Intraretinal layer segmentation of macular optical coherence tomography images using optimal 3-D graph search

Choroidal Layer Analysis in OCT images via Ambiguous Boundary-aware Attention

Optical Coherence Tomography (OCT) is a commonly used retina imaging technique, and it is capable of revealing the morphology of the choroid. However, the segmentation and quantitative analysis of the sublayers and vessels in choroid are rarely explored, primarily due to the indistinct boundaries of choroidal sublayers, and imbalanced distribution of vessels observed in OCT imagery. In this paper, we propose a novel two-stage architecture called Choroidal Layer Analysis network (CLA), that may be considered the first attempt in this research community for joint segmentation of choroidal sublayers and choroidal vessels in OCT images. CLA employs the encoder-decoder network with the residual U-shape module as the backbone. In order to empower the ability of the segmentation model to identify the inconspicuous boundaries of choroidal sublayers, we introduce an Ambiguous Boundary Attention block (ABA) into the bottleneck of the encoder-decoder network in the first stage. For more accurate segmentation of large choroidal vessels with ambiguous contours and imbalanced spatial distribution, the second stage introduces an active contour-based loss to refine the contours of choroidal vessels simultaneously with precise identification of each vessel via contextual modeling. To train, test and validate the proposed model, we conducted a choroidal segmentation dataset containing 800 OCT images, with their sublayers and large choroidal vessels manually annotated. Experimental results demonstrate the superiority of the proposed approach compared with other state-of-the-art segmentation networks in large margins. It is worth noting that we also reconstructed the large choroidal vessels in three-dimensional (3D) based on the segmentation results, and multiple 3D morphological parameters were calculated. The statistical analysis of these parameters demonstrates significant differences between the healthy control and high myopia group, and this further confirms the proposed work may facilitate subsequent disease understanding and clinical decision-making.

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

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

Graph Attention U-Net for Retinal Layer Surface Detection and Choroid Neovascularization Segmentation in OCT Images

Choroidal neovascularization (CNV) is a typical symptom of age-related macular degeneration (AMD) and is one of the leading causes for blindness. Accurate segmentation of CNV and detection of retinal layers are critical for eye disease diagnosis and monitoring. In this paper, we propose a novel graph attention U-Net (GA-UNet) for retinal layer surface detection and CNV segmentation in optical coherence tomography (OCT) images. Due to retinal layer deformation caused by CNV, it is challenging for existing models to segment CNV and detect retinal layer surfaces with the correct topological order. We propose two novel modules to address the challenge. The first module is a graph attention encoder (GAE) in a U-Net model that automatically integrates topological and pathological knowledge of retinal layers into the U-Net structure to achieve effective feature embedding. The second module is a graph decorrelation module (GDM) that takes reconstructed features by the decoder of the U-Net as inputs, it then decorrelates and removes information unrelated to retinal layer for improved retinal layer surface detection. In addition, we propose a new loss function to maintain the correct topological order of retinal layers and the continuity of their boundaries. The proposed model learns graph attention maps automatically during training and performs retinal layer surface detection and CNV segmentation simultaneously with the attention maps during inference. We evaluated the proposed model on our private AMD dataset and another public dataset. Experiment results show that the proposed model outperformed the competing methods for retinal layer surface detection and CNV segmentation and achieved new state of the arts on the datasets.

A Systematic Review on Retinal Biomarkers to Diagnose Dementia from OCT/OCTA Images

Background

Traditional methods for diagnosing dementia are costly, time-consuming, and somewhat invasive. Since the retina shares significant anatomical similarities with the brain, retinal abnormalities detected via optical coherence tomography (OCT) and OCT angiography (OCTA) have been studied as a potential non-invasive diagnostic tool for neurodegenerative disorders; however, the most effective retinal changes remain a mystery to be unraveled in this review.

Objective

This study aims to explore the relationship between retinal abnormalities in OCT/OCTA images and cognitive decline as well as evaluating biomarkers' effectiveness in detecting neurodegenerative diseases.

Methods

A systematic search was conducted on PubMed, Web of Science, and Scopus until December 2022, resulted in 64 papers using agreed search keywords, and inclusion/exclusion criteria.

Results

The superior peripapillary retinal nerve fiber layer (pRNFL) is a trustworthy biomarker to identify most Alzheimer's disease (AD) cases; however, it is inefficient when dealing with mild AD and mild cognitive impairment (MCI). The global pRNFL (pRNFL-G) is another reliable biomarker to discriminate frontotemporal dementia from mild AD and healthy controls (HCs), moderate AD and MCI from HCs, as well as identifing pathological Aβ42/tau in cognitively healthy individuals. Conversely, pRNFL-G fails to realize mild AD and the progression of AD. The average pRNFL thickness variation is considered a viable biomarker to monitor the progression of AD. Finally, the superior and average pRNFL thicknesses are considered consistent for advanced AD but not for early/mild AD.

Conclusions

Retinal changes may indicate dementia, but further research is needed to confirm the most effective biomarkers for early and mild AD.

