]. The Singapore Epidemiology of Eye Diseases is a population-based, cross-sectional study of Chinese, Malays and Indians living in Singapore. Iowa Reference Algorithms (Iowa Institute for Biomedical Imaging) were used for intra-retinal layer segmentation and mean thickness of 10 intra-retinal layers rescaled with magnification correction using axial length value. Linear regression models were performed to investigate the association of retinal layers with risk factors. After excluding participants with history of diabetes or ocular diseases, high-quality macular SD-OCT images were available for 2,047 participants (44–89 years old). Most of the retinal layers decreased with age except for foveal retinal nerve fiber layer (RNFL) and the inner/outer segments of photoreceptors where they increased with age. Men generally had thicker retinal layers than women. Chinese have the thickest RNFL and retinal pigment epithelium amongst the ethnic groups. Axial length and refractive error remained correlated with retinal layers in spite of magnification correction. Our data show pronounced age-related changes in retinal morphology. Age, gender, ethnicity and axial length need be considered when establishing OCT imaging biomarkers for ocular or systemic disease.

Normative Data and Minimally Detectable Change for Inner Retinal Layer Thicknesses Using a Semi-automated OCT Image Segmentation Pipeline.

Neurodegenerative and neuroinflammatory diseases regularly cause optic nerve and retinal damage. Evaluating retinal changes using optical coherence tomography (OCT) in diseases like multiple sclerosis has thus become increasingly relevant. However, intraretinal segmentation, a necessary step for interpreting retinal changes in the context of these diseases, is not standardized and often requires manual correction. Here we present a semi-automatic intraretinal layer segmentation pipeline and establish normative values for retinal layer thicknesses at the macula, including dependencies on age, sex, and refractive error. Spectral domain OCT macular 3D volume scans were obtained from healthy participants using a Heidelberg Engineering Spectralis OCT. A semi-automated segmentation tool (SAMIRIX) based on an interchangeable third-party segmentation algorithm was developed and employed for segmentation, correction, and thickness computation of intraretinal layers. Normative data is reported from a 6 mm Early Treatment Diabetic Retinopathy Study (ETDRS) circle around the fovea. An interactive toolbox for the normative database allows surveying for additional normative data. We cross-sectionally evaluated data from 218 healthy volunteers (144 females/74 males, age 36.5 ± 12.3 years, range 18–69 years). Average macular thickness (MT) was 313.70 ± 12.02 μm, macular retinal nerve fiber layer thickness (mRNFL) 39.53 ± 3.57 μm, ganglion cell and inner plexiform layer thickness (GCIPL) 70.81 ± 4.87 μm, and inner nuclear layer thickness (INL) 35.93 ± 2.34 μm. All retinal layer thicknesses decreased with age. MT and GCIPL were associated with sex, with males showing higher thicknesses. Layer thicknesses were also positively associated with each other. Repeated-measurement reliability for the manual correction of automatic intraretinal segmentation results was excellent, with an intra-class correlation coefficient >0.99 for all layers. The SAMIRIX toolbox can simplify intraretinal segmentation in research applications, and the normative data application may serve as an expandable reference for studies, in which normative data cannot be otherwise obtained.

Can Optical Coherence Tomography Be Used to Guide Treatment Decisions in Adult or Pediatric Multiple Sclerosis?

