Search

Purpose: The integration of artificial intelligence (AI), particularly deep learning (DL), with optical coherence tomography (OCT) offers significant opportunities in the diagnosis and management of glaucoma. This article explores the application of various DL models in enhancing OCT capabilities and addresses the challenges associated with their clinical implementation., Methods: A review of articles utilizing DL models was conducted, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), autoencoders, and large language models (LLMs). Key developments and practical applications of these models in OCT image analysis were emphasized, particularly in the context of enhancing image quality, glaucoma diagnosis, and monitoring progression., Results: CNNs excel in segmenting retinal layers and detecting glaucomatous damage, whereas RNNs are effective in analyzing sequential OCT scans for disease progression. GANs enhance image quality and data augmentation, and autoencoders facilitate advanced feature extraction. LLMs show promise in integrating textual and visual data for comprehensive diagnostic assessments. Despite these advancements, challenges such as data availability, variability, potential biases, and the need for extensive validation persist., Conclusions: DL models are reshaping glaucoma management by enhancing OCT's diagnostic capabilities. However, the successful translation into clinical practice requires addressing major challenges related to data variability, biases, fairness, and model validation to ensure accurate and reliable patient care., Translational Relevance: This review bridges the gap between basic research and clinical care by demonstrating how AI, particularly DL models, can markedly enhance OCT's clinical utility in diagnosis, monitoring, and prediction, moving toward more individualized, personalized, and precise treatment strategies.

Purpose: This study aims to investigate the prevalence of artifacts in optical coherence tomography (OCT) images with acceptable signal strength and evaluate the performance of supervised deep learning models in improving OCT image quality assessment., Methods: We conducted a retrospective study on 4555 OCT images from 546 patients, with each image having an acceptable signal strength (≥6). A comprehensive analysis of prevalent OCT artifacts was performed, and five pretrained convolutional neural network models were trained and tested to infer images based on quality., Results: Our results showed a high prevalence of artifacts in OCT images with acceptable signal strength. Approximately 21% of images were labeled as nonacceptable quality. The EfficientNetV2 model demonstrated superior performance in classifying OCT image quality, achieving an area under the receiver operating characteristic curve of 0.950 ± 0.007 and an area under the precision recall curve of 0.985 ± 0.002., Conclusions: The findings highlight the limitations of relying solely on signal strength for OCT image quality assessment and the potential of deep learning models in accurately classifying image quality., Translational Relevance: Application of the deep learning-based OCT image quality assessment models may improve the OCT image data quality for both clinical applications and research.

A multiscale extension for the well-known block matching and 4D filtering (BM4D) method is proposed by analyzing and extending the wavelet subbands denoising method in such a way that the proposed method avoids directly denoising detail subbands, which considerably simplifies the computations and makes the multiscale processing feasible in 3D. To this end, we first derive the multiscale construction method in 2D and propose multiscale extensions for three 2D natural image denoising methods. Then, the derivation is extended to 3D by proposing mixed multiscale BM4D (mmBM4D) for optical coherence tomography (OCT) image denoising. We tested mmBM4D on three public OCT datasets captured by various imaging devices. The experiments revealed that mmBM4D significantly outperforms its original counterpart and performs on par with the state-of-the-art OCT denoising methods. In terms of peak-signal-to-noise-ratio (PSNR), mmBM4D surpasses the original BM4D by more than 0.68 decibels over the first dataset. In the second and third datasets, significant improvements in the mean to standard deviation ratio, contrast to noise ratio, and equivalent number of looks were achieved. Furthermore, on the downstream task of retinal layer segmentation, the layer quality preservation of the compared OCT denoising methods is evaluated., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier Ltd.)

Background: Spectral-domain (SD-) optical coherence tomography (OCT) can reliably measure axonal (peripapillary retinal nerve fiber layer [pRNFL]) and neuronal (macular ganglion cell + inner plexiform layer [GCIPL]) thinning in the retina. Measurements from 2 commonly used SD-OCT devices are often pooled together in multiple sclerosis (MS) studies and clinical trials despite software and segmentation algorithm differences; however, individual pRNFL and GCIPL thickness measurements are not interchangeable between devices. In some circumstances, such as in the absence of a consistent OCT segmentation algorithm across platforms, a conversion equation to transform measurements between devices may be useful to facilitate pooling of data. The availability of normative data for SD-OCT measurements is limited by the lack of a large representative world-wide sample across various ages and ethnicities. Larger international studies that evaluate the effects of age, sex, and race/ethnicity on SD-OCT measurements in healthy control participants are needed to provide normative values that reflect these demographic subgroups to provide comparisons to MS retinal degeneration., Methods: Participants were part of an 11-site collaboration within the International Multiple Sclerosis Visual System (IMSVISUAL) consortium. SD-OCT was performed by a trained technician for healthy control subjects using Spectralis or Cirrus SD-OCT devices. Peripapillary pRNFL and GCIPL thicknesses were measured on one or both devices. Automated segmentation protocols, in conjunction with manual inspection and correction of lines delineating retinal layers, were used. A conversion equation was developed using structural equation modeling, accounting for clustering, with healthy control data from one site where participants were scanned on both devices on the same day. Normative values were evaluated, with the entire cohort, for pRNFL and GCIPL thicknesses for each decade of age, by sex, and across racial groups using generalized estimating equation (GEE) models, accounting for clustering and adjusting for within-patient, intereye correlations. Change-point analyses were performed to determine at what age pRNFL and GCIPL thicknesses exhibit accelerated rates of decline., Results: The healthy control cohort (n = 546) was 54% male and had a wide distribution of ages, ranging from 18 to 87 years, with a mean (SD) age of 39.3 (14.6) years. Based on 346 control participants at a single site, the conversion equation for pRNFL was Cirrus = -5.0 + (1.0 × Spectralis global value). Based on 228 controls, the equation for GCIPL was Cirrus = -4.5 + (0.9 × Spectralis global value). Standard error was 0.02 for both equations. After the age of 40 years, there was a decline of -2.4 μm per decade in pRNFL thickness ( P < 0.001, GEE models adjusting for sex, race, and country) and -1.4 μm per decade in GCIPL thickness ( P < 0.001). There was a small difference in pRNFL thickness based on sex, with female participants having slightly higher thickness (2.6 μm, P = 0.003). There was no association between GCIPL thickness and sex. Likewise, there was no association between race/ethnicity and pRNFL or GCIPL thicknesses., Conclusions: A conversion factor may be required when using data that are derived between different SD-OCT platforms in clinical trials and observational studies; this is particularly true for smaller cross-sectional studies or when a consistent segmentation algorithm is not available. The above conversion equations can be used when pooling data from Spectralis and Cirrus SD-OCT devices for pRNFL and GCIPL thicknesses. A faster decline in retinal thickness may occur after the age of 40 years, even in the absence of significant differences across racial groups., (Copyright © 2022 by North American Neuro-Ophthalmology Society.)

Purpose: The lamina cribrosa (LC) has an important role in the pathophysiology of ocular diseases. The purpose of this study is to characterize in vivo, noninvasively, and in 3D the structure of the LC in healthy non-human primates (NHPs)., Methods: Spectral-domain optical coherence tomography (OCT; Leica, Chicago, IL) scans of the optic nerve head (ONH) were obtained from healthy adult rhesus macaques monkeys. Using a previously reported semi-automated segmentation algorithm, microstructure measurements were assessed in central and peripheral regions of an equal area, in quadrants and depth-wise. Linear mixed-effects models were used to compare parameters among regions, adjusting for visibility, age, analyzable depth, graded scan quality, disc area, and the correlation between eyes. Spearmen's rank correlation coefficients were calculated for assessing the association between the lamina's parameters., Results: Sixteen eyes of 10 animals (7 males and 3 females; 9 OD, 7 OS) were analyzed with a mean age of 10.5 ± 2.1 years. The mean analyzable depth was 175 ± 37 µm, with average LC visibility of 25.4 ± 13.0% and average disc area of 2.67 ± 0.45mm2. Within this volume, an average of 74.9 ± 39.0 pores per eye were analyzed. The central region showed statistically significantly thicker beams than the periphery. The quadrant-based analysis showed significant differences between the superior and inferior quadrants. The anterior LC had smaller beams and pores than both middle and posterior lamina., Conclusions: Our study provides in vivo microstructure details of NHP's LC to be used as the foundation for future studies. We demonstrated mostly small but statistically significant regional variations in LC microstructure that should be considered when comparing LC measurements.