Segmentation of paracentral acute middle maculopathy lesions in spectral-domain optical coherence tomography images through weakly supervised deep convolutional networks

BACKGROUND AND OBJECTIVES

Spectral-domain optical coherence tomography (SD-OCT) is a valuable tool for non-invasive imaging of the retina, allowing the discovery and visualization of localized lesions, the presence of which is associated with eye diseases. The present study introduces X-Net, a weakly supervised deep-learning framework for automated segmentation of paracentral acute middle maculopathy (PAMM) lesions in retinal SD-OCT images. Despite recent advances in the development of automatic methods for clinical analysis of OCT scans, there remains a scarcity of studies focusing on the automated detection of small retinal focal lesions. Additionally, most existing solutions depend on supervised learning, which can be time-consuming and require extensive image labeling, whereas X-Net offers a solution to these challenges. As far as we can determine, no prior study has addressed the segmentation of PAMM lesions in SD-OCT images.

METHODS

This study leverages 133 SD-OCT retinal images, each containing instances of paracentral acute middle maculopathy lesions. A team of eye experts annotated the PAMM lesions in these images using bounding boxes. Then, labeled data were used to train a U-Net that performs pre-segmentation, producing region labels of pixel-level accuracy. To attain a highly-accurate final segmentation, we introduced X-Net, a novel neural network made up of a master and a slave U-Net. During training, it takes the expert annotated, and pixel-level pre-segment annotated images and employs sophisticated strategies to ensure the highest segmentation accuracy.

RESULTS

The proposed method was rigorously evaluated on clinical retinal images excluded from training and achieved an accuracy of 99% with a high level of similarity between the automatic segmentation and expert annotation, as demonstrated by a mean Intersection-over-Union of 0.8. Alternative methods were tested on the same data. Single-stage neural networks proved insufficient for achieving satisfactory results, confirming that more advanced solutions, such as the proposed method, are necessary. We also found that X-Net using Attention U-net for both the pre-segmentation and X-Net arms for the final segmentation shows comparable performance to the proposed method, suggesting that the proposed approach remains a viable solution even when implemented with variants of the classic U-Net.

CONCLUSIONS

The proposed method exhibits reasonably high performance, validated through quantitative and qualitative evaluations. Medical eye specialists have also verified its validity and accuracy. Thus, it could be a viable tool in the clinical assessment of the retina. Additionally, the demonstrated approach for annotating the training set has proven to be effective in reducing the expert workload.

Deep learning network with differentiable dynamic programming for retina OCT surface segmentation

Multiple-surface segmentation in optical coherence tomography (OCT) images is a challenging problem, further complicated by the frequent presence of weak image boundaries. Recently, many deep learning-based methods have been developed for this task and yield remarkable performance. Unfortunately, due to the scarcity of training data in medical imaging, it is challenging for deep learning networks to learn the global structure of the target surfaces, including surface smoothness. To bridge this gap, this study proposes to seamlessly unify a U-Net for feature learning with a constrained differentiable dynamic programming module to achieve end-to-end learning for retina OCT surface segmentation to explicitly enforce surface smoothness. It effectively utilizes the feedback from the downstream model optimization module to guide feature learning, yielding better enforcement of global structures of the target surfaces. Experiments on Duke AMD (age-related macular degeneration) and JHU MS (multiple sclerosis) OCT data sets for retinal layer segmentation demonstrated that the proposed method was able to achieve subvoxel accuracy on both datasets, with the mean absolute surface distance (MASD) errors of 1.88 ± 1.96μm and 2.75 ± 0.94μm, respectively, over all the segmented surfaces.

Annotation-efficient learning for OCT segmentation

Deep learning has been successfully applied to OCT segmentation. However, for data from different manufacturers and imaging protocols, and for different regions of interest (ROIs), it requires laborious and time-consuming data annotation and training, which is undesirable in many scenarios, such as surgical navigation and multi-center clinical trials. Here we propose an annotation-efficient learning method for OCT segmentation that could significantly reduce annotation costs. Leveraging self-supervised generative learning, we train a Transformer-based model to learn the OCT imagery. Then we connect the trained Transformer-based encoder to a CNN-based decoder, to learn the dense pixel-wise prediction in OCT segmentation. These training phases use open-access data and thus incur no annotation costs, and the pre-trained model can be adapted to different data and ROIs without re-training. Based on the greedy approximation for the k-center problem, we also introduce an algorithm for the selective annotation of the target data. We verified our method on publicly-available and private OCT datasets. Compared to the widely-used U-Net model with 100% training data, our method only requires ∼10% of the data for achieving the same segmentation accuracy, and it speeds the training up to ∼3.5 times. Furthermore, our proposed method outperforms other potential strategies that could improve annotation efficiency. We think this emphasis on learning efficiency may help improve the intelligence and application penetration of OCT-based technologies.

