Reliability and reproducibility of macular segmentation using a custom-built optical coherence tomography retinal image analysis software

The assessment of acute chorioretinal changes due to intensive physical exercise in young adults

Purpose

There is abundant evidence on the benefits of physical activity on cardiovascular health. However, there are only few data on the acute effects of physical exercise on the retina and choroid. Our aim was the in vivo examination of chorioretinal alterations following short intense physical activity by spectral domain optical coherence tomography (SD-OCT).

Methods

Twenty-one eyes of 21 healthy, young subjects (mean age 22.5 ± 4.1 years, 15 males and 6 females) were recruited. Macular scanning with a SD-OCT was performed before and following a vita maxima-type physical strain exercise on a rowing ergometer until complete fatigue. Follow-up OCT scans were performed 1, 5, 15, 30 and 60 minutes following the exercise. The OCT images were exported and analyzed using our custom-built OCTRIMA 3D software and the thickness of 7 retinal layers was calculated, along with semi-automated measurement of the choroidal thickness. One-way ANOVA analysis was performed followed by Dunnett post hoc test for the thickness change compared to baseline and the correlation between performance and thickness change has also been calculated. The level of significance was set at 0.001.

Results

We observed a significant thinning of the total retina 1 minute post-exercise (-7.3 ± 0.6 μm, p < 0.001) which was followed by a significant thickening by 5 and 15 minutes (+3.6 ± 0.6 μm and +4.0 ± 0.6 μm, respectively, both p <0.001). Post-exercise retinal thickness returned to baseline by 30 minutes. This trend was present throughout the most layers of the retina, with significant changes in the ganglion cell–inner plexiform layer complex, (-1.3 ± 0.1 μm, +0.6 ± 0.1 μm and +0.7 ± 0.1 μm, respectively, p <0.001 for all), in the inner nuclear layer at 1 and 5 minutes (-0.8 ± 0.1 μm and +0.8 ± 0.1 μm, respectively, p <0.001 for both), in the outer nuclear layer–photoreceptor inner segment complex at 5 minute (+2.3 ± 0.4 μm, p <0.001 for all) and in the interdigitation zone–retinal pigment epithelium complex at 1 and 15 minutes (-3.3 ± 0.4 μm and +1.8 ± 0.4 μm, respectively, p <0.001 for both). There was no significant change in choroidal thickness; however, we could detect a tendency towards thinning at 1, 15, and 30 minutes following exercise. The observed changes in thickness change did not correlate with performance. Similar trends were observed in both professional and amateur sportsmen (n = 15 and n = 6, respectively). The absolute changes in choroidal thickness did not show any correlation with the thickness changes of the intraretinal layers.

Conclusions

Our study implies that in young adults, intense physical exercise has an acute effect on the granular layers of the retina, resulting in thinning followed by rebound thickening before normalization. We could not identify any clear correlation with either choroidal changes or performance that might explain our observations, and hence the exact mechanism warrants further clarification. We believe that a combination of vascular and mechanic changes is behind the observed trends.

Assessing the validity of a cross-platform retinal image segmentation tool in normal and diseased retina

Comparing automated retinal layer segmentation using proprietary software (Heidelberg Spectralis HRA + OCT) and cross-platform Optical Coherence Tomography (OCT) segmentation software (Orion). Image segmentations of normal and diseased (iAMD, DME) eyes were performed using both softwares and then compared to the 'gold standard' of manual segmentation. A qualitative assessment and quantitative (layer volume) comparison of segmentations were performed. Segmented images from the two softwares were graded by two masked graders and in cases with difference, a senior retina specialist made a final independent decisive grading. Cross-platform software was significantly better than the proprietary software in the segmentation of NFL and INL layers in Normal eyes. It generated significantly better segmentation only for NFL in iAMD and for INL and OPL layers in DME eyes. In normal eyes, all retinal layer volumes calculated by the two softwares were moderate-strongly correlated except OUTLY. In iAMD eyes, GCIPL, INL, ONL, INLY, TRV layer volumes were moderate-strongly correlated between softwares. In eyes with DME, all layer volume values were moderate-strongly correlated between softwares. Cross-platform software can be used reliably in research settings to study the retinal layers as it compares well against manual segmentation and the commonly used proprietary software for both normal and diseased eyes.

PRE-RETINOPATHY OF TYPE 1 DIABETES IN THE CONTEXT OF FUNCTIONAL, STRUCTURAL AND MICROCIRCULATORY CHANGES IN THE MACULAR AREA

AIM

The authors assessed the development of intraocular changes in type 1 diabetes (T1DM) from the onset of the disease leading to diabetic retinopathy (DR). The quote: &#8220;There must be an intermediate stage between the physiological intraocular finding and the diabetic retinopathy itself &#8220;, (prof. Jan Vav&#345;inec).

METHODS

A two-year study (2018 and 2019) was conducted at the Department of Ophthalmology of the Teaching Hospital Kralovske Vinohrady in Prague (Czech Republic). There were 54 patients aged 17-42 years, the detection of T1DM ranged between the 1st and 14th year of life, with a duration of 12-35 years. Individual patients were always examined simultaneously by three methods: CS (contrast sensitivity), SD-OCT (spectral domain optical coherence tomography) and OCT-A (optical coherence tomography-angiography). We examined 106 eyes once and in a comprehensive manner.

RESULTS

We have shown that there is an intermediate stage between the physiological finding on the retina and DR, so-called diabetic pre-retinopathy (DpR). Subsequent redistribution of the observed into two DpR subgroups was derived from the size of the FAZ, either with its smaller area or with a larger area determining the microvascularity of the central area of the retina. The results of both other methods were assigned to these values. For SD-OCT, the depth of the fovea (the difference between the central retinal thickness and the total average retinal thickness) was determined, which was affected by the increased the macular cubature. In all patients it was on average 10.3 &#956;m3. The retina in the central area was significantly strengthened compared to the healthy population at the level of significance p 0,001. We divided the actual DpR into an image: DpR1 in 26.5 % of eyes - condition with an average shallower fovea only by 21.5 &#956;m below the level of the surrounding retina and an average narrower FAZ: 0.165 mm2 and with a more significant decrease in CS; DpR2 in 40.5 % of eyes - condition with average deeper fovea by 42 &#956;m, i.e., more significantly and average larger FAZ: 0.325 mm2 with lower decrease of CS. At the same time, other changes in microvascularity were noted, such as disorders in the sense of non-perfusion in the central part of the retina of various degrees. This finding differed significantly from changes in already established (non-proliferative) NPDR in 36 % of eyes, when a significant decrease in CS with normal visual acuity was found 4/4 ETDRS. Statistical differences in CS between DpR1 and DpR2 and NPDR were determined - always p 0.001. The average depth of the fovea was NPDR: 29.5 &#956;m. NPDR had the largest average FAZ: 0.56 mm2. Also significant were the most significant changes in non-perfusion and especially the presence of microaneurysms.

CONCLUSIONS

These three non - invasive methods helped to monitor the dynamics of the development of ocular changes in T1DM of better quality than the determination of visual acuity and ophthalmoscopic examination. Increased retinal volume induced hypoxia of visual cells with subsequent dual autoregulatory mechanism conditioning two types of diabetic pre-retinopathy before the onset of DR.

Macular imaging with optical coherence tomography in glaucoma

With the advent of spectral-domain optical coherence tomography, imaging of the posterior segment of the eye can be carried out rapidly at multiple anatomical locations, including the optic nerve head, circumpapillary retinal nerve fiber layer, and macula. There is now ample evidence to support the role of spectral-domain optical coherence tomography imaging of the macula for detection of early glaucoma. Macular spectral-domain optical coherence tomography measurements demonstrate high reproducibility, and evidence on its utility for detection of glaucoma progression is accumulating. We present a comprehensive review of macular spectral-domain optical coherence tomography imaging emerging as an essential diagnostic tool in glaucoma.

Diagnostic Capability of Three-Dimensional Macular Parameters for Glaucoma Using Optical Coherence Tomography Volume Scans

Purpose

To compare the diagnostic capability of three-dimensional (3D) macular parameters against traditional two-dimensional (2D) retinal nerve fiber layer (RNFL) thickness using spectral domain optical coherence tomography. To determine if manual correction and interpolation of B-scans improve the ability of 3D macular parameters to diagnose glaucoma.