Purpose: Growing evidence suggests that dendrite retraction or degeneration in a subpopulation of the retinal ganglion cells (RGCs) may precede detectable soma abnormalities and RGC death in glaucoma. Visualization of the lamellar structure of the inner plexiform layer (IPL) could advance clinical management and fundamental understanding of glaucoma. We investigated whether visible-light optical coherence tomography (vis-OCT) could detect the difference in the IPL sublayer thicknesses between small cohorts of healthy and glaucomatous subjects., Method: We imaged nine healthy and five glaucomatous subjects with vis-OCT. Four of the healthy subjects were scanned three times each in two separate visits, and five healthy and five glaucoma subjects were scanned three times during a single visit. IPL sublayers were manually segmented using averaged A-line profiles., Results: The mean ages of glaucoma and healthy subjects are 59.6 ± 13.4 and 45.4 ± 14.4 years (P = 0.02.) The visual field mean deviations (MDs) are -26.4 to -7.7 dB in glaucoma patients and -1.6 to 1.1 dB in healthy subjects (P = 0.002). Median coefficients of variation (CVs) of intrasession repeatability for the entire IPL and three sublayers are 3.1%, 5.6%, 6.9%, and 5.6% in healthy subjects and 1.8%, 6.0%, 7.7%, and 6.2% in glaucoma patients, respectively. The mean IPL thicknesses are 36.2 ± 1.5 µm in glaucomatous and 40.1 ± 1.7 µm in healthy eyes (P = 0.003)., Conclusions: IPL sublayer analysis revealed that the middle sublayer could be responsible for the majority of IPL thinning in glaucoma. Vis-OCT quantified IPL sublayers with good repeatability in both glaucoma and healthy subjects.

Purpose: To determine which spectral domain optical coherence tomography biomarkers of idiopathic macular hole (MH) correlate with the postoperative best-corrected visual acuity (BCVA) in anatomically closed MH., Methods: Retrospective analysis of spectral domain optical coherence tomography scans of 44 patients presenting with MH followed for a mean of 17 months. Widths of MH aperture, base, and ellipsoid zone disruption were calculated from presenting foveal spectral domain optical coherence tomography B-scans. Macular hole base area and ellipsoid zone disruption area were calculated through the custom in-house software., Results: Poorer postoperative BCVA correlated with increased preoperative choroidal hypertransmission (r = 0.503, P = 0.0005), minimum diameter (r = 0.491, P = 0.0007), and base diameter (r = 0.319, P = 0.0348), but not with preoperative ellipsoid zone width (r = 0.199, P = 0.2001). Applying en-face analysis, the BCVA correlated weakly with preoperative ellipsoid zone loss area (r = 0.380, P = 0.013), but not with preoperative MH base area (r = 0.253, P = 0.1058)., Conclusion: Increased MH minimum diameter, base diameter, base area, and choroidal hypertransmission are correlated with a poorer postoperative BCVA. Ellipsoid zone loss measurements were not consistently correlated with a BCVA. Choroidal hypertransmission width may be an easy-to-visualize predictive imaging biomarker in MH surgery.

The direct analysis of 3D Optical Coherence Tomography (OCT) volumes enables deep learning models (DL) to learn spatial structural information and discover new bio-markers that are relevant to glaucoma. Downsampling 3D input volumes is the state-of-art solution to accommodate for the limited number of training volumes as well as the available computing resources. However, this limits the network's ability to learn from small retinal structures in OCT volumes. In this paper, our goal is to improve the performance by providing guidance to DL model during training in order to learn from finer ocular structures in 3D OCT volumes. Therefore, we propose an end-to-end attention guided 3D DL model for glaucoma detection and estimating visual function from retinal structures. The model consists of three pathways with the same network architecture but different inputs. One input is the original 3D-OCT cube and the other two are computed during training guided by the 3D gradient class activation heatmaps. Each pathway outputs the class-label and the whole model is trained concurrently to minimize the sum of losses from three pathways. The final output is obtained by fusing the predictions of the three pathways. Also, to explore the robustness and generalizability of the proposed model, we apply the model on a classification task for glaucoma detection as well as a regression task to estimate visual field index (VFI) (a value between 0 and 100). A 5-fold cross-validation with a total of 3782 and 10,370 OCT scans is used to train and evaluate the classification and regression models, respectively. The glaucoma detection model achieved an area under the curve (AUC) of 93.8% compared with 86.8% for a baseline model without the attention-guided component. The model also outperformed six different feature based machine learning approaches that use scanner computed measurements for training. Further, we also assessed the contribution of different retinal layers that are relevant to glaucoma. The VFI estimation model achieved a Pearson correlation and median absolute error of 0.75 and 3.6%, respectively, for a test set of size 3100 cubes.

Purpose: The purpose of this study was to develop a machine learning model to forecast future circumpapillary retinal nerve fiber layer (cpRNFL) thickness in eyes of healthy, glaucoma suspect, and glaucoma participants from multimodal temporal data., Design: Retrospective analysis of a longitudinal clinical cohort., Participants: Longitudinal clinical cohort of healthy, glaucoma suspect, and glaucoma participants., Methods: The forecasting models used multimodal patient information including clinical (age and intraocular pressure), structural (cpRNFL thickness derived from scans as well as deep learning-derived OCT image features), and functional (visual field test parameters) data and the intervisit interval for prediction of cpRNFL thickness at the next visit. Four models were developed based on the number of visits used (n = 1 to 4). Longitudinal data from 1089 participants (mean observation period, 3.65±1.73 years) was used with 80% of the cohort for the development of the models. The results of our models were compared with those of a commonly adopted linear regression model, which we refer to here as linear trend-based estimation (LTBE)., Main Outcome Measures: The mean absolute difference and Pearson's correlation coefficient between the true and forecasted values of the cpRNFL in the healthy, glaucoma suspect, and glaucoma patients., Results: The best forecasting model of cpRNFL was obtained using 3 visits and incorporated deep learning-derived OCT image features. The mean error was 1.10±0.60 μm, 1.79±1.73 μm, and 1.87±1.85 μm in eyes of healthy, glaucoma suspect, and glaucoma participants, respectively. Our method significantly outperformed the LTBE model for glaucoma suspect and glaucoma participants ( P < 0.001), which showed a mean error of 1.55±1.16 μm, 2.4±2.67 μm, and 3.02±3.06 μm in the 3 groups, respectively. The Pearson's correlation coefficient between the forecasted value and the measured thickness was ρ = 0.96 ( P < 0.01), ρ = 0.95 ( P < 0.01), and ρ = 0.96 ( P < 0.01) for the 3 groups, respectively., Conclusions: The performance of the proposed forecasting model for cpRNFL is consistent across glaucoma suspect and glaucoma patients, which implies the robustness of the developed model against the disease state. These forecasted values may be useful to personalize patient care by determining the most appropriate intervisit schedule for timely interventions.

Optical coherence tomography (OCT) based measurements of retinal layer thickness, such as the retinal nerve fibre layer (RNFL) and the ganglion cell with inner plexiform layer (GCIPL) are commonly employed for the diagnosis and monitoring of glaucoma. Previously, machine learning techniques have relied on segmentation-based imaging features such as the peripapillary RNFL thickness and the cup-to-disc ratio. Here, we propose a deep learning technique that classifies eyes as healthy or glaucomatous directly from raw, unsegmented OCT volumes of the optic nerve head (ONH) using a 3D Convolutional Neural Network (CNN). We compared the accuracy of this technique with various feature-based machine learning algorithms and demonstrated the superiority of the proposed deep learning based method. Logistic regression was found to be the best performing classical machine learning technique with an AUC of 0.89. In direct comparison, the deep learning approach achieved a substantially higher AUC of 0.94 with the additional advantage of providing insight into which regions of an OCT volume are important for glaucoma detection. Computing Class Activation Maps (CAM), we found that the CNN identified neuroretinal rim and optic disc cupping as well as the lamina cribrosa (LC) and its surrounding areas as the regions significantly associated with the glaucoma classification. These regions anatomically correspond to the well established and commonly used clinical markers for glaucoma diagnosis such as increased cup volume, cup diameter, and neuroretinal rim thinning at the superior and inferior segments., Competing Interests: Authors S.M., B.A, and R.G. are employed by IBM Research Australia. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Purpose: Previously, we introduced a state-based 2-dimensional continuous-time hidden Markov model (2D CT HMM) to model the pattern of detected glaucoma changes using structural and functional information simultaneously. The purpose of this study was to evaluate the detected glaucoma change prediction performance of the model in a real clinical setting using a retrospective longitudinal dataset., Design: Longitudinal, retrospective study., Participants: One hundred thirty-four eyes from 134 participants diagnosed with glaucoma or as glaucoma suspects (average follow-up, 4.4±1.2 years; average number of visits, 7.1±1.8)., Methods: A 2D CT HMM model was trained using OCT (Cirrus HD-OCT; Zeiss, Dublin, CA) average circumpapillary retinal nerve fiber layer (cRNFL) thickness and visual field index (VFI) or mean deviation (MD; Humphrey Field Analyzer; Zeiss). The model was trained using a subset of the data (107 of 134 eyes [80%]) including all visits except for the last visit, which was used to test the prediction performance (training set). Additionally, the remaining 27 eyes were used for secondary performance testing as an independent group (validation set). The 2D CT HMM predicts 1 of 4 possible detected state changes based on 1 input state., Main Outcome Measures: Prediction accuracy was assessed as the percentage of correct prediction against the patient's actual recorded state. In addition, deviations of the predicted long-term detected change paths from the actual detected change paths were measured., Results: Baseline mean ± standard deviation age was 61.9±11.4 years, VFI was 90.7±17.4, MD was -3.50±6.04 dB, and cRNFL thickness was 74.9±12.2 μm. The accuracy of detected glaucoma change prediction using the training set was comparable with the validation set (57.0% and 68.0%, respectively). Prediction deviation from the actual detected change path showed stability throughout patient follow-up., Conclusions: The 2D CT HMM demonstrated promising prediction performance in detecting glaucoma change performance in a simulated clinical setting using an independent cohort. The 2D CT HMM allows information from just 1 visit to predict at least 5 subsequent visits with similar performance., (Copyright © 2018 American Academy of Ophthalmology. Published by Elsevier Inc. All rights reserved.)