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

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

A Review of Machine Learning Algorithms for Retinal Cyst Segmentation on Optical Coherence Tomography

Optical coherence tomography (OCT) is an emerging imaging technique for diagnosing ophthalmic diseases and the visual analysis of retinal structure changes, such as exudates, cysts, and fluid. In recent years, researchers have increasingly focused on applying machine learning algorithms, including classical machine learning and deep learning methods, to automate retinal cysts/fluid segmentation. These automated techniques can provide ophthalmologists with valuable tools for improved interpretation and quantification of retinal features, leading to more accurate diagnosis and informed treatment decisions for retinal diseases. This review summarized the state-of-the-art algorithms for the three essential steps of cyst/fluid segmentation: image denoising, layer segmentation, and cyst/fluid segmentation, while emphasizing the significance of machine learning techniques. Additionally, we provided a summary of the publicly available OCT datasets for cyst/fluid segmentation. Furthermore, the challenges, opportunities, and future directions of artificial intelligence (AI) in OCT cyst segmentation are discussed. This review is intended to summarize the key parameters for the development of a cyst/fluid segmentation system and the design of novel segmentation algorithms and has the potential to serve as a valuable resource for imaging researchers in the development of assessment systems related to ocular diseases exhibiting cyst/fluid in OCT imaging.

Deep semi-supervised multiple instance learning with self-correction for DME classification from OCT images

Supervised deep learning has achieved prominent success in various diabetic macular edema (DME) recognition tasks from optical coherence tomography (OCT) volumetric images. A common problematic issue that frequently occurs in this field is the shortage of labeled data due to the expensive fine-grained annotations, which increases substantial difficulty in accurate analysis by supervised learning. The morphological changes in the retina caused by DME might be distributed sparsely in B-scan images of the OCT volume, and OCT data is often coarsely labeled at the volume level. Hence, the DME identification task can be formulated as a multiple instance classification problem that could be addressed by multiple instance learning (MIL) techniques. Nevertheless, none of previous studies utilize unlabeled data simultaneously to promote the classification accuracy, which is particularly significant for a high quality of analysis at the minimum annotation cost. To this end, we present a novel deep semi-supervised multiple instance learning framework to explore the feasibility of leveraging a small amount of coarsely labeled data and a large amount of unlabeled data to tackle this problem. Specifically, we come up with several modules to further improve the performance according to the availability and granularity of their labels. To warm up the training, we propagate the bag labels to the corresponding instances as the supervision of training, and propose a self-correction strategy to handle the label noise in the positive bags. This strategy is based on confidence-based pseudo-labeling with consistency regularization. The model uses its prediction to generate the pseudo-label for each weakly augmented input only if it is highly confident about the prediction, which is subsequently used to supervise the same input in a strongly augmented version. This learning scheme is also applicable to unlabeled data. To enhance the discrimination capability of the model, we introduce the Student-Teacher architecture and impose consistency constraints between two models. For demonstration, the proposed approach was evaluated on two large-scale DME OCT image datasets. Extensive results indicate that the proposed method improves DME classification with the incorporation of unlabeled data and outperforms competing MIL methods significantly, which confirm the feasibility of deep semi-supervised multiple instance learning at a low annotation cost.

Automatic segmentation of hyperreflective dots via focal priors and visual saliency

PURPOSE

Hyperreflective dots (HRDs) can be observed in spectral domain optical coherence tomography (SD-OCT), which can provide a sensitive marker in the treatment decision process. Quantitative analyses of HRDs are the key to make appropriate decisions on observation, treatment, and retreatment. The purpose of this study is to automatically and accurately segment HRDs in SD-OCT B-scans with diabetic retinopathy (DR).

METHODS

The authors propose an automatic segmentation algorithm of HRDs via focal priors and visual saliency. The algorithm is divided into three stages: segmentation of retinal layers, calculation of the multiscale local contrast saliency map, and adaptive threshold segmentation. First, a method based on improved graph search is used to segment retinal layers to obtain the region of interest (ROI) and the reflectivity estimation of the retinal pigment epithelium (RPE) layer; then, the multiscale local contrast saliency map is obtained by using a local contrast measure, which measures the dissimilarity between the current pixels and corresponding neighborhoods; finally, an adaptive threshold is applied to segment HRDs.

RESULTS

Experimental results on 20 SD-OCT B-scans demonstrate that our method is effective for HRDs segmentation. The average dice similarity coefficient (DSC) and detection accuracy are 71.12% and 85.07%, respectively.

CONCLUSIONS

The proposed method can accurately segment HRDs in SD-OCT B-scans with DR and outperforms current state-of-the-art methods. Our method can provide reliable HRDs segmentation to assist ophthalmologists in clinical diagnosis, treatment, disease monitoring, and progression.

Automatic Segmentation and Measurement of Choroid Layer in High Myopia for OCT Imaging Using Deep Learning

Automatic segmentation and measurement of the choroid layer is useful in studying of related fundus diseases, such as diabetic retinopathy and high myopia. However, most algorithms are not helpful for choroid layer segmentation due to its blurred boundaries and complex gradients. Therefore, this paper aimed to propose a novel choroid segmentation method that combines image enhancement and attention-based dense (AD) U-Net network. The choroidal images obtained from optical coherence tomography (OCT) are pre-enhanced by algorithms that include flattening, filtering, and exponential and linear enhancement to reduce choroid-independent information. Experimental results obtained from 800 OCT B-scans of the choroid layers from both normal eyes and high myopia showed that image enhancement significantly increased the performance of ADU-Net, with an AUC of 99.51% and a DSC of 97.91%. The accuracy of segmentation using the ADU-Net method with image enhancement is superior to that of the existing networks. In addition, we describe some algorithms that can measure automatically choroidal foveal thickness and the volume of adjacent areas. Statistical analyses of the choroidal parameters variation indicated that compared with normal eyes, high myopia has a reduction of 86.3% of the choroidal foveal thickness and 90% of the adjacent volume. It proved that high myopia is likely to cause choroid layer attenuation. These algorithms would have wide application in the diagnosis and precaution of related fundus lesions caused by choroid thinning from high myopia in future studies.