With the recognition that neurodegeneration represents the principal substrate of disability in multiple sclerosis (MS), there has been increased strives towards identifying biomarkers for accurately quantifying and tracking neurodegeneration during the disease course. The retina provides an opportune "window" into the central nervous system (CNS) in MS, with retinal changes in MS reflecting not only local, but also global aspects of neurodegeneration and inflammation operative in the disease. Optical coherence tomography (OCT) is a rapid, inexpensive, reproducible, high-resolution imaging technique allowing accurate quantification of discrete retinal layers. OCT determined thinning of inner retinal layers such as the retinal nerve fiber layer (RNFL) and in particular the composite of the ganglion cell and inner plexiform (GCIP) layers, predominantly related to optic neuropathy, have been shown to not only correlate with high and low contrast visual function in MS, but also global MS disability scores, as well as whole brain and particularly gray matter volumes. Rates of GCIP thinning have been shown to be accelerated among MS patients exhibiting inflammatory activity outside of the visual pathways, as well as disability progression during follow-up. Moreover, baseline RNFL thickness in MS has been shown to have utility for predicting future disability accumulation. On the other hand, thickening of the inner nuclear layer (INL) in MS, the pathophysiologic basis of which remains to be elucidated, has been found to predict the development of clinical and radiological inflammatory activity, as well as subsequent disability progression in MS. Given the potential for OCT to provide insight into neurodegeneration and inflammation occurring in MS, this review focuses on the potential utility of OCT within the clinical setting to influence treatment decisions for MS patients. The evolution of spectral domain-OCT technology, with improved resolution and reproducibility allowing intra-retinal layer segmentation, has facilitated the determination that the OCT derived measure GCIP thickness is a highly accurate measure for quantifying and tracking neurodegeneration, and conversely neuroprotection, in MS. The strong relationships between rates of GCIP and brain atrophy across MS subtypes over time underpin the insight derived regarding the global MS disease process from OCT and highlight OCT as an excellent complementary tool to magnetic resonance imaging (MRI) for tracking MS patients. More recently, longitudinal studies are emerging which support the utility of OCT for monitoring the differential effects of disease-modifying therapies (DMTs) in MS. Although further work is required, there is mounting evidence supporting the utility of OCT in the clinical setting to monitor disease course in individual patients with MS and to aid in the prediction of disease course. As pharmacological treatment options in MS expand to also include potentially neuroprotective and/or remyelinating or neurorestorative drugs, OCT as a biomarker of neurodegeneration and neuroprotection (and neuroinflammation to a lesser degree) may become an invaluable tool in both the research and clinical settings.

Optical coherence tomography image denoising using Gaussianization transform.

We demonstrate the power of the Gaussianization transform (GT) for modeling image content by applying GT for optical coherence tomography (OCT) denoising. The proposed method is a developed version of the spatially constrained Gaussian mixture model (SC-GMM) method, which assumes that each cluster of similar patches in an image has a Gaussian distribution. SC-GMM tries to find some clusters of similar patches in the image using a spatially constrained patch clustering and then denoise each cluster by the Wiener filter. Although in this method GMM distribution is assumed for the noisy image, holding this assumption on a dataset is not investigated. We illustrate that making a Gaussian assumption on a noisy dataset has a significant effect on denoising results. For this purpose, a suitable distribution for OCT images is first obtained and then GT is employed to map this original distribution of OCT images to a GMM distribution. Then, this Gaussianized image is used as the input of the SC-GMM algorithm. This method, which is a combination of GT and SC-GMM, remarkably improves the results of OCT denoising compared with earlier version of SC-GMM and even produces better visual and numerical results than the state-of-the art works in this field. Indeed, the main advantage of the proposed OCT despeckling method is texture preservation, which is important for main image processing tasks like OCT inter- and intraretinal layer analysis. Thus, to prove the efficacy of the proposed method for this analysis, an improvement in the segmentation of intraretinal layers using the proposed method as a preprocessing step is investigated. Furthermore, the proposed method can achieve the best expert ranking between other contending methods, and the results show the helpfulness and usefulness of the proposed method in clinical applications.

Automated Retinal Layer Segmentation Using Spectral Domain Optical Coherence Tomography: Evaluation of Inter-Session Repeatability and Agreement between Devices.

Retinal and intra-retinal layer thicknesses are routinely generated from optical coherence tomography (OCT) images, but on-board software capabilities and image scaling assumptions are not consistent across devices. This study evaluates the device-independent Iowa Reference Algorithms (Iowa Institute for Biomedical Imaging) for automated intra-retinal layer segmentation and image scaling for three OCT systems. Healthy participants (n = 25) underwent macular volume scans using a Cirrus HD-OCT (Zeiss), 3D-OCT 1000 (Topcon), and a non-commercial long-wavelength (1040nm) OCT on two occasions. Mean thickness of 10 intra-retinal layers was measured in three ETDRS subfields (fovea, inner ring and outer ring) using the Iowa Reference Algorithms. Where available, total retinal thicknesses were measured using on-board software. Measured axial eye length (AEL)-dependent scaling was used throughout, with a comparison made to the system-specific fixed-AEL scaling. Inter-session repeatability and agreement between OCT systems and segmentation methods was assessed. Inter-session coefficient of repeatability (CoR) for the foveal subfield total retinal thickness was 3.43μm, 4.76μm, and 5.98μm for the Zeiss, Topcon, and long-wavelength images respectively. For the commercial software, CoR was 4.63μm (Zeiss) and 7.63μm (Topcon). The Iowa Reference Algorithms demonstrated higher repeatability than the on-board software and, in addition, reliably segmented all 10 intra-retinal layers. With fixed-AEL scaling, the algorithm produced significantly different thickness values for the three OCT devices (P<0.05), with these discrepancies generally characterized by an overall offset (bias) and correlations with axial eye length for the foveal subfield and outer ring (P<0.05). This correlation was reduced to an insignificant level in all cases when AEL-dependent scaling was used. Overall, the Iowa Reference Algorithms are viable for clinical and research use in healthy eyes imaged with these devices, however ocular biometry is required for accurate quantification of OCT images.