In this paper, we propose a novel classification model for automatically identifying individuals with age-related macular degeneration (AMD) or Diabetic Macular Edema (DME) using retinal features from Spectral Domain Optical Coherence Tomography (SD-OCT) images. Our classification method uses retinal features such as the thickness of the retina and the thickness of the individual retinal layers, and the volume of the pathologies such as drusen and hyper-reflective intra-retinal spots. We extract automatically, ten clinically important retinal features by segmenting individual SD-OCT images for classification purposes. The effectiveness of the extracted features is evaluated using several classification methods such as Random Forrest on 251 (59 normal, 177 AMD and 15 DME) subjects. We have performed 15-fold cross-validation tests for three phenotypes; DME, AMD and normal cases using these data sets and achieved accuracy of more than 95% on each data set with the classification method using Random Forrest. When we trained the system as a two-class problem of normal and eye with pathology, using the Random Forrest classifier, we obtained an accuracy of more than 96%. The area under the receiver operating characteristic curve (AUC) finds a value of 0.99 for each dataset. We have also shown the performance of four state-of-the-methods for classification the eye participants and found that our proposed method showed the best accuracy., Competing Interests: The authors have declared that no competing interests exist.

The choroid is vascular tissue located underneath the retina and supplies oxygen to the outer retina; any damage to this tissue can be a precursor to retinal diseases. This paper presents an automated method of choroidal segmentation from Enhanced Depth Imaging Optical Coherence Tomography (EDI-OCT) images. The Dijkstra shortest path algorithm is used to segment the choroid-sclera interface (CSI), the outermost border of the choroid. A novel intensity-normalisation technique that is based on the depth of the choroid is used to equalise the intensity of all non-vessel pixels in the choroid region. The outer boundary of choroidal vessel and CSI are determined approximately and incorporated to the edge weight of the CSI segmentation to choose optimal edge weights. This method is tested on 190 B-scans of 10 subjects against choroid thickness (CTh) results produced manually by two graders. For comparison, results obtained by two state-of-the-art automated methods and our proposed method are compared against the manual grading, and our proposed method performed the best. The mean root-mean-square error (RMSE) for finding the CSI boundary by our method is 7.71±6.29 pixels, which is significantly lower than the RMSE for the two other state-of-the-art methods (36.17±11.97 pixels and 44.19±19.51 pixels). The correlation coefficient for our method is 0.76, and 0.51 and 0.66 for the other two state-of-the-art methods. The interclass correlation coefficients are 0.72, 0.43 and 0.56 respectively. Our method is highly accurate, robust, reliable and consistent. This identification can enable to quantify the biomarkers of the choroidin large scale study for assessing, monitoring disease progression as well as early detection of retinal diseases. Identification of the boundary can help to determine the loss or change of choroid, which can be used as features for the automatic determination of the stages of retinal diseases., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Glaucoma is a leading cause of blindness that leads to characteristic changes in the optic nerve head (ONH) region, such as nasalization of vessels. It is unknown whether the spatial location of this vessel shift inside the ONH occurs within the lamina cribrosa (LC) or the prelaminar tissue. The purpose of this study was to compare the location of the central retinal vessel trunk (CRVT) in the LC and prelaminar tissue in living healthy and glaucomatous eyes. We acquired 3-dimensional ONH scans from 119 eyes (40 healthy, 29 glaucoma suspect, and 50 glaucoma) using optical coherence tomography (OCT). The CRVT location was manually delineated in separate projection images of the LC and prelamina. We found that the CRVT in glaucoma suspect and glaucomatous eyes was located significantly more nasally compared to healthy eyes at the level of the prelamina. There was no detectable difference found in the location of the CRVT at the level of the LC between diagnostic groups. While the nasal location of the CRVT in the prelamina has been associated with glaucomatous axonal death, our results suggest that the CRVT in the LC is anchored in the tissue with minimal variation in glaucomatous eyes.

Purpose: Evaluation of the effect of prelaminar tissue thickness on visualization of the lamina cribrosa (LC) using optical coherence tomography (OCT)., Methods: The optic nerve head (ONH) region was scanned using OCT. The quality of visible LC microstructure was assessed subjectively using a grading system and objectively by analyzing the signal intensity of each scan's superpixel components. Manual delineations were made separately and in 3-dimensions quantifying prelaminar tissue thickness, analyzable regions of LC microstructure, and regions with a visible anterior LC (ALC) boundary. A linear mixed effect model quantified the association between tissue thickness and LC visualization., Results: A total of 17 healthy, 27 glaucoma suspect, and 47 glaucomatous eyes were included. Scans with thicker average prelaminar tissue measurements received worse grading scores (P = 0.007), and superpixels with low signal intensity were associated significantly with regions beneath thick prelaminar tissue (P < 0.05). The average prelaminar tissue thickness in regions of scans where the LC was analyzable (214 μm) was significantly thinner than in regions where the LC was not analyzable (569 μm; P < 0.001). Healthy eyes had significantly thicker average prelaminar tissue measurements than glaucoma or glaucoma suspect eyes (both P < 0.001), and glaucoma suspect eyes had significantly thicker average prelaminar tissue measurements than glaucoma eyes (P = 0.008). Significantly more of the ALC boundary was visible in glaucoma eyes (63% of ONH) than in healthy eyes (41%; P = 0.005)., Conclusions: Thick prelaminar tissue was associated with impaired visualization of the LC. Healthy subjects generally had thicker prelaminar tissue, which potentially could create a selection bias against healthy eyes when comparing LC structures.

Purpose: Optical coherence tomography (OCT) can monitor for glaucoma by measuring dimensions of the optic nerve head (ONH) cup and disc. Multiple clinical studies have shown that different OCT devices yield different estimates of retinal dimensions. We developed phantoms mimicking ONH morphology as a new way to compare ONH measurements from different clinical OCT devices., Methods: Three phantoms were fabricated to model the ONH: One normal and two with glaucomatous anatomies. Phantoms were scanned with Stratus, RTVue, and Cirrus clinical devices, and with a laboratory OCT system as a reference. We analyzed device-reported ONH measurements of cup-to-disc ratio (CDR) and cup volume and compared them with offline measurements done manually and with a custom software algorithm, respectively., Results: The mean absolute difference between clinical devices with device-reported measurements versus offline measurements was 0.082 vs. 0.013 for CDR and 0.044 mm3 vs. 0.019 mm3 for cup volume. Statistically significant differences between devices were present for 16 of 18 comparisons of device-reported measurements from the phantoms. Offline Cirrus measurements tended to be significantly different from those from Stratus and RTVue., Conclusions: The interdevice differences in CDR and cup volume are primarily caused by the devices' proprietary ONH analysis algorithms. The three devices yield more similar ONH measurements when a consistent offline analysis technique is applied. Scan pattern on the ONH also may be a factor in the measurement differences. This phantom-based study has provided unique insights into characteristics of OCT measurements of the ONH.