JOINT MOTION CORRECTION AND 3D SEGMENTATION WITH GRAPH-ASSISTED NEURAL NETWORKS FOR RETINAL OCT

Optical Coherence Tomography (OCT) is a widely used non-invasive high resolution 3D imaging technique for biological tissues and plays an important role in ophthalmology. OCT retinal layer segmentation is a fundamental image processing step for OCT-Angiography projection, and disease analysis. A major problem in retinal imaging is the motion artifacts introduced by involuntary eye movements. In this paper, we propose neural networks that jointly correct eye motion and retinal layer segmentation utilizing 3D OCT information, so that the segmentation among neighboring B-scans would be consistent. The experimental results show both visual and quantitative improvements by combining motion correction and 3D OCT layer segmentation comparing to conventional and deep-learning based 2D OCT layer segmentation.

Synergistically segmenting choroidal layer and vessel using deep learning for choroid structure analysis

Objective. The choroid is the most vascularized structure in the human eye, whose layer structure and vessel distribution are both critical for the physiology of the retina, and disease pathogenesis of the eye. Although some works have used graph-based methods or convolutional neural networks to separate the choroid layer from the outer-choroid structure, few works focused on further distinguishing the inner-choroid structure, such as the choroid vessel and choroid stroma. Approach. Inspired by the multi-task learning strategy, in this paper, we propose a segmentation pipeline for choroid analysis which can separate the choroid layer from other structures and segment the choroid vessel synergistically. The key component of this pipeline is the proposed choroidal U-shape network (CUNet), which catches both correlation features and specific features between the choroid layer and the choroid vessel. Then pixel-wise classification is generated based on these two types of features to obtain choroid layer segmentation and vessel segmentation. Besides, the training process of CUNet is supervised by a proposed adaptive multi-task segmentation loss which adds a regularization term that is used to balance the performance of the two tasks. Main results. Experiments show the high performance (4% higher dice score) and less computational complexity (18.85 M lower size) of our proposed strategy. Significance. The high performance and generalization on both choroid layer and vessel segmentation indicate the clinical potential of our proposed pipeline.

Automated retinal boundary segmentation of optical coherence tomography images using an improved Canny operator

Retinal segmentation is a prerequisite for quantifying retinal structural features and diagnosing related ophthalmic diseases. Canny operator is recognized as the best boundary detection operator so far, and is often used to obtain the initial boundary of the retina in retinal segmentation. However, the traditional Canny operator is susceptible to vascular shadows, vitreous artifacts, or noise interference in retinal segmentation, causing serious misdetection or missed detection. This paper proposed an improved Canny operator for automatic segmentation of retinal boundaries. The improved algorithm solves the problems of the traditional Canny operator by adding a multi-point boundary search step on the basis of the original method, and adjusts the convolution kernel. The algorithm was used to segment the retinal images of healthy subjects and age-related macular degeneration (AMD) patients; eleven retinal boundaries were identified and compared with the results of manual segmentation by the ophthalmologists. The average difference between the automatic and manual methods is: 2–6 microns (1–2 pixels) for healthy subjects and 3–10 microns (1–3 pixels) for AMD patients. Qualitative method is also used to verify the accuracy and stability of the algorithm. The percentage of “perfect segmentation” and “good segmentation” is 98% in healthy subjects and 94% in AMD patients. This algorithm can be used alone or in combination with other methods as an initial boundary detection algorithm. It is easy to understand and improve, and may become a useful tool for analyzing and diagnosing eye diseases.

Globally optimal OCT surface segmentation using a constrained IPM optimization

Segmentation of multiple surfaces in optical coherence tomography (OCT) images is a challenging problem, further complicated by the frequent presence of weak boundaries, varying layer thicknesses, and mutual influence between adjacent surfaces. The traditional graph-based optimal surface segmentation method has proven its effectiveness with its ability to capture various surface priors in a uniform graph model. However, its efficacy heavily relies on handcrafted features that are used to define the surface cost for the "goodness" of a surface. Recently, deep learning (DL) is emerging as a powerful tool for medical image segmentation thanks to its superior feature learning capability. Unfortunately, due to the scarcity of training data in medical imaging, it is nontrivial for DL networks to implicitly learn the global structure of the target surfaces, including surface interactions. This study proposes to parameterize the surface cost functions in the graph model and leverage DL to learn those parameters. The multiple optimal surfaces are then simultaneously detected by minimizing the total surface cost while explicitly enforcing the mutual surface interaction constraints. The optimization problem is solved by the primal-dual interior-point method (IPM), which can be implemented by a layer of neural networks, enabling efficient end-to-end training of the whole network. Experiments on spectral-domain optical coherence tomography (SD-OCT) retinal layer segmentation demonstrated promising segmentation results with sub-pixel accuracy.