Methods

A total of 101 open angle glaucoma patients (29 with early glaucoma) and 57 healthy subjects had peripapillary 2D RNFL thickness and 3D macular volume scans. Four parameters were calculated for six different-sized annuli: total macular thickness (M-thickness), total macular volume (M-volume), ganglion cell complex (GCC) thickness, and GCC volume of the innermost 3 macular layers (retinal nerve fiber layer + ganglion cell layer + inner plexiform layer). All macular parameters were calculated with and without correction and interpolation of frames with artifacts. The areas under the receiver operating characteristic curves (AUROC) were calculated for all the parameters.

Results

The 3D macular parameter with the best diagnostic performance was GCC-volume-34, with an inner diameter of 3 mm and an outer of 4 mm. The AUROC for RNFL thickness and GCC-volume-34 were statistically similar for all regions (global: RNFL thickness 0.956, GCC-volume-34 0.939, P value = 0.3827), except for the temporal GCC-volume-34, which was significantly better than temporal RNFL thickness (P value = 0.0067). Correction of artifacts did not significantly change the AUROC of macular parameters (P values between 0.8452 and 1.0000).

Conclusions

The diagnostic performance of best macular parameters (GCC-volume-34 and GCC-thickness-34) were similar to or better than 2D RNFL thickness. Manual correction of artifacts with data interpolation is unnecessary in the clinical setting.

Automatic detection of retinal regions using fully convolutional networks for diagnosis of abnormal maculae in optical coherence tomography images

In conventional retinal region detection methods for optical coherence tomography (OCT) images, many parameters need to be set manually, which is often detrimental to their generalizability. We present a scheme to detect retinal regions based on fully convolutional networks (FCN) for automatic diagnosis of abnormal maculae in OCT images. The FCN model is trained on 900 labeled age-related macular degeneration (AMD), diabetic macular edema (DME) and normal (NOR) OCT images. Its segmentation accuracy is validated and its effectiveness in recognizing abnormal maculae in OCT images is tested and compared with traditional methods, by using the spatial pyramid matching based on sparse coding (ScSPM) classifier and Inception V3 classifier on two datasets: Duke dataset and our clinic dataset. In our clinic dataset, we randomly selected half of the B-scans of each class (300 AMD, 300 DME, and 300 NOR) for training classifier and the rest (300 AMD, 300 DME, and 300 NOR) for testing with 10 repetitions. Average accuracy, sensitivity, and specificity of 98.69%, 98.03%, and 99.01% are obtained by using ScSPM classifier, and those of 99.69%, 99.53%, and 99.77% are obtained by using Inception V3 classifier. These two classification algorithms achieve 100% classification accuracy when directly applied to Duke dataset, where all the 45 OCT volumes are used as test set. Finally, FCN model with or without flattening and cropping and its influence on classification performance are discussed.

Retinal structural-vascular-functional relationship using optical coherence tomography and optical coherence tomography - angiography in myopia

Background

To examine the retinal structure–vascular-function relationship using optical coherence tomography (OCT) and optical coherence tomography angiography (OCTA) in myopia.

Methods

This was a prospective cross-sectional study comprising 86 eyes of 45 individuals with varying axial lengths and spherical equivalents and no posterior segment abnormalities. All eyes underwent optical coherence tomography with the Spectralis SD-OCT and OCTA with RTVue-XR Avanti; Optovue. Individual macular retinal layer thicknesses and flow areas and vessel densities were measured on OCT and OCTA, respectively. Linear correlations were made between the macular layer thicknesses, flow areas and vessel densities with axial length, spherical equivalent and visual acuity.

Results

The participants’ mean ages were 33.34 ± 14.45 years, mean spherical equivalent refractions were − 7.17 ± 5.71 D and axial lengths were 25.95 ± 2.41 mm. There were significant positive correlations of foveal angle (r = 0.757, p = 0.001), inner retinal (r = 0.764, p = 0.001) and outer plexiform layer (r = 0.771, p = 0.001) thickness on OCT and vessel densities in deep capillary plexus (r = 0.313, p = 0.003) on OCTA with axial length and negative correlations with spherical equivalents and visual acuity. Significant negative correlations of outer nuclear layer (r = − 0.560, p = 0.03) and photoreceptor outer segment layer thickness (r = − 0.856, p < 0.001) were noted on OCT with axial length and positive correlations with spherical equivalents and visual acuity.

Conclusion

The lateral retinal stretching in myopia could possibly explain the correlation between retinal layer thickness, vascular density and visual acuity in these eyes. Further research is required to investigate this.

Spectral-Domain OCT Measurements in Alzheimer's Disease: A Systematic Review and Meta-analysis

TOPIC

OCT is a noninvasive tool to measure specific retinal layers in the eye. The relationship of retinal spectral-domain (SD) OCT measurements with Alzheimer's disease (AD) and mild cognitive impairment (MCI) remains unclear. Hence, we conducted a systematic review and meta-analysis to examine the SD OCT measurements in AD and MCI.

CLINICAL RELEVANCE

Current methods of diagnosing early AD are expensive and invasive. Retinal measurements of SD OCT, which are noninvasive, technically simple, and inexpensive, are potential biomarkers of AD.

METHODS

We conducted a literature search in PubMed and Excerpta Medica Database to identify studies published before December 31, 2017, that assessed the associations between AD, MCI, and measurements of SD OCT: ganglion cell-inner plexiform layer (GC-IPL), ganglion cell complex (GCC), macular volume, and choroidal thickness, in addition to retinal nerve fiber layer (RNFL) and macular thickness. We used a random-effects model to examine these relationships. We also conducted meta-regression and assessed heterogeneity, publication bias, and study quality.

RESULTS

We identified 30 eligible studies, involving 1257 AD patients, 305 MCI patients, and 1460 controls, all of which were cross-sectional studies. In terms of the macular structure, AD patients showed significant differences in GC-IPL thickness (standardized mean difference [SMD], -0.46; 95% confidence interval [CI], -0.80 to -0.11; I² = 71%), GCC thickness (SMD, -0.84; 95% CI, -1.10 to -0.57; I² = 0%), macular volume (SMD, -0.58; 95% CI, -1.03 to -0.14; I² = 80%), and macular thickness of all inner and outer sectors (SMD range, -0.52 to -0.74; all P < 0.001) when compared with controls. Peripapillary RNFL thickness (SMD, -0.67; 95% CI, -0.95 to -0.38; I² = 89%) and choroidal thickness (SMD range, -0.88 to -1.03; all P < 0.001) also were thinner in AD patients.

CONCLUSIONS

Our results confirmed the associations between retinal measurements of SD OCT and AD, highlighting the potential usefulness of SD OCT measurements as biomarkers of AD.

Effects of Measurement Center Shift on Ganglion Cell-inner Plexiform Layer Thickness Measurements

SIGNIFICANCE

Our authors studied the effects of measurement center shift on ganglion cell-inner plexiform layer (GCIPL) thickness measurements in Cirrus spectral-domain optical coherence tomography (SD-OCT). The measurement center shift affects the GCIPL thickness measurement depending on the distance of shift.

PURPOSE

The purpose of this study was to explore changes in macular GCIPL thicknesses measurements after manual shifting of the measurement center using SD-OCT.

METHODS

A prospective study was conducted. A total of 30 normal eyes of 30 subjects were included in the study. An experienced examiner obtained two consecutive measurements of GCIPL thickness using SD-OCT. Coefficients of repeatability were calculated for the average, minimum, and sectoral (superotemporal, superior, superonasal, inferonasal, inferior, and inferotemporal) thicknesses. Next, the measurement center was manually shifted from the foveal center. Three measurement centers were horizontally placed at 59-μm intervals from the foveal center, and two further centers were placed 176 μm apart. Also, three measurement centers were vertically placed at 47-μm intervals from the foveal center, and two further centers were placed 142 μm apart. The thickness of GCIPL was measured again at each shift point, and the changes of thickness before and after movement were analyzed.

RESULTS

When the measurement centers were shifted to positions 59 μm horizontally or 47 μm vertically from the fovea, no significant changes in GCIPL thicknesses were evident. However, upon more pronounced shifting, the average GCIPL thickness of the direction of the shift region was significantly lower than baseline, whereas the GCIPL of the diametrically opposite sector was thicker than baseline.

CONCLUSIONS

The impact of changes associated with shifting of the measurement center should be taken into consideration when measuring GCIPL thickness in patients with retinal diseases, glaucoma, or neuro-ophthalmological conditions.