Purpose: To evaluate longitudinal changes in circumpapillary retinal nerve fiber layer (RNFL) thickness, as measured by spectral-domain optical coherence tomography (SD OCT), in children with optic pathway gliomas., Design: Longitudinal cohort study., Methods: Global and quadrant-specific circumpapillary RNFL thickness measures were acquired using either a hand-held SD OCT during sedation or a table-top SD OCT in children old enough to cooperate. Vision loss was defined as either a 0.2 logMAR decline in visual acuity or progression of visual field. Percent change in circumpapillary RNFL thickness in eyes experiencing vision loss was compared to eyes with stable vision., Results: Fifty-five eyes completed 250 study visits. Ten eyes (18%) from 7 patients experienced a new episode of vision loss during the study and 45 eyes (82%) from 39 patients demonstrated stable vision across study visits. Percent decline of RNFL thickness between the baseline visit and first event of vision loss event was greatest in the superior (-14%) and inferior (-10%) quadrants as well as global average (-13%). Using a threshold of ≥10% decline in RNFL, the positive and negative predictive value for vision loss when 2 or more anatomic sectors were affected was 100% and 94%, respectively., Conclusions: Children experiencing vision loss from their optic pathway gliomas frequently demonstrate a ≥10% decline of RNFL thickness in 1 or more anatomic sectors. Global average and the inferior quadrant demonstrated the best positive and negative predictive values. Circumpapillary RNFL is a surrogate marker of vision and could be helpful in making treatment decisions for children with optic pathway gliomas., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Purpose: We minimized the influence of image quality variability, as measured by signal strength (SS), on optical coherence tomography (OCT) thickness measurements using the histogram matching (HM) method., Methods: We scanned 12 eyes from 12 healthy subjects with the Cirrus HD-OCT device to obtain a series of OCT images with a wide range of SS (maximal range, 1-10) at the same visit. For each eye, the histogram of an image with the highest SS (best image quality) was set as the reference. We applied HM to the images with lower SS by shaping the input histogram into the reference histogram. Retinal nerve fiber layer (RNFL) thickness was automatically measured before and after HM processing (defined as original and HM measurements), and compared to the device output (device measurements). Nonlinear mixed effects models were used to analyze the relationship between RNFL thickness and SS. In addition, the lowest tolerable SSs, which gave the RNFL thickness within the variability margin of manufacturer recommended SS range (6-10), were determined for device, original, and HM measurements., Results: The HM measurements showed less variability across a wide range of image quality than the original and device measurements (slope = 1.17 vs. 4.89 and 1.72 μm/SS, respectively). The lowest tolerable SS was successfully reduced to 4.5 after HM processing., Conclusions: The HM method successfully extended the acceptable SS range on OCT images. This would qualify more OCT images with low SS for clinical assessment, broadening the OCT application to a wider range of subjects.

Purpose: To determine the intra- and inter-visit reproducibility of ganglion cell-inner plexiform layer thickness measures using handheld optical coherence tomography (OCT) in sedated children with optic pathway gliomas and/or neurofibromatosis type 1 (NF1)., Design: Prospective longitudinal cohort study., Methods: Children with sporadic optic pathway gliomas and/or NF1 who had ≥2 volumes acquired over the macula using handheld OCT during sedation for clinically indicated magnetic resonance imaging were eligible for the intra-visit cohort. Children with repeat handheld OCT imaging within 6 months were eligible for the inter-visit cohort. Total retinal thickness and ganglion cell-inner plexiform layer thickness were measured using custom-designed automated segmentation software. Reproducibility was compared across average and anatomic quadrant by calculating the coefficient of variation (CV) and intraclass correlation coefficient (ICC)., Results: Forty-two subjects (median age 5.4 years, range 0.8-12.7 years) contributed 45 eyes to the intra-visit cohort. Thirty-one subject eyes had normal vision and 14 had abnormal vision (decreased visual acuity and/or visual field). Average and quadrant ganglion cell-inner plexiform layer measures demonstrated CVs ≤4.5% with excellent ICCs (>0.935). The superior quadrant CV differed between subjects with (4.4%) and without (2.1%) vision loss (P < .05). Twenty-five subject eyes were eligible for the inter-visit cohort, demonstrating CVs from 1.6% to 5.2%. Inter-visit ICCs were excellent (0.955-0.995)., Discussion: Handheld OCT imaging in sedated children with optic pathway gliomas produces highly reproducible measures of ganglion cell-inner plexiform layer thickness., (Copyright © 2014 Elsevier Inc. All rights reserved.)

Purpose: To determine the intra- and intervisit reproducibility of circumpapillary retinal nerve fiber layer (RNFL) measures using handheld optical coherence tomography (OCT) in sedated children., Design: Prospective cross-sectional and longitudinal study., Methods: Children undergoing sedation for a clinically indicated magnetic resonance imaging for an optic pathway glioma and/or neurofibromatosis type 1 (NF1) had multiple 6 × 6 mm volumes (isotropic 300 × 300 or nonisotropic 1000 × 100 samplings) acquired over the optic nerve. Children with 2 handheld OCT sessions within 6 months were included in the intervisit cohort. The intra- and intervisit coefficient of variation (CV) and intraclass correlation coefficient (ICC) were calculated for the average and anatomic quadrant circumpapillary RNFL thickness., Results: Fifty-nine subjects (mean age 5.1 years, range 0.8-13.0 years) comprised the intravisit cohort and 29 subjects (mean age 5.7 years, range 1.8-12.7 years) contributed to the intervisit cohort. Forty-nine subjects had an optic pathway glioma and 10 subjects had NF1 without an optic pathway glioma. The CV was comparable regardless of imaging with an isotropic and nonisotropic volume in both the intra- and intervisit cohorts. The average circumpapillary RNFL demonstrated the lowest CV and highest ICC compared to the quadrants. For the intervisit cohort, the average ICC was typically higher while the CV was typically lower, but not statistically different compared to the other quadrants., Discussion: Circumpapillary RNFL measures acquired with handheld OCT during sedation demonstrate good intra- and intervisit reproducibility. Handheld OCT has the potential to monitor progressive optic neuropathies in young children who have difficulty cooperating with traditional OCT devices., (Copyright © 2014 Elsevier Inc. All rights reserved.)

Purpose: To characterize the in vivo three-dimensional (3D) lamina cribrosa (LC) microarchitecture of healthy eyes using adaptive optics spectral-domain optical coherence tomography (AO-SDOCT)., Methods: A multimodal retinal imaging system with a light source centered at 1050 nm and AO confocal scanning laser ophthalmoscopy was used in this study. One randomly selected eye from 18 healthy subjects was scanned in a 6° × 6° window centered on the LC. Subjects also underwent scanning with Cirrus HD-OCT. Lamina cribrosa microarchitecture was semiautomatically segmented and quantified for connective tissue volume fraction (CTVF), beam thickness, pore diameter, pore area, and pore aspect ratio. The LC was assessed in central and peripheral regions of equal areas and quadrants and with depth. A linear mixed effects model weighted by the fraction of visible LC was used to compare LC structure between regions., Results: The nasal quadrant was excluded due to poor visualization. The central sector showed greater CTVF and thicker beams as compared to the periphery (P < 0.01). Both superior and inferior quadrants showed greater CTVF, pore diameter, and pore mean area than the temporal quadrant (P < 0.05). Depth analysis showed that the anterior and posterior aspects of the LC contained smaller pores with greater density and thinner beams as compared to the middle third (P < 0.05). The anterior third also showed a greater CTVF than the middle third (P < 0.05)., Conclusions: In vivo analysis of healthy eyes using AO-SDOCT showed significant, albeit small, regional variation in LC microarchitecture by quadrant, radially, and with depth, which should be considered in further studies of the LC., (Copyright 2014 The Association for Research in Vision and Ophthalmology, Inc.)

Purpose: To determine the reproducibility of automated segmentation of the three-dimensional (3D) lamina cribrosa (LC) microarchitecture scanned in-vivo using optical coherence tomography (OCT)., Methods: Thirty-nine eyes (8 healthy, 19 glaucoma suspects and 12 glaucoma) from 49 subjects were scanned twice using swept-source (SS-) OCT in a 3.5×3.5×3.64 mm (400×400×896 pixels) volume centered on the optic nerve head, with the focus readjusted after each scan. The LC was automatically segmented and analyzed for microarchitectural parameters, including pore diameter, pore diameter standard deviation (SD), pore aspect ratio, pore area, beam thickness, beam thickness SD, and beam thickness to pore diameter ratio. Reproducibility of the parameters was assessed by computing the imprecision of the parameters between the scans., Results: The automated segmentation demonstrated excellent reproducibility. All LC microarchitecture parameters had an imprecision of less or equal to 4.2%. There was little variability in imprecision with respect to diagnostic category, although the method tends to show higher imprecision amongst healthy subjects., Conclusion: The proposed automated segmentation of the LC demonstrated high reproducibility for 3D LC parameters. This segmentation analysis tool will be useful for in-vivo studies of the LC.