Utility of Spectral-Domain Optical Coherence Tomography in Differentiating Papilledema From Pseudopapilledema: A Prospective Longitudinal Study

BACKGROUND

Prospective and longitudinal studies assessing the utility of spectral-domain optical coherence tomography (SD-OCT) to differentiate papilledema from pseudopapilledema are lacking. We studied the sensitivity and specificity of baseline and longitudinal changes in SD-OCT parameters with 3D segmentation software to distinguish between papilledema and pseudopapilledema in a cohort of patients referred for evaluation of undiagnosed optic disc elevation.

METHODS

Fifty-two adult patients with optic disc elevation were enrolled in a prospective longitudinal study. A diagnosis of papilledema was made when there was a change in the appearance of the optic disc elevation on fundus photographs as noted by an independent observer at or before 6 months. The degree of optic disc elevation was graded using the Frisen scale and patients with mild optic disc elevation (Frisen grades 1 and 2) were separately analyzed. SD-OCT parameters including peripapillary retinal nerve fiber layer (pRNFL), total retinal thickness (TRT), paracentral ganglion cell layer-inner plexiform layer (GCL-IPL) thickness, and optic nerve head volume (ONHV) at baseline and within 6 months of follow-up were measured.

RESULTS

Twenty-seven (52%) patients were diagnosed with papilledema and 25 (48%) with pseudopapilledema. Among patients with mild optic disc elevation (Frisen grades 1 and 2), baseline pRNFL (110.1 µm vs 151.3 µm) and change in pRNFL (ΔpRNFL) (7.3 µm vs 52.3 µm) were greater among those with papilledema. Baseline and absolute changes in TRT and ONHV were also significantly higher among patients with papilledema. The mean GCL-IPL thickness was similar at baseline, but there was a small reduction in GCL-IPL thickness among patients with papilledema. Receiver operator curves (ROCs) were generated; ΔpRNFL (0.93), ΔTRT (0.94), and ΔONHV (0.95) had the highest area under the curve (AUC).

CONCLUSIONS

The mean baseline and absolute changes in SD-OCT measurements (pRFNL, TRT, and ONHV) were significantly greater among patients with papilledema, and remained significantly greater when patients with mild optic disc elevation were separately analyzed. ROCs demonstrated that ΔpRNFL, ΔTRT, and ΔONHV have the highest AUC and are best able to differentiate between papilledema and pseudopapilledema.

Segmentation of Preretinal Space in Optical Coherence Tomography Images Using Deep Neural Networks

This paper proposes an efficient segmentation of the preretinal area between the inner limiting membrane (ILM) and posterior cortical vitreous (PCV) of the human eye in an image obtained with the use of optical coherence tomography (OCT). The research was carried out using a database of three-dimensional OCT imaging scans obtained with the Optovue RTVue XR Avanti device. Various types of neural networks (UNet, Attention UNet, ReLayNet, LFUNet) were tested for semantic segmentation, their effectiveness was assessed using the Dice coefficient and compared to the graph theory techniques. Improvement in segmentation efficiency was achieved through the use of relative distance maps. We also show that selecting a larger kernel size for convolutional layers can improve segmentation quality depending on the neural network model. In the case of PVC, we obtain the effectiveness reaching up to 96.35%. The proposed solution can be widely used to diagnose vitreomacular traction changes, which is not yet available in scientific or commercial OCT imaging solutions.

Detection of Longitudinal Ganglion Cell/Inner Plexiform Layer Change: Comparison of Two Spectral-Domain Optical Coherence Tomography Devices

PURPOSE

We compared rates of change of macular ganglion cell/inner plexiform (GCIPL) thickness and proportion of worsening and improving rates from 2 optical coherence tomography (OCT) devices in a cohort of eyes with glaucoma.

DESIGN

Longitudinal cohort study.

METHODS

In a tertiary glaucoma clinic we evaluated 68 glaucoma eyes with ≥2 years of follow-up and ≥4 OCT images. Macular volume scans from 2 OCT devices were exported, coregistered, and segmented. Global and sectoral GCIPL data from the central 4.8 × 4.0-mm region were extracted. GCIPL rates of change were estimated with linear regression. Permutation analyses were used to control specificity with the 2.5 percentile cutoff point used to define "true" worsening. Main outcome measures included differences in global/sectoral GCIPL rates of change between 2 OCT devices and the proportion of negative vs positive rates of change (P < .05).

RESULTS

Average (standard deviation) 24-2 visual field mean deviation, median (interquartile range) follow-up time, and number of OCT images were -9.4 (6.1) dB, 3.8 (3.3-4.2) years, and 6 (5-8), respectively. GCIPL rates of thinning from Spectralis OCT were faster (more negative) compared with Cirrus OCT; differences were significant in superonasal (P = .03) and superotemporal (P = .04) sectors. A higher proportion of significant negative rates was observed with Spectralis OCT both globally and in inferotemporal/superotemporal sectors (P < .04). Permutation analyses confirmed the higher proportion of global and sectoral negative rates of change with Spectralis OCT (P < .001).

CONCLUSIONS

Changes in macular GCIPL were detected more frequently on Spectralis' longitudinal volume scans than those of Cirrus OCT. OCT devices are not interchangeable with regard to detection of macular structural progression.