Automatic diagnosis of abnormal macula in retinal optical coherence tomography images using wavelet-based convolutional neural network features and random forests classifier

The present research intends to propose a fully automatic algorithm for the classification of three-dimensional (3-D) optical coherence tomography (OCT) scans of patients suffering from abnormal macula from normal candidates. The method proposed does not require any denoising, segmentation, retinal alignment processes to assess the intraretinal layers, as well as abnormalities or lesion structures. To classify abnormal cases from the control group, a two-stage scheme was utilized, which consists of automatic subsystems for adaptive feature learning and diagnostic scoring. In the first stage, a wavelet-based convolutional neural network (CNN) model was introduced and exploited to generate B-scan representative CNN codes in the spatial-frequency domain, and the cumulative features of 3-D volumes were extracted. In the second stage, the presence of abnormalities in 3-D OCTs was scored over the extracted features. Two different retinal SD-OCT datasets are used for evaluation of the algorithm based on the unbiased fivefold cross-validation (CV) approach. The first set constitutes 3-D OCT images of 30 normal subjects and 30 diabetic macular edema (DME) patients captured from the Topcon device. The second publicly available set consists of 45 subjects with a distribution of 15 patients in age-related macular degeneration, DME, and normal classes from the Heidelberg device. With the application of the algorithm on overall OCT volumes and 10 repetitions of the fivefold CV, the proposed scheme obtained an average precision of 99.33% on dataset1 as a two-class classification problem and 98.67% on dataset2 as a three-class classification task.

Using Retinal Imaging to Study Dementia

The retina offers a unique "window" to study pathophysiological processes of dementia in the brain, as it is an extension of the central nervous system (CNS) and shares prominent similarities with the brain in terms of embryological origin, anatomical features and physiological properties.  The vascular and neuronal structure in the retina can now be visualized easily and non-invasively using retinal imaging techniques, including fundus photography and optical coherence tomography (OCT), and quantified semi-automatically using computer-assisted analysis programs. Studying the associations between vascular and neuronal changes in the retina and dementia could improve our understanding of dementia and, potentially, aid in diagnosis and risk assessment.  This protocol aims to describe a method of quantifying and analyzing retinal vasculature and neuronal structure, which are potentially associated with dementia. This protocol also provides examples of retinal changes in subjects with dementia, and discusses technical issues and current limitations of retinal imaging.

Association of Structural and Functional Measures With Contrast Sensitivity in Glaucoma

PURPOSE

To test the hypothesis that structural and functional measures predict contrast sensitivity (CS) outcomes in glaucomatous eyes.

DESIGN

Cross-sectional prospective study.

METHODS

One hundred five eyes of 65 patients who underwent macular spectral-domain optical coherence tomography imaging, 24-2 standard achromatic visual fields (VF), and CS measurement on the same day were enrolled. Association of CS at 4 spatial frequencies (3, 6, 12, and 18 cycles per degree, cpd) with structural and functional outcomes was explored with correlation and regression analyses.

RESULTS

The median (IQR) 24-2 visual field mean deviation was -7.6 (-11.1 to -3.0). Significant correlations were found between CS at 6 cpd and ganglion cell/inner plexiform layer thickness at inferotemporal and inferonasal macular sectors (ρ = 0.222, P = .023 and ρ = 0.209, P = .032, respectively). CS at 6 cpd demonstrated higher correlations with full macular thickness measurements, the strongest of which was with the central macular thickness in the superior 6 × 3-degree region (ρ = 0.311, P = .001). Contrast sensitivity at 6 cpd also had the strongest correlation with mean deviation of the 4 central VF points (ρ = -0.420; P < .001). There was a significant correlation between logMAR visual acuity and contrast sensitivity at 6, 12, and 18 cpd (ρ = -0.306, ρ = -0.348 and ρ = -0.241, P < .013, respectively).

CONCLUSIONS

Structural and functional measures showed a fair relationship with contrast sensitivity. This association was most prominent between full-thickness macular measures or central VF parameters and CS at 6 cpd. Contrast sensitivity was not a reliable surrogate for glaucoma severity in this cross-sectional study.

Joint Segmentation of Retinal Layers and Focal Lesions in 3-D OCT Data of Topologically Disrupted Retinas

Accurate quantification of retinal structures in 3-D optical coherence tomography data of eyes with pathologies provides clinically relevant information. We present an approach to jointly segment retinal layers and lesions in eyes with topology-disrupting retinal diseases by a loosely coupled level set framework. In the new approach, lesions are modeled as an additional space-variant layer delineated by auxiliary interfaces. Furthermore, the segmentation of interfaces is steered by local differences in the signal between adjacent retinal layers, thereby allowing the approach to handle local intensity variations. The accuracy of the proposed method of both layer and lesion segmentation has been evaluated on eyes affected by central serous retinopathy and age-related macular degeneration. In addition, layer segmentation of the proposed approach was evaluated on eyes without topology-disrupting retinal diseases. Good agreement between the segmentation performed manually by a medical doctor and results obtained from the automatic segmentation was found for all data types. The mean unsigned error for all interfaces varied between 2.3 and 11.9 μm (0.6-3.1 pixels). Furthermore, lesion segmentation showed a Dice coefficient of 0.68 for drusen and 0.89 for fluid pockets. Overall, the method provides a flexible and accurate solution to jointly segment lesions and retinal layers.

Fully automated macular pathology detection in retina optical coherence tomography images using sparse coding and dictionary learning

We propose a framework for automated detection of dry age-related macular degeneration (AMD) and diabetic macular edema (DME) from retina optical coherence tomography (OCT) images, based on sparse coding and dictionary learning. The study aims to improve the classification performance of state-of-the-art methods. First, our method presents a general approach to automatically align and crop retina regions; then it obtains global representations of images by using sparse coding and a spatial pyramid; finally, a multiclass linear support vector machine classifier is employed for classification. We apply two datasets for validating our algorithm: Duke spectral domain OCT (SD-OCT) dataset, consisting of volumetric scans acquired from 45 subjects—15 normal subjects, 15 AMD patients, and 15 DME patients; and clinical SD-OCT dataset, consisting of 678 OCT retina scans acquired from clinics in Beijing—168, 297, and 213 OCT images for AMD, DME, and normal retinas, respectively. For the former dataset, our classifier correctly identifies 100%, 100%, and 93.33% of the volumes with DME, AMD, and normal subjects, respectively, and thus performs much better than the conventional method; for the latter dataset, our classifier leads to a correct classification rate of 99.67%, 99.67%, and 100.00% for DME, AMD, and normal images, respectively.

The measurement repeatability using different partition methods of intraretinal tomographic thickness maps in healthy human subjects

Purpose

To determine the repeatability and profiles with different partition methods in intraretinal thickness layers in healthy human subjects, using optical coherence tomography (OCT).

Methods

A custom-built ultrahigh-resolution OCT was used to acquire three-dimensional volume of the macula in 20 healthy subjects. The dataset was acquired twice using the macular cube 512×128 protocol in an area of 6×6 mm² centered on the fovea. Commercially available segmentation software (Orion™) was used to segment the dataset into thickness maps of six intraretinal layers. The coefficient of repeatability and intraclass coefficient of correlation (ICC) were analyzed using hemispheric zoning and sectors defined by the Early Treatment Diabetic Retinopathy Study (ETDRS).

Results

All datasets were successfully segmented to create six thickness maps of individual intraretinal layers. Coefficients of repeatabilities of these layers in hemispheric zones ranged from 0.9 to 6.6 µm, with an average of 3.6 µm (standard deviation [SD] 1.4), which was not significantly different compared to ETDRS sectors (P>0.05). ICCs of these layers in hemispheric zones ranged from 0.68 to 0.99, with an average of 0.91 (SD 0.07). There were no significant differences in ICCs between two zoning methods (P>0.05). Significant variations of tomographic intraretinal thicknesses were found between the inner and outer annuli and among the quadrantal sectors within the inner and outer annuli (P<0.05). Significant variations of the quadrantal sectors including both inner and outer annuli were evident in intraretinal layers (P<0.05) except for the outer plexiform layer.

Conclusion

The measurement repeatabilities of tomographic thicknesses of intraretinal layers are comparable using both hemispheric and ETDRS partitions in volumetric data combined with the commercially available segmentation software. In keeping with known, normal anatomical variation, significant differences in tomographic thickness in various intraretinal layers were apparent in both hemispheric and ETDRS sectors.