Automated layer segmentation of macular OCT images via graph-based SLIC superpixels and manifold ranking approach

Using the graph-based a simple linear iterative clustering (SLIC) superpixels and manifold ranking technology, a novel automated intra-retinal layer segmentation method is proposed in this paper. Eleven boundaries of ten retinal layers in optical coherence tomography (OCT) images are exactly, fast and reliably quantified. Instead of considering the intensity or gradient features of the single-pixel in most existing segmentation methods, the proposed method focuses on the superpixels and the connected components-based image cues. The image is represented as some weighted graphs with superpixels or connected components as nodes. Each node is ranked with the gradient and spatial distance cues via graph-based Dijkstra’s method or manifold ranking. So that it can effectively overcome speckle noise, organic texture and blood vessel artifacts issues. Segmentation is carried out in a three-stage scheme to extract eleven boundaries efficiently. The segmentation algorithm is validated on 2D and 3D OCT images in three databases, and is compared with the manual tracings of two independent observers. It demonstrates promising results in term of the mean unsigned boundaries errors, the mean signed boundaries errors, and layers thickness errors.

Quantification of retinal layer thickness changes in acute macular neuroretinopathy

PurposeTo quantitatively evaluate retinal layer thickness changes in acute macular neuroretinopathy (AMN).MethodsAMN areas were identified using near-infrared reflectance (NIR) images. Intraretinal layer segmentation using Heidelberg software was performed. The inbuilt...

Statistical Modeling of Retinal Optical Coherence Tomography.

In this paper, a new model for retinal Optical Coherence Tomography (OCT) images is proposed. This statistical model is based on introducing a nonlinear Gaussianization transform to convert the probability distribution function (pdf) of each OCT intra-retinal layer to a Gaussian distribution. The retina is a layered structure and in OCT each of these layers has a specific pdf which is corrupted by speckle noise, therefore a mixture model for statistical modeling of OCT images is proposed. A Normal-Laplace distribution, which is a convolution of a Laplace pdf and Gaussian noise, is proposed as the distribution of each component of this model. The reason for choosing Laplace pdf is the monotonically decaying behavior of OCT intensities in each layer for healthy cases. After fitting a mixture model to the data, each component is gaussianized and all of them are combined by Averaged Maximum A Posterior (AMAP) method. To demonstrate the ability of this method, a new contrast enhancement method based on this statistical model is proposed and tested on thirteen healthy 3D OCTs taken by the Topcon 3D OCT and five 3D OCTs from Age-related Macular Degeneration (AMD) patients, taken by Zeiss Cirrus HD-OCT. Comparing the results with two contending techniques, the prominence of the proposed method is demonstrated both visually and numerically. Furthermore, to prove the efficacy of the proposed method for a more direct and specific purpose, an improvement in the segmentation of intra-retinal layers using the proposed contrast enhancement method as a preprocessing step, is demonstrated.

Homonymous hemimacular thinning in retrochiasmal lesions

PurposeTo evaluate the thickness of macular retina and determine which retinal layers are most affected by retrochiasmal visual pathways lesions.MethodsMean retinal thickness utilizing automated intraretinal layer segmentation of spectral domain optical coherence tomography scans was performed in 40 eyes of 40 patients with retrochiasmal visual pathways lesions and compared with 60 eyes of control subjects. Multiple linear regression analysis was used to determine the relationship between retinal thickness and follow‐up, age and gender.ResultsGanglion cell and inner plexiform layer was thinner in the temporal hemiretina ipsilateral and in the nasal hemiretina contralateral than in healthy controls.The mean thickness was significantly reduced in lesions over 6 months of duration, with no differences in correlations with age or gender.ConclusionsThis study demonstrate ganglion cell and inner plexiform thinning in the hemimacular area. These results support the concept that transneuronal retrograde degeneration of the retinal ganglion cells can be detected by OCT in humans with retrochiasmal visual pathways lesions.This homonymous hemimacular thinning in OCT represents an imaging biomarker that can be of valu in diagnosis, prognosis and clinical trials of neuroprotectives therapies.

Reliability of Intra-Retinal Layer Thickness Estimates.