Artificial intelligence in OCT angiography

Optical coherence tomographic angiography (OCTA) is a non-invasive imaging modality that provides three-dimensional, information-rich vascular images. With numerous studies demonstrating unique capabilities in biomarker quantification, diagnosis, and monitoring, OCTA technology has seen rapid adoption in research and clinical settings. The value of OCTA imaging is significantly enhanced by image analysis tools that provide rapid and accurate quantification of vascular features and pathology. Today, the most powerful image analysis methods are based on artificial intelligence (AI). While AI encompasses a large variety of techniques, machine-learning-based, and especially deep-learning-based, image analysis provides accurate measurements in a variety of contexts, including different diseases and regions of the eye. Here, we discuss the principles of both OCTA and AI that make their combination capable of answering new questions. We also review contemporary applications of AI in OCTA, which include accurate detection of pathologies such as choroidal neovascularization, precise quantification of retinal perfusion, and reliable disease diagnosis.

Topographic Distribution and Progression of Soft Drusen Volume in Age-Related Macular Degeneration Implicate Neurobiology of Fovea

Purpose

To refine estimates of macular soft drusen abundance in eyes with age-related macular degeneration (AMD) and evaluate hypotheses about drusen biogenesis, we investigated topographic distribution and growth rates of drusen by optical coherence tomography (OCT). We compared results to retinal features with similar topographies (cone density and macular pigment) in healthy eyes.

Methods

In a prospective study, distribution and growth rates of soft drusen in eyes with AMD were identified by human observers in OCT volumes and analyzed with computer-assistance. Published histologic data for macular cone densities (n = 12 eyes) and in vivo macular pigment optical density (MPOD) measurements in older adults with unremarkable maculae (n = 31; 62 paired eyes, averaged) were revisited. All values were normalized to Early Treatment Diabetic Retinopathy Study (ETDRS) subfield areas.

Results

Sixty-two eyes of 44 patients were imaged for periods up to 78 months. Soft drusen volume per unit volume at baseline is 24.6-fold and 2.3-fold higher in the central ETDRS subfield than in outer and inner rings, respectively, and grows most prominently there. Corresponding ratios (central versus inner and central versus outer) for cone density in donor eyes is 13.3-fold and 5.1-fold and for MPOD, 24.6 and 23.9-fold, and 3.6 and 3.6-fold.

Conclusions

Normalized soft drusen volume in AMD eyes as assessed by OCT is ≥ 20-fold higher in central ETDRS subfields than in outer rings, paralleling MPOD distribution in healthy eyes. Data on drusen volume support this metric for AMD risk assessment and clinical trial outcome measure. Alignment of different data modalities support the ETDRS grid for standardizing retinal topography in mechanistic studies of drusen biogenesis.

Optical Coherence Tomography Neuro-Toolbox for the Diagnosis and Management of Papilledema, Optic Disc Edema, and Pseudopapilledema

BACKGROUND

Distinguishing optic disc edema from pseudopapilledema is a common, sometimes challenging clinical problem. Advances in spectral-domain optical coherence tomography (SD-OCT) of the optic nerve head (ONH) has proven to be a cost effective, noninvasive, outpatient procedure that may help. At its core are tools that quantify the thickness of the retinal nerve fiber layer (RNFL) and ganglion cell-inner plexiform layer (GC-IPL). The SD-OCT also provides a set of tools that may be qualitatively interpreted in the same way that we read an MRI. They include the transverse axial, en face, and circular tomogram. Our goal is to describe a practical office-based set of tools using SD-OCT in the diagnosis and monitoring of papilledema, optic disc edema, and pseudopapilledema.

EVIDENCE ACQUISITION

Searches on PubMed were performed using combinations of the following key words: OCT, papilledema, pseudopapilledema, optic disc drusen, retinal folds (RF), and choroidal folds (CF).

RESULTS

The principal elements of SD-OCT analysis of the ONH are the RNFL and GC-IPL thickness; however, these metrics have limitations when swelling is severe. Qualitative interpretation of the transverse axial SD-OCT aids in assessing peripapillary shape that may help distinguish papilledema from pseudopapilledema, evaluate atypical optic neuropathies, diagnose shunt failures, and identify outer RF and CF. There is a consensus that the SD-OCT is the most sensitive way of identifying buried optic disc drusen. En face SD-OCT is especially effective at detecting peripapillary wrinkles and outer retinal creases, both of which are common and distinctive signs of optic disc edema that rule out pseudopapilledema. Mechanically stressing the ONH in the adducted eye position, in patients with papilledema, may expose folds and peripapillary deformations that may not be evident in primary position. We also discuss how to optimize the acquisition and registration of SD-OCT images.

CONCLUSIONS

The SD-OCT is not a substitute for a complete history and a careful examination. It is, however, a convenient ancillary test that aids in the diagnosis and management of papilledema, optic disc edema, and pseudopapilledema. It is particularly helpful in monitoring changes over the course of time and distinguishing low-grade papilledema from buried drusen. The application of the SD-OCT toolbox depends on optimizing the acquisition of images, understanding its limitations, recognizing common artifacts, and accurately interpreting images in the context of both history and clinical findings.