Variability in Spectral-Domain Optical Coherence Tomography over 4 Weeks by Age

PURPOSE

To quantify variation in spectral-domain optical coherence tomography (SD-OCT) measures of total retinal thickness (top of inner limiting membrane to top of retinal pigment epithelium, RPE) and RPE thickness measures over a 4-week period and by age.

METHODS

A total of 76 volunteers aged 40-85 years were seen at three visits over 4 weeks. Two Topcon SD-OCT scans were taken at each visit. Following grid re-centration, total retinal and RPE thickness were determined in nine subfields. Multilevel modeling was used to quantify variance between scans and by age.

RESULTS

In the central circle, mean total retinal thickness was 237.9 µm (standard deviation, SD, 23.5 µm) and RPE thickness was 46.0 µm (SD 5.3 µm). Intraclass correlation coefficient in the central circle was 0.988 for total retinal thickness and 0.714 for RPE thickness. Pairwise measures taken within 4 weeks were strongly correlated (p > 0.95). Within-subject variation of total retinal thickness increased significantly with age. Subjects in the oldest age group had significantly increased among- and within-subject variability in measures of RPE thickness.

CONCLUSIONS

Correlation between retinal thickness measures was very high (>0.95) over a period of 4 weeks with small changes likely due to variation in measurement. Increasing variability in total retinal and RPE thickness measures with age suggest that the use of more and/or higher quality images to calculate mean thickness to reduce variability may benefit the study of these measures in older persons. This may also impact sample size calculations for future studies of SD-OCT measures in older adults.

Investigating Tissue Optical Properties and Texture Descriptors of the Retina in Patients with Multiple Sclerosis

Purpose

To assess the differences in texture descriptors and optical properties of retinal tissue layers in patients with multiple sclerosis (MS) and to evaluate their usefulness in the detection of neurodegenerative changes using optical coherence tomography (OCT) image segmentation.

Patients and Methods

38 patients with MS were examined using Stratus OCT. The raw macular OCT data were exported and processed using OCTRIMA software. The enrolled eyes were divided into two groups, based on the presence of optic neuritis (ON) in the history (MSON+ group, n = 36 and MSON- group, n = 31). Data of 29 eyes of 24 healthy subjects (H) were used as controls. A total of seven intraretinal layers were segmented and thickness as well as optical parameters such as contrast, fractal dimension, layer index and total reflectance were measured. Mixed-model ANOVA analysis was used for statistical comparisons.

Results

Significant thinning of the retinal nerve fiber layer (RNFL), ganglion cell/inner plexiform layer complex (GCL+IPL) and ganglion cell complex (GCC, RNFL+GCL+IPL) was observed between study groups in all comparisons. Significant difference was found in contrast in the RNFL, GCL+IPL, GCC, inner nuclear layer (INL) and outer plexiform layer when comparing MSON+ to the other groups. Higher fractal dimension values were observed in GCL+IPL and INL layers when comparing H vs. MSON+ groups. A significant difference was found in layer index in the RNFL, GCL+IPL and GCC layers in all comparisons. A significant difference was observed in total reflectance in the RNFL, GCL+IPL and GCC layers between the three examination groups.

Conclusion

Texture and optical properties of the retinal tissue undergo pronounced changes in MS even without optic neuritis. Our results may help to further improve the diagnostic efficacy of OCT in MS and neurodegeneration.

The Effect of Axial Length on the Thickness of Intraretinal Layers of the Macula

Purpose

The aim of this study was to evaluate the effect of axial length (AL) on the thickness of intraretinal layers in the macula using optical coherence tomography (OCT) image analysis.

Methods

Fifty three randomly selected eyes of 53 healthy subjects were recruited for this study. The median age of the participants was 29 years (range: 6 to 67 years). AL was measured for each eye using a Lenstar LS 900 device. OCT imaging of the macula was also performed by Stratus OCT. OCTRIMA software was used to process the raw OCT scans and to determine the weighted mean thickness of 6 intraretinal layers and the total retina. Partial correlation test was performed to assess the correlation between the AL and the thickness values.

Results

Total retinal thickness showed moderate negative correlation with AL (r = -0.378, p = 0.0007), while no correlation was observed between the thickness of the retinal nerve fiber layer (RNFL), ganglion cell layer (GCC), retinal pigment epithelium (RPE) and AL. Moderate negative correlation was observed also between the thickness of the ganglion cell layer and inner plexiform layer complex (GCL+IPL), inner nuclear layer (INL), outer plexiform layer (OPL), outer nuclear layer (ONL) and AL which were more pronounced in the peripheral ring (r = -0.402, p = 0.004; r = -0.429, p = 0.002; r = -0.360, p = 0.01; r = -0.448, p = 0.001).

Conclusions

Our results have shown that the thickness of the nuclear layers and the total retina is correlated with AL. The reason underlying this could be the lateral stretching capability of these layers; however, further research is warranted to prove this theory. Our results suggest that the effect of AL on retinal layers should be taken into account in future studies.

Automatic segmentation of the optic nerve head for deformation measurements in video rate optical coherence tomography

Optical coherence tomography (OCT) imaging has become a standard diagnostic tool in ophthalmology, providing essential information associated with various eye diseases. In order to investigate the dynamics of the ocular fundus, we present a simple and accurate automated algorithm to segment the inner limiting membrane in video-rate optic nerve head spectral domain (SD) OCT images. The method is based on morphological operations including a two-step contrast enhancement technique, proving to be very robust when dealing with low signal-to-noise ratio images and pathological eyes. An analysis algorithm was also developed to measure neuroretinal tissue deformation from the segmented retinal profiles. The performance of the algorithm is demonstrated, and deformation results are presented for healthy and glaucomatous eyes.

Evaluation of the retinal ganglion cell layer thickness in healthy Turkish children

PURPOSE

To identify the distribution, variation, and determinants of ganglion cell-inner plexiform layer (GC-IPL) thickness in healthy Turkish children measured by high-definition optical coherence tomography (HD-OCT).

PATIENTS AND METHODS

This institutional study involved 296 eyes from 296 healthy children aged between 3 and 17 years. Each child underwent a dilated eye examination, cycloplegic refraction, and axial length measurement using Nidek AL-Scan optical biometer. Macular scan was used to measure the GC-IPL thickness, and peripapillary retinal nerve fiber layer (RNFL) thickness was measured using the HD-OCT (Cirrus HD-OCT). Right eye of each subject was selected for analysis.

RESULTS

A total of 296 children (125 boys, 171 girls) were included in this study. The mean age of the children was 9.62±4.10 years (range, 3 to 17 y). The mean spherical equivalent was -0.09±1.49 D. The mean AL was 23.03±1.03 mm. The mean overall GC-IPL thickness was 83.44±5.52 μm and RNFL thickness was 96.91±10.21 μm. They were thicker than has been reported in adults. According to age-adjusted multiple regression analyses significant predictors of mean GC-IPL thickness were peripapillary RNFL thickness and AL (P<0.001).

CONCLUSIONS

This study ensures a pediatric normative database of GC-IPL using spectral-domain OCT. This information may provide to diagnosis and monitoring of optic nerve diseases and glaucoma in children.

Macular Thickness Profiles of Intraretinal Layers in Myopia Evaluated by Ultrahigh-Resolution Optical Coherence Tomography

PURPOSE

To investigate the thickness and variation profiles of 8 intraretinal layers in myopia.

DESIGN

Prospective cross-sectional study.

METHODS

Young subjects with spherical equivalents ranging from +0.50 to -10.25 diopters and good corrected vision were divided into emmetropic (n = 20), low myopic (n = 50), and high myopic (n = 30) groups. Retinal images centered on the fovea along the horizontal and vertical meridians were obtained by ultrahigh-resolution optical coherence tomography (OCT). Macular images were segmented into 8 intraretinal layers by an automatic segmentation algorithm to yield thickness profiles within a 6-mm-diameter circle divided into central, pericentral, and peripheral regions.

RESULTS

For intraretinal layers in the central region, the outer segment of receptors layer was thicker in the high myopic group and positively correlated with axial length. In the pericentral and peripheral regions, all layers except the ganglion cell and inner plexiform layer had thickness changes in high myopia. The total thickness of the peripheral region was less than in the emmetropic controls owing to thinner inner nuclear layer, combined Henle fiber and outer nuclear layer, and outer segment of receptors layer. Nevertheless, the thicknesses of the combined myoid and ellipsoid zone and the combined interdigitation zone and retinal pigment epithelium/Bruch complex in the peripheral region were greater than for the emmetropic controls.