Purpose: To investigate gold nanorods (GNRs) as a contrast agent to enhance Doppler optical coherence tomography (OCT) imaging of the intrascleral aqueous humor outflow., Methods: A serial dilution of GNRs was scanned with a spectral-domain OCT device (Bioptigen, Durham, NC) to visualize Doppler signal. Doppler measurements using GNRs were validated using a controlled flow system. To demonstrate an application of GNR enhanced Doppler, porcine eyes were perfused at constant pressure with mock aqueous alone or 1.0×10(12) GNR/mL mixed with mock aqueous. Twelve Doppler and volumetric SD-OCT scans were obtained from the limbus in a radial fashion incremented by 30°, forming a circular scan pattern. Volumetric flow was computed by integrating flow inside non-connected vessels throughout all 12 scans around the limbus., Results: At the GNR concentration of 0.7×10(12) GNRs/mL, Doppler signal was present through the entire depth of the testing tube without substantial attenuation. A well-defined laminar flow profile was observed for Doppler images of GNRs flowing through the glass capillary tube. The Doppler OCT measured flow profile was not statistically different from the expected flow profile based upon an autoregressive moving average model, with an error of -0.025 to 0.037 mm/s (p = 0.6435). Cross-sectional slices demonstrated the ability to view anterior chamber outflow ex-vivo using GNR-enhanced Doppler OCT. Doppler volumetric flow measurements were comparable to flow recorded by the perfusion system., Conclusions: GNRs created a measureable Doppler signal within otherwise silent flow fields in OCT Doppler scans. Practical application of this technique was confirmed in a constant pressure ex-vivo aqueous humor outflow model in porcine eyes.

Importance: Monitoring young children with optic pathway gliomas (OPGs) for visual deterioration can be difficult owing to age-related noncompliance. Optical coherence tomography (OCT) measures of retinal nerve fiber layer (RNFL) thickness have been proposed as a surrogate marker of vision but this technique is also limited by patient cooperation., Objective: To determine whether measures of circumpapillary RNFL thickness, acquired with handheld OCT (HH-OCT) during sedation, can differentiate between young children with and without vision loss from OPGs., Design, Setting, and Participants: This cross-sectional analysis of a prospective observational study was conducted at a tertiary-care children's hospital. Children with an OPG (sporadic or secondary to neurofibromatosis type 1) who were cooperative for visual acuity testing, but required sedation to complete magnetic resonance imaging, underwent HH-OCT imaging of the circumpapillary RNFL while sedated., Main Outcomes and Measures: Area under the curve of the receiver operating characteristic, sensitivity, specificity, positive predictive value, and negative predictive value of the average and quadrant-specific RNFL thicknesses., Results: Thirty-three children (64 eyes) met inclusion criteria (median age, 4.8 years; range, 1.8-12.6 years). In children with vision loss (abnormal visual acuity and/or visual field), RNFL thickness was decreased in all quadrants compared with the normal-vision group (P < .001 for all comparisons). Using abnormal criteria of less than 5% and less than 1%, the area under the curve was highest for the average RNFL thickness (0.96 and 0.97, respectively) compared with specific anatomic quadrants. The highest discrimination and predictive values were demonstrated for participants with 2 or more quadrants meeting less than 5% (sensitivity = 93.3; specificity = 97.9; positive predictive value = 93.3; and negative predictive value = 97.9) and less than 1% (sensitivity = 93.3; specificity = 100; positive predictive value = 100; and negative predictive value = 98.0) criteria., Conclusions and Relevance: Measures of RNFL thickness acquired with HH-OCT during sedation can differentiate between young children with and without vision loss from OPGs. For young children who do not cooperate with vision testing, HH-OCT measures may be a surrogate marker of vision. Longitudinal studies are needed to delineate the temporal relationship between RNFL decline and vision loss.

Purpose: The lamina cribrosa (LC) is a prime location of glaucomatous damage. The purpose of this study was to compare LC 3-dimensional micro-architecture between healthy and glaucomatous eyes in vivo by using optical coherence tomography (OCT)., Methods: Sixty-eight eyes (19 healthy and 49 glaucomatous) from 47 subjects were scanned in a 3.5 × 3.5 × 3.64-mm volume (400 × 400 × 896 pixels) at the optic nerve head by using swept-source OCT. The LC micro-architecture parameters were measured on the visible LC by an automated segmentation algorithm. The LC parameters were compared to diagnosis and visual field mean deviation (VF MD) by using a linear mixed effects model accounting for age., Results: The average VF MD for the healthy and glaucomatous eyes was -0.50 ± 0.80 dB and -7.84 ± 8.75 dB, respectively. Beam thickness to pore diameter ratio (P = 0.04) and pore diameter standard deviation (P < 0.01) were increased in glaucomatous eyes. With worse MD, beam thickness to pore diameter ratio (P < 0.01), pore diameter standard deviation (P = 0.05), and beam thickness (P < 0.01) showed a statistically significant increase while pore diameter (P = 0.02) showed a significant decrease. There were no significant interactions between any of the parameters and age (all P > 0.05)., Conclusions: Glaucomatous micro-architecture changes in the LC, detected by OCT analysis, reflect beams remodeling and axonal loss leading to reduction in pore size and increased pore size variability.

Purpose: To test the effect of a novel signal normalization method for reducing systematic optical coherence tomography (OCT) measurement differences among multiple spectral-domain (SD) OCT devices., Methods: A total of 109 eyes from 59 subjects were scanned with two SD-OCT devices (Cirrus and RTVue) at the same visit. Optical coherence tomography image data were normalized to match their signal characteristics between the devices. To compensate signal strength differences, custom high dynamic range (HDR) processing was also applied only to images with substantially lower signal strength. Global mean peripapillary retinal nerve fiber layer (RNFL) thicknesses were then measured automatically from all images using custom segmentation software and were compared to the original device outputs. Structural equation models were used to analyze the absolute RNFL thickness difference between original device outputs and our software outputs after signal normalization., Results: The device-measured RNFL thickness showed a statistically significant difference between the two devices (mean absolute difference 10.58 μm, P < 0.05), while there was no significant difference after normalization on eyes with 62.4-μm or thicker RNFL (mean absolute difference 2.95 μm, P < 0.05)., Conclusions: The signal normalization method successfully reduces the systematic difference in RNFL thickness measurements between two SD-OCT devices. Enabling direct comparison of RNFL thickness obtained from multiple devices would broaden the use of OCT technology in both clinical and research applications.

Purpose: We developed a method to normalize optical coherence tomography (OCT) signal profiles from two spectral-domain (SD) OCT devices so that the comparability between devices increases., Methods: We scanned 21 eyes from 14 healthy and 7 glaucoma subjects with two SD-OCT devices on the same day, with equivalent cube scan patterns centered on the fovea (Cirrus HD-OCT and RTVue). Foveola positions were selected manually and used as the center for registration of the corresponding images. A-scan signals were sampled 1.8 mm from the foveola in the temporal, superior, nasal, and inferior quadrants. After oversampling and rescaling RTVue data along the Z-axis to match the corresponding Cirrus data format, speckle noise reduction and amplitude normalization were applied. For comparison between normalized A-scan profiles, mean absolute difference in amplitude in percentage was measured at each sampling point. As a reference, the mean absolute difference between two Cirrus scans on the same eye also was measured., Results: The mean residual of the A-scan profile amplitude was reduced significantly after signal normalization (12.7% vs. 6.2%, P < 0.0001, paired t-test). All four quadrants also showed statistically significant reduction (all P < 0.0001). Mean absolute difference after normalization was smaller than the one between two Cirrus scans. No performance difference was detected between health and glaucomatous eyes., Conclusions: The reported signal normalization method successfully reduced the A-scan profile differences between two SD-OCT devices. This signal normalization processing may improve the direct comparability of OCT image analysis and measurement on various devices.

Purpose: To develop and test a novel signal enhancement method for optical coherence tomography (OCT) images based on the high dynamic range (HDR) imaging concept., Methods: Three virtual channels, which represent low, medium, and high signal components, were produced for each OCT signal dataset. The dynamic range of each signal component was normalized to the full gray scale range. Finally, the three components were recombined into one image using various weights. Fourteen eyes of 14 healthy volunteers were scanned multiple times using time-domain (TD)-OCT before and while preventing blinking in order to produce a wide variety of signal strength (SS) images on the same eye scanned on the same day. For each eye, a pair of scans with the highest and lowest SS with successful retinal nerve fiber layer (RNFL) segmentation was selected to test the signal enhancement effect. In addition, spectral-domain (SD)-OCT images with poor signal qualities were also processed., Results: Mean SS of good and poor quality scans were 9.0 ± 1.1 and 4.4 ± 0.9, respectively. TD-OCT RNFL thickness showed significant differences between good and poor quality scans on the same eye (mean difference 11.9 ± 6.0 μm, P < 0.0001, paired t-test), while there was no significant difference after signal enhancement (1.7 ± 6.2 μm, P = 0.33). However, HDR had weaker RNFL compensation effect on images with SS less than or equal to 4, while it maintained good compensation effect on images with SS greater than 4. Successful signal enhancement was also confirmed subjectively on SD-OCT images., Conclusion: The HDR imaging successfully restored OCT signal and image quality and reduced RNFL thickness differences due to variable signal level to the level within the expected measurement variability. This technique can be applied to both TD- and SD-OCT images.