Structured layer surface segmentation for retina OCT using fully convolutional regression networks

Optical coherence tomography (OCT) is a noninvasive imaging modality with micrometer resolution which has been widely used for scanning the retina. Retinal layers are important biomarkers for many diseases. Accurate automated algorithms for segmenting smooth continuous layer surfaces with correct hierarchy (topology) are important for automated retinal thickness and surface shape analysis. State-of-the-art methods typically use a two step process. Firstly, a trained classifier is used to label each pixel into either background and layers or boundaries and non-boundaries. Secondly, the desired smooth surfaces with the correct topology are extracted by graph methods (e.g., graph cut). Data driven methods like deep networks have shown great ability for the pixel classification step, but to date have not been able to extract structured smooth continuous surfaces with topological constraints in the second step. In this paper, we combine these two steps into a unified deep learning framework by directly modeling the distribution of the surface positions. Smooth, continuous, and topologically correct surfaces are obtained in a single feed forward operation. The proposed method was evaluated on two publicly available data sets of healthy controls and subjects with either multiple sclerosis or diabetic macular edema, and is shown to achieve state-of-the art performance with sub-pixel accuracy.

Evaluation of Asymmetry in Right and Left Eyes of Normal Individuals Using Extracted Features from Optical Coherence Tomography and Fundus Images

BACKGROUND

Asymmetry analysis of retinal layers in right and left eyes can be a valuable tool for early diagnoses of retinal diseases. To determine the limits of the normal interocular asymmetry in retinal layers around macula, thickness measurements are obtained with optical coherence tomography (OCT).

METHODS

For this purpose, after segmentation of intraretinal layer in threedimensional OCT data and calculating the midmacular point, the TM of each layer is obtained in 9 sectors in concentric circles around the macula. To compare corresponding sectors in the right and left eyes, the TMs of the left and right images are registered by alignment of retinal raphe (i.e. diskfovea axes). Since the retinal raphe of macular OCTs is not calculable due to limited region size, the TMs are registered by first aligning corresponding retinal raphe of fundus images and then registration of the OCTs to aligned fundus images. To analyze the asymmetry in each retinal layer, the mean and standard deviation of thickness in 9 sectors of 11 layers are calculated in 50 normal individuals.

RESULTS

The results demonstrate that some sectors of retinal layers have signifcant asymmetry with P < 0.05 in normal population. In this base, the tolerance limits for normal individuals are calculated.

CONCLUSION

This article shows that normal population does not have identical retinal information in both eyes, and without considering this reality, normal asymmetry in information gathered from both eyes might be interpreted as retinal disorders.

Optimizing 3D retinal vasculature imaging in diabetic retinopathy using registration and averaging of OCT-A

High resolution visualization of optical coherence tomography (OCT) and OCT angiography (OCT-A) data is required to fully take advantage of the imaging modality's three-dimensional nature. However, artifacts induced by patient motion often degrade OCT-A data quality. This is especially true for patients with deteriorated focal vision, such as those with diabetic retinopathy (DR). We propose a novel methodology for software-based OCT-A motion correction achieved through serial acquisition, volumetric registration, and averaging. Motion artifacts are removed via a multi-step 3D registration process, and visibility is significantly enhanced through volumetric averaging. We demonstrate that this method permits clear 3D visualization of retinal pathologies and their surrounding features, 3D visualization of inner retinal capillary connections, as well as reliable visualization of the choriocapillaris layer.

The Impact of Glycemic Control on Retinal Photoreceptor Layers and Retinal Pigment Epithelium in Patients With Type 2 Diabetes Without Diabetic Retinopathy: A Follow-Up Study

AIMS

To establish the sequential changes by glycemic control in the mean thickness, volume and reflectance of the macular photoreceptor layers (MPRLs) and retinal pigment epithelium in patients with type 2 diabetes without diabetic retinopathy.

METHODS

Thirty-one poorly controlled (HbA1c > 8.0%) patients with type 2 diabetes without diabetic retinopathy undergoing glycemic control and 39 control subjects with normal HbA1c levels (< 5.9%) underwent periodical full medical, neurological and ophthalmological examinations over 2 years. Glycemic variability was evaluated by standard deviation and coefficient of variation of monthly measured HbA1c levels and casual plasma glucose. 3D swept source-optical coherence tomography (OCT) and OCT-Explorer-generated enface thickness, volume and reflectance images for 9 subfields defined by Early Treatment Diabetic Retinopathy Study of 4 MPRLs {outer nuclear layer, ellipsoid zone, photoreceptor outer segment (PROS) and interdigitation zone} and retinal pigment epithelium were acquired every 3 months.

RESULTS

Glycemic control sequentially restored the thickness and volume at 6, 4 and 5 subfields of outer nuclear layer, ellipsoid zone and PROS, respectively. The thickness and volume of outer nuclear layer were restored related to the decrease in HbA1c and casual plasma glucose levels, but not related to glycemic variability and neurological tests. The reflectance of MPRLs and retinal pigment epithelium in patients was marginally weaker than controls, and further decreased at 6 or 15 months during glycemic control. The reduction at 6 months coincided with high HbA1c levels.

CONCLUSION

Glycemic control sequentially restored the some MPRL thickness, especially of outer nuclear layer. In contrast, high glucose during glycemic control decreased reflectance and may lead to the development of diabetic retinopathy induced by glycemic control. The repeated OCT examinations can clarify the benefit and hazard of glycemic control to the diabetic retinopathy.