CONCLUSIONS

Intraretinal layer thicknesses in young high myopic eyes varied significantly from emmetropic controls, especially in the peripheral region. Ultrahigh-resolution OCT with automated segmentation can detect changes in retinal macular microstructure during the development of myopia.

Ganglion cell complex thickness in nonexudative age-related macular degeneration

PURPOSE

TO evaluate ganglion cell complex (GCC) thickness with spectral domain optical coherence tomography (SD-OCT) in eyes with nonexudative age-related macular degeneration (NEAMD).

METHODS

Forty-seven eyes of 28 patients with nonexudative age-related macular degeneration (NEAMD) and 54 eyes of 28 age-matched healthy subjects were enrolled. Each subject underwent a complete ophthalmic examination before SD-OCT were obtained. Macular scans were taken with software version 6.0 of the ganglion cell analysis (GCA) algorithm. GCC thickness was evaluated automatically as the average, minimum, temporal superior, superior, nasal superior, nasal inferior, inferior, and temporal-inferior segments by SD-OCT and parameters were compared between groups.

RESULTS

The mean age was 68.7±8.73 years in patient group, and 61.51±5.66 years in control group. There were no significant differences in mean age, gender distribution, intraocular pressure, and sferic equivalent at imaging between the groups (P>0.05). The mean (±SD) GCC thicknesses were as follows; average 71.53±16.53 μm, minumum 62.36±21.51 μm, temporal superior 72.23±14.60 μm, superior 72.76±20.40 μm, nasal superior 72.31±20.13 μm, nasal inferior 69.74±20.51 μm, inferior 69.38±19.03 μm, and temporal-inferior 73.12±15.44 μm in patient group. Corresponding values in control group were 81.46±4.90 μm, 78.66±6.00 μm, 81.51±4.66 μm, 82.94±5.14 μm, 81.79±5.86 μm, 80.94±6.18 μm, 80.14±6.30 μm, and 81.75±5.26 μm, respectively. There were significant differences between two groups in each segments (Mann-Whitney U-test, P<0.05).

CONCLUSION

The average GCC thickness values (in all segments) of NEAMD patients were lower than control group. NEAMD, which is considered as a disease of outer layers of retina, may be accompanied with a decrease of ganglion cell thickness, so inner layers of retina may be affected.

Kernel regression based segmentation of optical coherence tomography images with diabetic macular edema

We present a fully automatic algorithm to identify fluid-filled regions and seven retinal layers on spectral domain optical coherence tomography images of eyes with diabetic macular edema (DME). To achieve this, we developed a kernel regression (KR)-based classification method to estimate fluid and retinal layer positions. We then used these classification estimates as a guide to more accurately segment the retinal layer boundaries using our previously described graph theory and dynamic programming (GTDP) framework. We validated our algorithm on 110 B-scans from ten patients with severe DME pathology, showing an overall mean Dice coefficient of 0.78 when comparing our KR + GTDP algorithm to an expert grader. This is comparable to the inter-observer Dice coefficient of 0.79. The entire data set is available online, including our automatic and manual segmentation results. To the best of our knowledge, this is the first validated, fully-automated, seven-layer and fluid segmentation method which has been applied to real-world images containing severe DME.

Perspectives of an innovative ophthalmological technology: optical coherence tomography (OCT)--what should be of interest to the neurologist?

Ocular coherence tomography has revolutionised the prospects of measuring the loss of retinal ganglion cells secondary to degenerative diseases and monitoring time-dependent changes of optic disc morphology, since the resolution has been improved considerably and the time required has been reduced. Although the non-invasive technique promises a high inter-session reproducibility, the limitations of retinal imaging and the problems of segmenting of the retinal layers have to be taken into account. While the first studies were limited to single sessions in small groups, further trials will elucidate how the retinal nerve fibre layer (RNFL) is altered in the course of different episodic forms of multiple sclerosis. This review points out that the examination technique already provides comprehensive information, valuable in the daily care of neurological patients.

Fully automated detection of diabetic macular edema and dry age-related macular degeneration from optical coherence tomography images

We present a novel fully automated algorithm for the detection of retinal diseases via optical coherence tomography (OCT) imaging. Our algorithm utilizes multiscale histograms of oriented gradient descriptors as feature vectors of a support vector machine based classifier. The spectral domain OCT data sets used for cross-validation consisted of volumetric scans acquired from 45 subjects: 15 normal subjects, 15 patients with dry age-related macular degeneration (AMD), and 15 patients with diabetic macular edema (DME). Our classifier correctly identified 100% of cases with AMD, 100% cases with DME, and 86.67% cases of normal subjects. This algorithm is a potentially impactful tool for the remote diagnosis of ophthalmic diseases.

Fractal-based analysis of optical coherence tomography data to quantify retinal tissue damage

BACKGROUND

The sensitivity of Optical Coherence Tomography (OCT) images to identify retinal tissue morphology characterized by early neural loss from normal healthy eyes is tested by calculating structural information and fractal dimension. OCT data from 74 healthy eyes and 43 eyes with type 1 diabetes mellitus with mild diabetic retinopathy (MDR) on biomicroscopy was analyzed using a custom-built algorithm (OCTRIMA) to measure locally the intraretinal layer thickness. A power spectrum method was used to calculate the fractal dimension in intraretinal regions of interest identified in the images. ANOVA followed by Newman-Keuls post-hoc analyses were used to test for differences between pathological and normal groups. A modified p value of <0.001 was considered statistically significant. Receiver operating characteristic (ROC) curves were constructed to describe the ability of each parameter to discriminate between eyes of pathological patients and normal healthy eyes.

RESULTS

Fractal dimension was higher for all the layers (except the GCL + IPL and INL) in MDR eyes compared to normal healthy eyes. When comparing MDR with normal healthy eyes, the highest AUROC values estimated for the fractal dimension were observed for GCL + IPL and INL. The maximum discrimination value for fractal dimension of 0.96 (standard error =0.025) for the GCL + IPL complex was obtained at a FD ≤ 1.66 (cut off point, asymptotic 95% Confidence Interval: lower-upper bound = 0.905-1.002). Moreover, the highest AUROC values estimated for the thickness measurements were observed for the OPL, GCL + IPL and OS. Particularly, when comparing MDR eyes with control healthy eyes, we found that the fractal dimension of the GCL + IPL complex was significantly better at diagnosing early DR, compared to the standard thickness measurement.

CONCLUSIONS

Our results suggest that the GCL + IPL complex, OPL and OS are more susceptible to initial damage when comparing MDR with control healthy eyes. Fractal analysis provided a better sensitivity, offering a potential diagnostic predictor for detecting early neurodegeneration in the retina.

Reproducibility and repeatability of ganglion cell-inner plexiform layer thickness measurements in healthy subjects

PURPOSE

To assess the reproducibility and repeatability of macular ganglion cell-inner plexiform layer (GC-IPL) thickness measurements in healthy subjects.

PROCEDURES

In this observational study, 60 healthy eyes were subjected to macular GC-IPL thickness measurements by means of Cirrus™ high-definition optical coherence tomography (Cirrus version 6.0; Carl Zeiss Meditec, Dublin, Calif., USA) by two examiners in two sessions. Average, minimum and 6 sectoral GC-IPL thicknesses were measured. Inter- and intraobserver reproducibility was tested and analyzed by means of the concordance correlation coefficient (CCC). The repeatability of measurements was assessed by the coefficient of repeatability (CR).

RESULTS

Mean age (±SD) was 29.63 (±5.1) years. The CRs for average GC-IPL thickness were 2.1 and 2.2 µm for the first and the second operator, respectively. Inter- and intraobserver CCCs ranged from 0.91 (95% CI: 0.89-0.93) to 0.98 (95% CI: 0.96-0.99) and from 0.92 (95% CI: 0.88-0.94) to 0.98 (95% CI: 0.97-0.99), respectively.

CONCLUSIONS

GC-IPL thickness measurements in young healthy subjects showed excellent reproducibility and repeatability, especially for average and sectoral GC-IPL thickness measurements.