PurposeMeasurement of intra-retinal layer thickness using optical coherence tomography (OCT) has become increasingly prominent in multiple sclerosis (MS) research. Nevertheless, the approaches used for determining the mean layer thicknesses vary greatly. Insufficient data exist on the reliability of different thickness estimates, which is crucial for their application in clinical studies. This study addresses this lack by evaluating the repeatability of different thickness estimates.MethodsStudies that used intra-retinal layer segmentation of macular OCT scans in patients with MS were retrieved from PubMed. To investigate the repeatability of previously applied layer estimation approaches, we generated datasets of repeating measurements of 15 healthy subjects and 13 multiple sclerosis patients using two OCT devices (Cirrus HD-OCT and Spectralis SD-OCT). We calculated each thickness estimate in each repeated session and analyzed repeatability using intra-class correlation coefficients and coefficients of repeatability.ResultsWe identified 27 articles, eleven of them used the Spectralis SD-OCT, nine Cirrus HD-OCT, two studies used both devices and two studies applied RTVue-100. Topcon OCT-1000, Stratus OCT and a research device were used in one study each. In the studies that used the Spectralis, ten different thickness estimates were identified, while thickness estimates of the Cirrus OCT were based on two different scan settings. In the simulation dataset, thickness estimates averaging larger areas showed an excellent repeatability for all retinal layers except the outer plexiform layer (OPL).ConclusionsGiven the good reliability, the thickness estimate of the 6mm-diameter area around the fovea should be favored when OCT is used in clinical research. Assessment of the OPL was weak in general and needs further investigation before OPL thickness can be used as a reliable parameter.

A novel automated segmentation method for retinal layers in OCT images proves retinal degeneration after optic neuritis

AimThe evaluation of inner retinal layer thickness can serve as a direct biomarker for monitoring the course of inflammatory diseases of the central nervous system such as multiple sclerosis (MS)....

Retinal pathology in Susac syndrome detected by spectral-domain optical coherence tomography.

The aim of this non-interventional study was to characterize retinal layer pathology in Susac syndrome (SuS), a disease with presumably autoimmune-mediated microvessel occlusions in the retina, brain, and inner ear, in comparison to the most important differential diagnosis multiple sclerosis (MS). Seventeen patients with SuS and 17 age- and sex-matched patients with relapsing-remitting MS (RRMS) and healthy controls (HC) were prospectively investigated by spectral-domain optical coherence tomography (OCT) including intraretinal layer segmentation in a multicenter study. Patients with SuS additionally received retinal fluorescein angiography (FA) and automated perimetry. Patchy thinning of the retinal nerve fiber layer, ganglion cell layer, inner plexiform layer, inner nuclear layer, and outer plexiform layer compared to corresponding sectors in RRMS and HC eyes (p < 0.003 for SuS vs RRMS and HC) was observed in 23/34 (68%) SuS eyes, particularly in temporal quadrants. The outer nuclear layer (ONL) and photoreceptor layers (PRL) were not affected. FA performed in 15/17 patients with SuS was negative for disease-specific branch retinal artery occlusions in all but 1 eye at the time of OCT examination and revealed no additional vascular abnormalities, even in severely damaged OCT areas. In a subset of patients with SuS, associations of visual field data with distinct retinal layers were observed. Distinct OCT patterns of scattered, scar-like intraretinal pathology in SuS eyes, sparing the ONL and PRL, suggest a retinal, but not choroidal, vascular pathomechanism and clearly differentiate SuS from RRMS. Depending on the disease stage, OCT and FA provide specific complementary diagnostic information in SuS.

Thickness mapping of eleven retinal layers segmented using the diffusion maps method in normal eyes.

This study was conducted to determine the thickness map of eleven retinal layers in normal subjects by spectral domain optical coherence tomography (SD-OCT) and evaluate their association with sex and age. Mean regional retinal thickness of 11 retinal layers was obtained by automatic three-dimensional diffusion map based method in 112 normal eyes of 76 Iranian subjects. We applied our previously reported 3D intraretinal fast layer segmentation which does not require edge-based image information but rather relies on regional image texture. The thickness maps are compared among 9 macular sectors within 3 concentric circles as defined by ETDRS. The thickness map of central foveal area in layers 1, 3, and 4 displayed the minimum thickness. Maximum thickness was observed in nasal to the fovea of layer 1 and in a circular pattern in the parafoveal retinal area of layers 2, 3, and 4 and in central foveal area of layer 6. Temporal and inferior quadrants of the total retinal thickness and most of other quadrants of layer 1 were significantly greater in the men than in the women. Surrounding eight sectors of total retinal thickness and a limited number of sectors in layers 1 and 4 significantly correlated with age.