Purpose: To develop a new three-dimensional (3D) spectral-domain optical coherence tomography (SD-OCT) data analysis method using a machine learning technique based on variable-size super pixel segmentation that efficiently utilizes full 3D dataset to improve the discrimination between early glaucomatous and healthy eyes., Methods: 192 eyes of 96 subjects (44 healthy, 59 glaucoma suspect and 89 glaucomatous eyes) were scanned with SD-OCT. Each SD-OCT cube dataset was first converted into 2D feature map based on retinal nerve fiber layer (RNFL) segmentation and then divided into various number of super pixels. Unlike the conventional super pixel having a fixed number of points, this newly developed variable-size super pixel is defined as a cluster of homogeneous adjacent pixels with variable size, shape and number. Features of super pixel map were extracted and used as inputs to machine classifier (LogitBoost adaptive boosting) to automatically identify diseased eyes. For discriminating performance assessment, area under the curve (AUC) of the receiver operating characteristics of the machine classifier outputs were compared with the conventional circumpapillary RNFL (cpRNFL) thickness measurements., Results: The super pixel analysis showed statistically significantly higher AUC than the cpRNFL (0.855 vs. 0.707, respectively, p = 0.031, Jackknife test) when glaucoma suspects were discriminated from healthy, while no significant difference was found when confirmed glaucoma eyes were discriminated from healthy eyes., Conclusions: A novel 3D OCT analysis technique performed at least as well as the cpRNFL in glaucoma discrimination and even better at glaucoma suspect discrimination. This new method has the potential to improve early detection of glaucomatous damage.

Aims: To evaluate the glaucoma discriminating ability of macular retinal layers as measured by spectral domain optical coherence tomography (SD-OCT)., Methods: Healthy, glaucoma suspect and glaucomatous subjects had a comprehensive ocular examination, visual field testing and SD-OCT imaging (Cirrus HD-OCT; Carl Zeiss Meditec, Dublin, California, USA) in the macular and optic nerve head regions. OCT macular scans were segmented into macular nerve fibre layer (mNFL), ganglion cell layer with inner plexiform layer (GCIP), ganglion cell complex (GCC) (composed of mNFL and GCIP), outer retinal complex and total retina. 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: Analysis was performed on 51 healthy, 49 glaucoma suspect and 63 glaucomatous eyes. The median visual field MD was -2.21 dB (IQR: -6.92 to -0.35) for the glaucoma group, -0.32 dB (IQR: -1.22 to 0.73) for the suspect group and -0.18 dB (IQR: -0.92 to 0.71) for the healthy group. Highest age adjusted AUCs were found for average GCC and GCIP (AUC=0.901 and 0.900, respectively) and their sectoral measurements: infero-temporal (0.922 and 0.913), inferior (0.904 and 0.912) and supero-temporal (0.910 and 0.897). These values were similar to the discriminating ability of the mean cpRNFL (AUC=0.913). Comparison of these AUCs did not yield any statistically significant difference (all p>0.05)., Conclusions: SD-OCT GCIP and GCC measurements showed similar glaucoma diagnostic ability and were comparable with that of cpRNFL.

Purpose: We sought to visualize the aqueous outflow system in 3 dimensions (3D) in living human eyes, and to investigate the use of commercially available spectral-domain optical coherence tomographic (SD-OCT) systems for this purpose., Design: Prospective, observational study., Participants: One randomly determined eye in each of 6 normal healthy subjects was included., Testing: We performed 3D SD-OCT imaging of the aqueous humor outflow structures with 2 devices: The Cirrus HD-OCT and the Bioptigen SDOIS., Main Outcome Measures: We created 3D virtual castings of Schlemm's canal (SC) and more distal outflow structures from scan data from each device., Results: Virtual casting of the SC provided visualization of more aqueous vessels branching from SC than could be located by interrogating the 2-dimensional (2D) image stack. Similarly, virtual casting of distal structures allowed visualization of large and small aqueous outflow channel networks that could not be appreciated with conventional 2D visualization., Conclusions: The outflow pathways from SC to the superficial vasculature can be identified and tracked in living human eyes using commercially available SD-OCT., (Copyright © 2012 American Academy of Ophthalmology. Published by Elsevier Inc. All rights reserved.)

Eye movement artifacts occurring during 3-D optical coherence tomography (OCT) scanning is a well-recognized problem that may adversely affect image analysis and interpretation. A particle filtering algorithm is presented in this paper to correct motion in a 3-D dataset by considering eye movement as a target tracking problem in a dynamic system. The proposed particle filtering algorithm is an independent 3-D alignment approach, which does not rely on any reference image. 3-D OCT data is considered as a dynamic system, while the location of each A-scan is represented by the state space. A particle set is used to approximate the probability density of the state in the dynamic system. The state of the system is updated frame by frame to detect A-scan movement. The proposed method was applied on both simulated data for objective evaluation and experimental data for subjective evaluation. The sensitivity and specificity of the x-movement detection were 98.85% and 99.43%, respectively, in the simulated data. For the experimental data (74 3-D OCT images), all the images were improved after z-alignment, while 81.1% images were improved after x-alignment. The proposed algorithm is an efficient way to align 3-D OCT volume data and correct the eye movement without using references.

Purpose: We compared retinal nerve fiber layer (RNFL) bias and imprecision among three spectral-domain optical coherence tomographs (SD-OCT)., Methods: A total of 152 eyes of 83 subjects (96 healthy and 56 glaucomatous eyes) underwent peripapillary RNFL imaging using at least 2 of the following 3 SD-OCT devices on the same day: Cirrus HD-OCT (optic nerve head [ONH]) cube 200 × 200 protocol), RTVue-100 (ONH protocol [12 radial lines and 13 concentric circles]), and 3D OCT-1000 (3D Scan 256 × 256 protocol). Calibration equations, bias and imprecision of RNFL measurements were calculated using structural equation models., Results: The calibration equations for healthy and glaucoma RNFL thickness measurements among the 3 devices were: Cirrus = 2.136 + 0.831*RTVue; Cirrus = -15.521 + 1.056*3D OCT-1000; RTVue = -21.257 + 1.271*3D OCT-1000. Using Cirrus bias as an arbitrary reference, RTVue bias was 1.20 (95% CI 1.09-1.32, P < 0.05) times larger and 3D OCT-1000 was 0.95 (0.87-1.03, P > 0.05) times smaller. Relative to 3D OCT-1000, the RTVue bias was 1.27 (1.13-1.42, P < 0.05). RTVue imprecision (healthy eyes 7.83, 95% CI 6.43-9.58; glaucoma cases 5.71, 4.19-7.64) was statistically significantly higher than both Cirrus (healthy eyes 3.23, 2.11-4.31; glaucoma cases 3.53, 0.69-5.24) and 3D OCT-1000 (healthy eyes 4.07, 3.11-5.35; glaucoma cases 5.33, 3.77-7.67) in healthy eyes. The imprecision also was significantly higher for RTVue measurements in healthy compared to glaucomatous eyes. None of the other comparisons was statistically significant., Conclusions: RTVue-100 showed higher imprecision (or higher measurement variability) than Cirrus HD-OCT and 3D OCT-1000 RNFL measurements. Three-dimensional cube scanning with post-hoc data sampling may be a factor reducing imprecision.

Purpose: Commercial optical coherence tomography (OCT) systems use global signal quality indices to quantify scan quality. Signal quality can vary throughout a scan, contributing to local retinal nerve fibre layer segmentation errors (SegE). The purpose of this study was to develop an automated method, using local scan quality, to predict SegE., Methods: Good-quality (global signal strength (SS) ≥ 6; manufacturer specification) peripapillary circular OCT scans (fast retinal nerve fibre layer scan protocol; Stratus OCT; Carl Zeiss Meditec, Dublin, California, USA) were obtained from 6 healthy, 19 glaucoma-suspect and 43 glaucoma subjects. Scans were grouped based on SegE. Quality index (QI) values were computed for each A-scan using software of our own design. Logistic mixed-effects regression modelling was applied to evaluate SS, global mean and SD of QI, and the probability of SegE., Results: The difference between local mean QI in SegE regions and No-SegE regions was -5.06 (95% CI -6.38 to 3.734) (p<0.001). Using global mean QI, QI SD and their interaction term resulted in the model of best fit (Akaike information criterion=191.8) for predicting SegE. Global mean QI ≥ 20 or SS ≥ 8 shows little chance for SegE. Once mean QI<20 or SS<8, the probability of SegE increases as QI SD increases., Conclusions: When combined with a signal quality parameter, the variation of signal quality between A-scans provides significant information about the quality of an OCT scan and can be used as a predictor of segmentation error.