Automated classifiers for early detection and diagnosis of retinopathy in diabetic eyes

BACKGROUND

Artificial neural networks (ANNs) have been used to classify eye diseases, such as diabetic retinopathy (DR) and glaucoma. DR is the leading cause of blindness in working-age adults in the developed world. The implementation of DR diagnostic routines could be feasibly improved by the integration of structural and optical property test measurements of the retinal structure that provide important and complementary information for reaching a diagnosis. In this study, we evaluate the capability of several structural and optical features (thickness, total reflectance and fractal dimension) of various intraretinal layers extracted from optical coherence tomography images to train a Bayesian ANN to discriminate between healthy and diabetic eyes with and with no mild retinopathy.

RESULTS

When exploring the probability as to whether the subject's eye was healthy (diagnostic condition, Test 1), we found that the structural and optical property features of the outer plexiform layer (OPL) and the complex formed by the ganglion cell and inner plexiform layers (GCL + IPL) provided the highest probability (positive predictive value (PPV) of 91% and 89%, respectively) for the proportion of patients with positive test results (healthy condition) who were correctly diagnosed (Test 1). The true negative, TP and PPV values remained stable despite the different sizes of training data sets (Test 2). The sensitivity, specificity and PPV were greater or close to 0.70 for the retinal nerve fiber layer's features, photoreceptor outer segments and retinal pigment epithelium when 23 diabetic eyes with mild retinopathy were mixed with 38 diabetic eyes with no retinopathy (Test 3).

CONCLUSIONS

A Bayesian ANN trained on structural and optical features from optical coherence tomography data can successfully discriminate between healthy and diabetic eyes with and with no retinopathy. The fractal dimension of the OPL and the GCL + IPL complex predicted by the Bayesian radial basis function network provides better diagnostic utility to classify diabetic eyes with mild retinopathy. Moreover, the thickness and fractal dimension parameters of the retinal nerve fiber layer, photoreceptor outer segments and retinal pigment epithelium show promise for the diagnostic classification between diabetic eyes with and with no mild retinopathy.

A morphological study of retinal changes in unilateral amblyopia using optical coherence tomography image segmentation

OBJECTIVE

The purpose of this study was to evaluate the possible structural changes of the macula in patients with unilateral amblyopia using optical coherence tomography (OCT) image segmentation.

PATIENTS AND METHODS

38 consecutive patients (16 male; mean age 32.4±17.6 years; range 6-67 years) with unilateral amblyopia were involved in this study. OCT examinations were performed with a time-domain OCT device, and a custom-built OCT image analysis software (OCTRIMA) was used for OCT image segmentation. The axial length (AL) was measured by a LenStar LS 900 device. Macular layer thickness, AL and manifest spherical equivalent refraction (MRSE) of the amblyopic eye were compared to that of the fellow eye. We studied if the type of amblyopia (strabismus without anisometropia, anisometropia without strabismus, strabismus with anisometropia) had any influence on macular layer thickness values.

RESULTS

There was significant difference between the amblyopic and fellow eyes in MRSE and AL in all subgroups. Comparing the amblyopic and fellow eyes, we found a statistically significant difference only in the thickness of the outer nuclear layer in the central region using linear mixed model analysis keeping AL and age under control (p = 0.032). There was no significant difference in interocular difference in the thickness of any macular layers between the subgroups with one-way between-groups ANCOVA while statistically controlling for interocular difference in AL and age.

CONCLUSIONS

According to our results there are subtle changes in amblyopic eyes affecting the outer nuclear layer of the fovea suggesting the possible involvement of the photoreceptors. However, further studies are warranted to support this hypothesis.

Repeatability and reproducibility of eight macular intra-retinal layer thicknesses determined by an automated segmentation algorithm using two SD-OCT instruments

Purpose

To evaluate the repeatability, reproducibility, and agreement of thickness profile measurements of eight intra-retinal layers determined by an automated algorithm applied to optical coherence tomography (OCT) images from two different instruments.

Methods

Twenty normal subjects (12 males, 8 females; 24 to 32 years old) were enrolled. Imaging was performed with a custom built ultra-high resolution OCT instrument (UHR-OCT, ∼3 µm resolution) and a commercial RTVue100 OCT (∼5 µm resolution) instrument. An automated algorithm was developed to segment the macular retina into eight layers and quantitate the thickness of each layer. The right eye of each subject was imaged two times by the first examiner using each instrument to assess intra-observer repeatability and once by the second examiner to assess inter-observer reproducibility. The intraclass correlation coefficient (ICC) and coefficients of repeatability and reproducibility (COR) were analyzed to evaluate the reliability.

Results

The ICCs for the intra-observer repeatability and inter-observer reproducibility of both SD-OCT instruments were greater than 0.945 for the total retina and all intra-retinal layers, except the photoreceptor inner segments, which ranged from 0.051 to 0.643, and the outer segments, which ranged from 0.709 to 0.959. The CORs were less than 6.73% for the total retina and all intra-retinal layers. The total retinal thickness measured by the UHR-OCT was significantly thinner than that measured by the RTVue100. However, the ICC for agreement of the thickness profiles between UHR-OCT and RTVue OCT were greater than 0.80 except for the inner segment and outer segment layers.

Conclusions

Thickness measurements of the intra-retinal layers determined by the automated algorithm are reliable when applied to images acquired by the UHR-OCT and RTVue100 instruments.

Automatic segmentation of up to ten layer boundaries in SD-OCT images of the mouse retina with and without missing layers due to pathology

Accurate quantification of retinal layer thicknesses in mice as seen on optical coherence tomography (OCT) is crucial for the study of numerous ocular and neurological diseases. However, manual segmentation is time-consuming and subjective. Previous attempts to automate this process were limited to high-quality scans from mice with no missing layers or visible pathology. This paper presents an automatic approach for segmenting retinal layers in spectral domain OCT images using sparsity based denoising, support vector machines, graph theory, and dynamic programming (S-GTDP). Results show that this method accurately segments all present retinal layer boundaries, which can range from seven to ten, in wild-type and rhodopsin knockout mice as compared to manual segmentation and has a more accurate performance as compared to the commercial automated Diver segmentation software.

Retinal thickness changes after phacoemulsification

Purpose

To determine the effect of phacoemulsification on macular volume and thickness using spectral domain optical coherence tomography examinations.

Methods

Twenty-seven eyes of 27 subjects who underwent phacoemulsification were studied. All nine areas of the macula were examined by spectral domain optical coherence tomography preoperatively and 2 months postoperatively. Effective phacoemulsification time and absolute phacoemulsification time were also recorded.

Results

There were statistically significant differences in macular thickness between preoperative and postoperative spectral domain optical coherence tomography examinations in nine areas including macular volume. In the paracentral macular area, the thickness of three quadrants significantly increased (superior P=0.015; temporal P=0.001; and nasal P=0.023). Peripheral macular thickness also increased significantly in the superior (P=0.05) and temporal macular areas (P<0.001). The macular volume increased significantly after phacoemulsification (P<0.001). There were no correlations between absolute/effective phacoemulsification time and macular cellular structures (P>0.05), but a significant correlation (P=0.011) was found between absolute phacoemulsification time and change in macular volume.

Conclusion

Macular thickness changes in the nasal, superior, and temporal quadrants of the paracentral area and the superior and temporal quadrants of the peripheral area, as well as macular volume, may be used as detailed biomarkers to measure the effects of intraocular pressure fluctuations and maneuvers in phacoemulsification intraocular surgeries.

[Diagnostic use of macular layer analysis by SD-OCT in primary open angle glaucoma]

PURPOSE

The purpose of this study is to evaluate the diagnostic ability of segmentation of the various internal macular layers by spectral domain optical coherence tomography (Cirrus HD-OCT, Carl Zeiss Meditec (CZM), Dublin, CA, USA) and to compare it to that of the peripapillary retinal nerve fiber layer (cpRNFL) in primary open angle glaucoma (POAG).

MATERIALS AND METHODS

This study included 252 eyes diagnosed with primary open angle glaucoma (POAG) (164 early POAG, 44 moderate POAG and 44 advanced POAG) and 223 eyes of control subjects. All patients underwent visual field testing (Humphrey Field Analyser, SITA-Standard 24-2, CZM), and SD-OCT imaging (Cirrus HD-OCT) of the macular and optic nerve head regions (ganglion cell analysis (GCA), macular cube 200×200; optic disc cube 200×200). OCT macular scans were segmented into macular nerve fiber layer (mNFL), ganglion cell layer with inner plexiform layer (GCIPL), outer retinal layers, and ganglion cell complex (GCC) (mNFL+GCIPL). Glaucoma discriminating ability was assessed using the area under the receiver operator characteristic curve (AUC) for all macular parameters and mean circumpapillary retinal nerve fibre layer (cpRNFL).

RESULTS

For the entire POAG population of this study, the minimum GCIPL index provided greater diagnostic ability than the other parameters studied, with a statistically significant difference in their AUC (minimum GCIPL [0.887], mean GCIPL [0.865], GCC [0.863], cpRNFL [0.823], mean mNFL [0.786] and minimum mNFL [0.742]). The results were similar in the early POAG group but without any statistically significant difference with the cpRNFL parameter. In the moderate POAG group, the diagnostic ability was similar for all indices, whereas in the advanced POAG group, minimum GCIPL and GCC gave the largest AUC indices.

DISCUSSION AND CONCLUSION

The minimum macular GCIPL is a new index obtained with the GCA algorithm of the Cirrus HD-OCT. It appears to have an excellent ability to detect glaucoma at every stage and demonstrates performance comparable to that of the cpRNFL parameter, in combination with which it may provide important complementary information for clinical practice.

Advances in the Structural Evaluation of Glaucoma with Optical Coherence Tomography

Optical coherence tomography (OCT) is capable of providing quantitative and objective assessments of the optic disc, macula and retinal nerve fiber layer in glaucoma. The recent advent of spectral domain OCT (SD-OCT) has enhanced the resolution, decreased scan acquisition time, and improved the reproducibility of measurements compared to older versions of this technology. However, although OCT has been successfully used for detection of disease and evaluation of progression, the limited agreement between structural and functional tests indicates the strong need for a combined approach for detecting and monitoring the disease. A recently described approach for estimation of rates of retinal ganglion cell loss from a combination of SD-OCT and functional data is a promising method for diagnosing, staging, detecting progression, and estimating rates of glaucomatous deterioration.

The effect of software upgrade on optical coherence tomography measurement of the retinal nerve fiber layer thickness

Purpose:

To determine the effect of software upgrades on retinal nerve fiber layer (RNFL) thickness measurements taken by spectral domain optical coherence tomography (SD-OCT).

Methods:

Eighty normal eyes (40 patients) were scanned for RNFL thickness measurements using Spectralis (Heidelberg Engineering, Heidelberg, Germany) SD-OCT. Scan analysis was performed using version 4.0 software and then reanalyzed with version 5.1.3. Student paired t testing and Pearson's correlation coefficient were used for statistical analysis.

Results:

Average and quadrant RNFL thicknesses generated using version 4.0 and 5.1.3 software on Spectralis demonstrated high correlation (r = 0.955-0.998). Average RNFL thickness using version 4.0 was 0.08μm thinner than version 5.1.3 (p = 0.409). Quadrant RNFL differences ranged from -0.26 to +0.97μm (p = 0.146-0.915). Segmentation errors were reduced 33% after the upgrade.

Conclusion:

Minor RNFL thickness changes may occur after software upgrades in Spectralis OCT. The differences did not reach statistical significance but segmentation errors were improved.

Optimizing the information yield of 3-D OCT in glaucoma

PURPOSE

To determine, first, which regions of 3-D optical coherence tomography (OCT) volumes can be segmented completely in the majority of subjects and, second, the relationship between analyzed area and thickness measurement test-retest variability.

METHODS

Three-dimensional OCT volumes (6 × 6 mm) centered around the fovea and optic nerve head (ONH) of 925 Rotterdam Study participants were analyzed; 44 participants were scanned twice. Volumes were segmented into 10 layers, and we determined the area where all layers could be identified in at least 95% (macula) or 90% (ONH) of subjects. Macular volumes were divided in 2 × 2, 4 × 4, 6 × 6, 8 × 8, or 68 blocks. We placed two circles around the ONH; the ONH had to fit into the smaller circle, and the larger circle had to fit into the segmentable part of the volume. The area between the circles was divided in 3 to 12 segments. We determined the test-retest variability (coefficient of repeatability) of the retinal nerve fiber layer (RNFL) and ganglion cell layer (RGCL) thickness measurements as a function of size of blocks/segments.

RESULTS

Eighty-two percent of the macular volume could be segmented in at least 95% of subjects; for the ONH, this was 65% in at least 90%. The radii of the circles were 1.03 and 1.84 mm. Depending on the analyzed area, median test-retest variability ranged from 8% to 15% for macular RNFL, 11% to 22% for macular RGCL, 5% to 11% for the two together, and 18% to 22% for ONH RNFL.

CONCLUSIONS

Test-retest variability hampers a detailed analysis of 3-D OCT data. Combined macular RNFL and RGCL thickness averaged over larger areas had the best test-retest variability.

Quantitative analysis of the intraretinal layers and optic nerve head using ultra-high resolution optical coherence tomography

This study is designed to test the repeatability of the quantitative analysis of intraretinal layer thickness and cup-disc ratio of the optic nerve head using ultra-high resolution optical coherence tomography (UHR-OCT). Group A, containing 23 eyes of 12 healthy subjects, was imaged twice and group B, containing eight eyes of four subjects, was imaged three times. Intraretinal layers were segmented manually and the cup-to-disc ratio of the optic nerve head was analyzed. Custom-built automatic segmentation software was also used to segment a set of images for comparison. A total of nine intraretinal layers were visualized and extracted manually. With group A, the central foveal thickness was 186.4 ± 15.9 μm (mean ± SD). The average retinal thickness was 296.4 ± 21.3 μm. The best repeatability, obtained when two repeated scans were taken, was obtained for the outer nuclear layer followed by the ganglion cell layer, the inner nuclear layer, the retinal nerve fiber layer and the worst was obtained for the outer segment. The intraclass correlation ranged from 0.824 to 0.997. The coefficients of repeatability ranged from 3.24 to 18.3 μm, corresponding to 1.47% to 26.20%. With group B, high interclass correlations were found and the automatic segmentation results were compatible with the manual results. Our results indicated that more retinal features might be imageable using UHR-OCT.

In vivo evaluation of retinal neurodegeneration in patients with multiple sclerosis

Objective

To evaluate macular morphology in the eyes of patients with multiple sclerosis (MS) with or without optic neuritis (ON) in previous history.

Methods

Optical coherence tomography (OCT) examination was performed in thirty-nine patients with MS and in thirty-three healthy subjects. The raw macular OCT data were processed using OCTRIMA software. The circumpapillary retinal nerve fiber layer (RNFL) thickness and the weighted mean thickness of the total retina and 6 intraretinal layers were obtained for each eye. The eyes of MS patients were divided into a group of 39 ON-affected eyes, and into a group of 34 eyes with no history of ON for the statistical analyses. Receiver operating characteristic (ROC) curves were constructed to determine which parameter can discriminate best between the non-affected group and controls.

Results

The circumpapillary RNFL thickness was significantly decreased in the non-affected eyes compared to controls group only in the temporal quadrant (p = 0.001) while it was decreased in the affected eyes of the MS patients in all quadrants compared to the non-affected eyes (p<0.05 in each comparison). The thickness of the total retina, RNFL, ganglion cell layer and inner plexiform layer complex (GCL+IPL) and ganglion cell complex (GCC, comprising the RNFL and GCL+IPL) in the macula was significantly decreased in the non-affected eyes compared to controls (p<0.05 for each comparison) and in the ON-affected eyes compared to the non-affected eyes (p<0.001 for each comparison). The largest area under the ROC curve (0.892) was obtained for the weighted mean thickness of the GCC. The EDSS score showed the strongest correlation with the GCL+IPL and GCC thickness (p = 0.007, r = 0.43 for both variables).

Conclusions

Thinning of the inner retinal layers is present in eyes of MS patients regardless of previous ON. Macular OCT image segmentation might provide a better insight into the pathology of neuronal loss and could therefore play an important role in the diagnosis and follow-up of patients with MS.

The structure and function of the macula in patients with advanced retinitis pigmentosa

PURPOSE

To assess the structure and function of the macula in advanced retinitis pigmentosa (RP).