Aims: To determine the retinal nerve fibre layer (RNFL) thickness at which visual field (VF) damage becomes detectable and associated with structural loss., Methods: In a prospective cross-sectional study, 72 healthy and 40 glaucoma subjects (one eye per subject) recruited from an academic institution had VF examinations and spectral domain optical coherence tomography (SD-OCT) optic disc cube scans (Humphrey field analyser and Cirrus HD-OCT, respectively). Comparison of global mean and sectoral RNFL thicknesses with VF threshold values showed a plateau of threshold values at high RNFL thicknesses and a sharp decrease at lower RNFL thicknesses. A 'broken stick' statistical model was fitted to global and sectoral data to estimate the RNFL thickness 'tipping point' where the VF threshold values become associated with the structural measurements. The slope for the association between structure and function was computed for data above and below the tipping point., Results: The mean RNFL thickness threshold for VF loss was 75.3 μm (95% CI: 68.9 to 81.8), reflecting a 17.3% RNFL thickness loss from age-matched normative value. Above the tipping point, the slope for RNFL thickness and threshold value was 0.03 dB/μm (CI: -0.02 to 0.08) and below the tipping point, it was 0.28 dB/μm (CI: 0.18 to 0.38); the difference between the slopes was statistically significant (p<0.001). A similar pattern was observed for quadrant and clock-hour analysis., Conclusions: Substantial structural loss (∼17%) appears to be necessary for functional loss to be detectable using the current testing methods.

We develop an automated method to determine the foveola location in macular 3D-OCT images in either healthy or pathological conditions. Structural Support Vector Machine (S-SVM) is trained to directly predict the location of the foveola, such that the score at the ground truth position is higher than that at any other position by a margin scaling with the associated localization loss. This S-SVM formulation directly minimizes the empirical risk of localization error, and makes efficient use of all available training data. It deals with the localization problem in a more principled way compared to the conventional binary classifier learning that uses zero-one loss and random sampling of negative examples. A total of 170 scans were collected for the experiment. Our method localized 95.1% of testing scans within the anatomical area of the foveola. Our experimental results show that the proposed method can effectively identify the location of the foveola, facilitating diagnosis around this important landmark.

Purpose: To develop an automated method to identify the normal macula and three macular pathologies (macular hole [MH], macular edema [ME], and age-related macular degeneration [AMD]) from the fovea-centered cross sections in three-dimensional (3D) spectral-domain optical coherence tomography (SD-OCT) images., Methods: A sample of SD-OCT macular scans (macular cube 200 × 200 or 512 × 128 scan protocol; Cirrus HD-OCT; Carl Zeiss Meditec, Inc., Dublin, CA) was obtained from healthy subjects and subjects with MH, ME, and/or AMD (dataset for development: 326 scans from 136 subjects [193 eyes], and dataset for testing: 131 scans from 37 subjects [58 eyes]). A fovea-centered cross-sectional slice for each of the SD-OCT images was encoded using spatially distributed multiscale texture and shape features. Three ophthalmologists labeled each fovea-centered slice independently, and the majority opinion for each pathology was used as the ground truth. Machine learning algorithms were used to identify the discriminative features automatically. Two-class support vector machine classifiers were trained to identify the presence of normal macula and each of the three pathologies separately. The area under the receiver operating characteristic curve (AUC) was calculated to assess the performance., Results: The cross-validation AUC result on the development dataset was 0.976, 0.931, 0939, and 0.938, and the AUC result on the holdout testing set was 0.978, 0.969, 0.941, and 0.975, for identifying normal macula, MH, ME, and AMD, respectively., Conclusions: The proposed automated data-driven method successfully identified various macular pathologies (all AUC > 0.94). This method may effectively identify the discriminative features without relying on a potentially error-prone segmentation module.

We address a novel problem domain in the analysis of optical coherence tomography (OCT) images: the diagnosis of multiple macular pathologies in retinal OCT images. The goal is to identify the presence of normal macula and each of three types of macular pathologies, namely, macular edema, macular hole, and age-related macular degeneration, in the OCT slice centered at the fovea. We use a machine learning approach based on global image descriptors formed from a multi-scale spatial pyramid. Our local features are dimension-reduced local binary pattern histograms, which are capable of encoding texture and shape information in retinal OCT images and their edge maps, respectively. Our representation operates at multiple spatial scales and granularities, leading to robust performance. We use 2-class support vector machine classifiers to identify the presence of normal macula and each of the three pathologies. To further discriminate sub-types within a pathology, we also build a classifier to differentiate full-thickness holes from pseudo-holes within the macular hole category. We conduct extensive experiments on a large dataset of 326 OCT scans from 136 subjects. The results show that the proposed method is very effective (all AUC>0.93)., (Copyright © 2011 Elsevier B.V. All rights reserved.)

Aqueous humor (AH) exiting the eye via the trabecular meshwork and Schlemm's canal (SC) passes through the deep and intrascleral venous plexus (ISVP) or directly through aqueous veins. The purpose of this study was to visualize the human AH outflow system 360° in three dimensions (3D) during active AH outflow in a virtual casting. The conventional AH outflow pathways of 7 donor eyes were imaged with a modified Bioptigen spectral-domain optical coherence tomography system (Bioptigen Inc, USA; SuperLum LTD, Ireland) at a perfusion pressure of 20 mmHg (N = 3), and 10 mmHg (N = 4). In all eyes, 36 scans (3 equally distributed in each clock hour), each covering a 2 × 3 × 2 mm volume (512 frames, each 512 × 1024 pixels), were obtained. All image data were black/white inverted, and the background subtracted (ImageJ 1.40 g, http://rsb.info.nih.gov/ij/). Contrast was adjusted to isolate the ISVP. SC, collector channels, the deep and ISVP, and episcleral veins were observed throughout the limbus. Aqueous veins could be observed extending into the episcleral veins. Individual scan ISVP castings were rendered and assembled in 3D space in Amira 4.1 (Visage Imaging Inc. USA). A 360-degree casting of the ISVP was obtained in all perfused eyes. The ISVP tended to be dense and overlapping in the superior and inferior quadrants, and thinner in the lateral quadrants. The human AH outflow pathway can be imaged using SD-OCT. The more superficial structures of the AH outflow pathway present with sufficient contrast as to be optically isolated and cast in-situ 360° in cadaver eye perfusion models. This approach may be useful as a model in future studies of human AH outflow., (Copyright © 2011 Elsevier Ltd. All rights reserved.)

Detection of disease progression is an important and challenging component of glaucoma management. Optical coherence tomography (OCT) has proved to be valuable in the detection of glaucomatous damage. With its high resolution and proven measurement reproducibility, OCT has the potential to become an important tool for glaucoma progression detection. This manuscript presents the capabilities of the OCT technology pertinent for detection of progressive glaucomatous damage and provides a review of the current knowledge on the device's clinical performance., (Copyright 2011, SLACK Incorporated.)

Optical coherence tomography (OCT) imaging has become widespread in ophthalmology over the past 15 years, because of its ability to visualize ocular structures at high resolution. This article reviews the history of OCT imaging of the eye, its current status, and the laboratory work that is driving the future of the technology.

Purpose: To investigate the longitudinal effect of optic nerve crush injury in mice by measuring retinal thickness with spectral-domain optical coherence tomography (SD-OCT)., Methods: Optic nerves of one eye from each C57Bl/6 mouse were crushed under direct visualization for 3 seconds, 1 mm posterior to the globe. The optic nerve head (ONH) was imaged with SD-OCT (1.5 × 1.5 × 2.0 mm scan) before the surgical intervention and repeated subsequently for up to 32 days postinjury. A cohort of mice not exposed to the nerve crush procedure served as control. En face SD-OCT images were used to manually align subsequent scans to the baseline en face image. Total retinal thickness (TRT) (along a sampling band with radii 0.33-0.42 mm centered on the ONH) from each follow-up day was automatically quantified for global and sectoral measurements using custom software. Linear mixed-effects models with quadratic terms were fitted to compare TRT of nerve-crushed and control eyes over time., Results: Eleven eyes from 11 nerve crush mice (baseline age 76 ± 11.8 days) and eight eyes from four healthy mice (baseline age 64 ± 0 days) were included. The control eyes showed a small, gradual, and consistent TRT increase throughout follow-up. Nerve-crushed eyes showed an initial period of thickening, followed by thinning and slight rebound after day 21. The decrease in thickness observed after the early thickening resolved was statistically significantly different from the control eyes (P < 0.05 for global and sectoral measurements)., Conclusions: SD-OCT can be used to quantitatively monitor changes in retinal thickness in mice over time.

Current standard quantitative 3D spectral-domain optical coherence tomography (SD-OCT) analyses of various ocular diseases is limited in detecting structural damage at early pathologic stages. This is mostly because only a small fraction of the 3D data is used in the current method of quantifying the structure of interest. This paper presents a novel SD-OCT data analysis technique, taking full advantage of the 3D dataset. The proposed algorithm uses machine classifier to analyze SD-OCT images after grouping adjacent pixels into super pixel in order to detect glaucomatous damage. A 3D SD-OCT image is first converted into a 2D feature map and partitioned into over a hundred super pixels. Machine classifier analysis using boosting algorithm is performed on super pixel features. One hundred and ninety-two 3D OCT images of the optic nerve head region were tested. Area under the receiver operating characteristic (AUC) was computed to evaluate the glaucoma discrimination performance of the algorithm and compare it to the commercial software output. The AUC of normal vs glaucoma suspect eyes using the proposed method was statistically significantly higher than the current method (0.855 and 0.707, respectively, p=0.031). This new method has the potential to improve early detection of glaucomatous structural damages.

Purpose: To test the reproducibility of spectral-domain optical coherence tomography (SD-OCT) total retinal thickness (TRT) measurements in mice., Methods: C57Bl/6 mice were anesthetized, and three repeated volumetric images were acquired in both eyes with SD-OCT (250 A-scans × 250 frames × 1024 samplings), centered on the optic nerve head (ONH). The mice were repositioned between scans. TRT was automatically measured within a sampling band of retinal thickness with radii of 55 to 70 pixels, centered on the ONH by using custom segmentation software. The first volumetric image acquired in a given eye was used to register the remaining two SD-OCT images by manually aligning the en face images with respect to rotation and linear translation. Linear mixed-effects models were fitted to global and quadrant thicknesses, taking into account the clustering between eyes, to assess imprecision (measurement reproducibility)., Results: Twenty-six eyes of 13 adult mice (age 13 weeks) were imaged. The mean global TRT across all eyes was 298.21 μm, with a mouse heterogeneity standard deviation (SD) of 4.88 μm (coefficient of variation [CV] = 0.016), an eye SD of 3.32 μm (CV = 0.011), and a device-related imprecision SD of 2.33 μm (CV = 0.008). The superior quadrant had the thickest mean TRT measurement (310.38 μm) and the highest (worst) imprecision SD (3.13 μm; CV = 0.010), and the inferior quadrant had the thinnest mean TRT (291.55 μm). The quadrant with the lowest (best) imprecision SD was in the nasal one (2.06 μm; CV = 0.007)., Conclusions: Good reproducibility was observed for SD-OCT retinal thickness measurements in mice. SD-OCT may be useful for in vivo longitudinal studies in mice.

Three dimensional (3D) ophthalmic imaging using optical coherence tomography (OCT) has revolutionized assessment of the eye, the retina in particular. Recent technological improvements have made the acquisition of 3D-OCT datasets feasible. However, while volumetric data can improve disease diagnosis and follow-up, novel image analysis techniques are now necessary in order to process the dense 3D-OCT dataset. Fundamental software improvements include methods for correcting subject eye motion, segmenting structures or volumes of interest, extracting relevant data post hoc and signal averaging to improve delineation of retinal layers. In addition, innovative methods for image display, such as C-mode sectioning, provide a unique viewing perspective and may improve interpretation of OCT images of pathologic structures. While all of these methods are being developed, most remain in an immature state. This review describes the current status of 3D-OCT scanning and interpretation, and discusses the need for standardization of clinical protocols as well as the potential benefits of 3D-OCT scanning that could come when software methods for fully exploiting these rich datasets are available clinically. The implications of new image analysis approaches include improved reproducibility of measurements garnered from 3D-OCT, which may then help improve disease discrimination and progression detection. In addition, 3D-OCT offers the potential for preoperative surgical planning and intraoperative surgical guidance., (Copyright © 2010 Elsevier Ltd. All rights reserved.)

Background and Objective: Digital pathology has thus far focused on producing digital images of glass microscope slides. Spectral domain optical coherence tomography (SD-OCT) can be used to directly scan tissue blocks to produce three-dimensional histology images, potentially bypassing glass slide workflow., Materials and Methods: Formalin-fixed paraffin-embedded tissue blocks were scanned using SD-OCT and resulting images were compared with corresponding areas on microscope slides., Results: Low-magnification features were recognizable, including tissue outlines, fat, vessels, and outlines of colonic mucosal crypts. Subtle textures that were suggestive of benign breast lobules and ovarian tumor features were also visible. Initial SD-OCT images lacked resolution and contrast relative to traditional microscopy, but the image content suggests that additional features of interest are present and may be revealed with improved SD-OCT resolution and more post-processing experience. Elucidation of three-dimensional histology and pathology are also future tasks., Conclusion: Eventual availability of diagnostic-quality three-dimensional histology would have a profound impact on anatomic pathology., (Copyright 2010, SLACK Incorporated.)

Purpose: Measurements of human Schlemm's canal (SC) have been limited to histologic sections. The purpose of this study was to demonstrate noninvasive measurements of aqueous outflow (AO) structures in the human eye, examining regional variation in cross-sectional SC areas (on/off collector channel [CC] ostia [SC/CC] and nasal/temporal) in the eyes of living humans., Methods: SC was imaged by spectral-domain optical coherence tomography with a 200-nm bandwidth light source. Both eyes of 21 healthy subjects and one glaucomatous eye of three subjects were imaged nasally and temporally. Contrast and magnification were adjusted to maximize visualization. Cross-sectional SC on and off SC/CC was traced three times by two independent masked observers using ImageJ (ImageJ 1.40g, http://rsb.info.nih.gov/ij/ Wayne Rasband, developer, National Institutes of Health, Bethesda, MD). The mean SC area was recorded. A linear mixed-effects model was used to analyze eye, nasal/temporal laterality, and SC area on or off SC/CC., Results: SC area was significantly larger on SC/CCs than off (12,890 vs. 7,391 micorm(2), P < 0.0001) and was significantly larger on the nasal side than on the temporal (10,983 vs. 8,308 micorm(2), P = 0.009). SC areas were significantly smaller in glaucoma patients than in normal subjects, whether pooled (P = 0.0073) or grouped by on (P = 0.0215) or off (P = 0.0114) SC/CC., Conclusions: Aqueous outflow structures, including SC and CCs, can be noninvasively assessed in the human eye. These measurements will be useful in physiological studies of AO and will be clinically useful in the determination of the impact of glaucoma therapies on IOP as well as presurgical planning.

Purpose: Time domain optical coherence tomography (TD-OCT) has been used commonly in clinical practice, producing a large inventory of circular scan data for retinal nerve fiber layer (RNFL) assessment. Spectral domain (SD)-OCT produces three-dimensional (3-D) data volumes. The purpose of this study was to create a robust technique that makes TD-OCT circular scan RNFL thickness measurements comparable with those from 3-D SD-OCT volumes., Methods: Eleven eyes of 11 healthy subjects and 7 eyes of 7 subjects with glaucoma were enrolled. Each eye was scanned with one centered and eight displaced TD-OCT scanning circles. One 3-D SD-OCT cube scan was obtained at the same visit. The matching location of the TD-OCT scanning circle was automatically detected within the corresponding 3-D SD-OCT scan. Algorithm performance was assessed by estimating the difference between the detected scanning circle location on 3-D SD-OCT volume and the TD-OCT circle location. Global and sectoral RNFL thickness measurement errors between the two devices were also compared., Results: The difference (95% confidence interval) in scanning circle center locations between TD- and SD-OCT was 2.3 (1.5-3.2) pixels (69.0 [45.0-96.0] microm on the retina) for healthy eyes and 3.1 (2.0-4.1) pixels (93.0 [60.0-123.0] microm on the retina) for glaucomatous eyes. The absolute RNFL thickness measurement difference was significantly smaller with the matched scanning circle., Conclusions: Scan location matching may bridge the gap in RNFL thickness measurements between TD-OCT circular scan data and 3-D SD-OCT scan data, providing follow-up comparability across the two generations of OCTs.