METHODS

Twenty-nine eyes of 22 patients with RP were compared against 17 control eyes. Time-domain optical coherence tomography (OCT) data were processed using OCTRIMA (optical coherence tomography retinal image analysis) as a means of quantifying commercial OCT system images. The thickness of the retinal nerve fiber layer (RNFL), ganglion cell layer and inner plexiform layer complex (GCL+IPL), inner nuclear layer and outer plexiform layer complex (INL+OPL), and the outer nuclear layer (ONL) were measured. Multifocal electroretinography (mfERG) was performed; two groups were formed based on the mfERG findings. Fourteen eyes had no detectable central retinal function (NCRF) on mfERG; detectable but abnormal retinal function (DRF) was present in the mfERG of the other 15 eyes.

RESULTS

The thickness of the ONL in the central macular region was significantly less in the NCRF eyes compared with that in both DRF eyes and controls. The ONL was significantly thinner in the pericentral region in both patient groups compared with that in controls, whereas the thickness of the GCL+IPL and INL+OPL was significantly decreased only in the NCRF eyes. The RNFL in the peripheral region was significantly thicker, whereas the thickness of the GCL+IPL and ONL was significantly thinner in both patient groups compared with that in controls.

CONCLUSIONS

The results are consistent with degeneration of the outer retina preceding inner retinal changes in RP. OCT image segmentation enables objective evaluation of retinal structural changes in RP, with potential use in the planning of therapeutic interventions and conceivably as an outcome measure.

Macular ganglion cell-inner plexiform layer: automated detection and thickness reproducibility with spectral domain-optical coherence tomography in glaucoma

PURPOSE

To demonstrate the capability of SD-OCT to measure macular retinal ganglion cell-inner plexiform layer (GCIPL) thickness and to assess its reproducibility in glaucomatous eyes.

METHODS

Fifty-one glaucomatous eyes (26 mild, 11 moderate, 14 severe) of 51 patients underwent macular scanning using the Cirrus HD-OCT (Carl Zeiss Meditec, Dublin, CA) macula 200×200 acquisition protocol. Five scans were obtained on 5 days within 2 months. The ganglion cell analysis (GCA) algorithm was used to detect the macular GCIPL and to measure the thickness of the overall average, minimum, superotemporal, superior, superonasal, inferonasal, inferior, and inferotemporal GCIPL. The reproducibility of the measurements was evaluated with intraclass correlation coefficients (ICCs), coefficients of variation (COVs), and test-retest standard deviations (TRTSDs).

RESULTS

Segmentation and measurement of GCIPL thickness were successful in 50 of 51 subjects. All ICCs ranged between 0.94 and 0.98, but ICCs for average and superior GCIPL parameters (0.97-0.98) were slightly higher than for inferior GCIPL parameters (0.94-0.97). All COVs were <5%, with 1.8% for average GCIPL and COVs for superior GCIPL parameters (2.2%-3.0%) slightly lower than those for inferior GCIPL parameters (2.5%-3.6%). The TRTSD was lowest for average GCIPL (1.16 μm) and varied from 1.43 to 2.15 μm for sectoral GCIPL CONCLUSIONS: The Cirrus HD-OCT GCA algorithm can successfully segment macular GCIPL and measure GCIPL thickness with excellent intervisit reproducibility. Longitudinal monitoring of GCIPL thickness may be possible with Cirrus HD-OCT for assessing glaucoma progression.

Common-path low-coherence interferometry fiber-optic sensor guided microincision

We propose and demonstrate a common-path low-coherence interferometry (CP-LCI) fiber-optic sensor guided precise microincision. The method tracks the target surface and compensates the tool-to-surface relative motion with better than ± 5 μm resolution using a precision micromotor connected to the tool tip. A single-fiber distance probe integrated microdissector was used to perform an accurate 100 μm incision into the surface of an Intralipid phantom. The CP-LCI guided incision quality in terms of depth was evaluated afterwards using three-dimensional Fourier-domain optical coherence tomography imaging, which showed significant improvement of incision accuracy compared to free-hand-only operations.

Comparison of retinal thickness by Fourier-domain optical coherence tomography and OCT retinal image analysis software segmentation analysis derived from Stratus optical coherence tomography images

PURPOSE

To compare thickness measurements between Fourier-domain optical coherence tomography (FD-OCT) and time-domain OCT images analyzed with a custom-built OCT retinal image analysis software (OCTRIMA).

METHODS

Macular mapping (MM) by StratusOCT and MM5 and MM6 scanning protocols by an RTVue-100 FD-OCT device are performed on 11 subjects with no retinal pathology. Retinal thickness (RT) and the thickness of the ganglion cell complex (GCC) obtained with the MM6 protocol are compared for each early treatment diabetic retinopathy study (ETDRS)-like region with corresponding results obtained with OCTRIMA. RT results are compared by analysis of variance with Dunnett post hoc test, while GCC results are compared by paired t-test.

RESULTS

A high correlation is obtained for the RT between OCTRIMA and MM5 and MM6 protocols. In all regions, the StratusOCT provide the lowest RT values (mean difference 43 ± 8 μm compared to OCTRIMA, and 42 ± 14 μm compared to RTVue MM6). All RTVue GCC measurements were significantly thicker (mean difference between 6 and 12 μm) than the GCC measurements of OCTRIMA.

CONCLUSION

High correspondence of RT measurements is obtained not only for RT but also for the segmentation of intraretinal layers between FD-OCT and StratusOCT-derived OCTRIMA analysis. However, a correction factor is required to compensate for OCT-specific differences to make measurements more comparable to any available OCT device.

Revealing Henle's fiber layer using spectral domain optical coherence tomography

PURPOSE

Spectral domain optical coherence tomography (SD-OCT) uses infrared light to visualize the reflectivity of structures of differing optical properties within the retina. Despite their presence on histologic studies, traditionally acquired SD-OCT images are unable to delineate the axons of photoreceptor nuclei, Henle's fiber layer (HFL). The authors present a new method to reliably identify HFL by varying the entry position of the SD-OCT beam through the pupil.

METHODS

Fifteen eyes from 11 subjects with normal vision were prospectively imaged using 1 of 2 commercial SD-OCT systems. For each eye, the entry position of the SD-OCT beam through the pupil was varied horizontally and vertically. The reflectivity of outer retinal layers was measured as a function of beam position, and thicknesses were recorded.

RESULTS

The reflectivity of HFL was directionally dependent and increased with eccentricity on the side of the fovea opposite the entry position. When HFL was included in the measurement, the thickness of the outer nuclear layer (ONL) of central horizontal B-scans increased by an average of 52% in three subjects quantified. Four cases of pathology, in which alterations to the normal macular geometry affected HFL intensity, were identified.

CONCLUSIONS

The authors demonstrated a novel method to distinguish HFL from true ONL. An accurate measurement of the ONL is critical to clinical studies measuring photoreceptor layer thickness using any SD-OCT system. Recognition of the optical properties of HFL can explain reflectivity changes imaged in this layer in association with macular pathology.

Improving image segmentation performance and quantitative analysis via a computer-aided grading methodology for optical coherence tomography retinal image analysis

We demonstrate quantitative analysis and error correction of optical coherence tomography (OCT) retinal images by using a custom-built, computer-aided grading methodology. A total of 60 Stratus OCT (Carl Zeiss Meditec, Dublin, California) B-scans collected from ten normal healthy eyes are analyzed by two independent graders. The average retinal thickness per macular region is compared with the automated Stratus OCT results. Intergrader and intragrader reproducibility is calculated by Bland-Altman plots of the mean difference between both gradings and by Pearson correlation coefficients. In addition, the correlation between Stratus OCT and our methodology-derived thickness is also presented. The mean thickness difference between Stratus OCT and our methodology is 6.53 microm and 26.71 microm when using the inner segment/outer segment (IS/OS) junction and outer segment/retinal pigment epithelium (OS/RPE) junction as the outer retinal border, respectively. Overall, the median of the thickness differences as a percentage of the mean thickness is less than 1% and 2% for the intragrader and intergrader reproducibility test, respectively. The measurement accuracy range of the OCT retinal image analysis (OCTRIMA) algorithm is between 0.27 and 1.47 microm and 0.6 and 1.76 microm for the intragrader and intergrader reproducibility tests, respectively. Pearson correlation coefficients demonstrate R(2)>0.98 for all Early Treatment Diabetic Retinopathy Study (ETDRS) regions. Our methodology facilitates a more robust and localized quantification of the retinal structure in normal healthy controls and patients with clinically significant intraretinal features.