DeepRetina: Layer Segmentation of Retina in OCT Images Using Deep Learning

Purpose

To automate the segmentation of retinal layers, we propose DeepRetina, a method based on deep neural networks.

Methods

DeepRetina uses the improved Xception65 to extract and learn the characteristics of retinal layers. The Xception65-extracted feature maps are inputted to an atrous spatial pyramid pooling module to obtain multiscale feature information. This information is then recovered to capture clearer retinal layer boundaries in the encoder-decoder module, thus completing retinal layer auto-segmentation of the retinal optical coherence tomography (OCT) images.

Results

We validated this method using a retinal OCT image database containing 280 volumes (40 B-scans per volume) to demonstrate its effectiveness. The results showed that the method exhibits excellent performance in terms of the mean intersection over union and sensitivity (Se), which are as high as 90.41 and 92.15%, respectively. The intersection over union and Se values of the nerve fiber layer, ganglion cell layer, inner plexiform layer, inner nuclear layer, outer plexiform layer, outer nuclear layer, outer limiting membrane, photoreceptor inner segment, photoreceptor outer segment, and pigment epithelium layer were found to be above 88%.

Conclusions

DeepRetina can automate the segmentation of retinal layers and has great potential for the early diagnosis of fundus retinal diseases. In addition, our approach will provide a segmentation model framework for other types of tissues and cells in clinical practice.

Translational Relevance

Automating the segmentation of retinal layers can help effectively diagnose and monitor clinical retinal diseases. In addition, it requires only a small amount of manual segmentation, significantly improving work efficiency.

Extraction of Retinal Layers Through Convolution Neural Network (CNN) in an OCT Image for Glaucoma Diagnosis

Glaucoma is a progressive and deteriorating optic neuropathy that leads to visual field defects. The damage occurs as glaucoma is irreversible, so early and timely diagnosis is of significant importance. The proposed system employs the convolution neural network (CNN) for automatic segmentation of the retinal layers. The inner limiting membrane (ILM) and retinal pigmented epithelium (RPE) are used to calculate cup-to-disc ratio (CDR) for glaucoma diagnosis. The proposed system uses structure tensors to extract candidate layer pixels, and a patch across each candidate layer pixel is extracted, which is classified using CNN. The proposed framework is based upon VGG-16 architecture for feature extraction and classification of retinal layer pixels. The output feature map is merged into SoftMax layer for classification and produces probability map for central pixel of each patch and decides whether it is ILM, RPE, or background pixels. Graph search theory refines the extracted layers by interpolating the missing points, and these extracted ILM and RPE are finally used to compute CDR value and diagnose glaucoma. The proposed system is validated using a local dataset of optical coherence tomography images from 196 patients, including normal and glaucoma subjects. The dataset contains manually annotated ILM and RPE layers; manually extracted patches for ILM, RPE, and background pixels; CDR values; and eventually final finding related to glaucoma. The proposed system is able to extract ILM and RPE with a small absolute mean error of 6.03 and 5.56, respectively, and it finds CDR value within average range of ± 0.09 as compared with glaucoma expert. The proposed system achieves average sensitivity, specificity, and accuracies of 94.6, 94.07, and 94.68, respectively.

Automatic Anisotropic Diffusion Filtering and Graph-search Segmentation of Macular Spectral-domain Optical Coherence Tomographic (SD-OCT) Images

BACKGROUND

Optical Coherence Tomography (OCT) is a non-invasive medical imaging technique that provides high-resolution cross-sectional images of the retina. There is a need to develop algorithms for obtaining quantitative and qualitative information about the retina which are essential for assessing and managing eye conditions.

METHODS

This work emphasizes on an automated image processing algorithm for segmenting retinal layers. It involves preprocessing of the acquired retinal SD-OCT image (B-scan) using the proposed automatic Anisotropic diffusion filter, followed with contrast stretching to suppress intrinsic speckle noise without blurring structural edges. Graph search segmentation using Dijkstra algorithm with a combination of threshold and axial gradient as the cost function is used to segment the retinal layer boundaries.

RESULTS

The algorithm was performed and the average thickness of the segmented retina was computed for the 3D retinal scan (128 B-scans) of 8 subjects (4 normal and 4 abnormal) using Early Treatment Diabetic Retinopathy Screening (ETDRS) chart.

CONCLUSION

Segmentation was evaluated using manually segmented B-scan by an Ophthalmologist as ground truth and accuracy was found to be 99.14 ± 0.27%.

HyCAD-OCT: A Hybrid Computer-Aided Diagnosis of Retinopathy by Optical Coherence Tomography Integrating Machine Learning and Feature Maps Localization

Optical Coherence Tomography (OCT) imaging has major advantages in effectively identifying the presence of various ocular pathologies and detecting a wide range of macular diseases. OCT examinations can aid in the detection of many retina disorders in early stages that could not be detected in traditional retina images. In this paper, a new hybrid computer-aided OCT diagnostic system (HyCAD) is proposed for classification of Diabetic Macular Edema (DME), Choroidal Neovascularization (CNV) and drusen disorders, while separating them from Normal OCT images. The proposed HyCAD hybrid learning system integrates the segmentation of Region of Interest (RoI), based on central serious chorioretinopathy (CSC) in Spectral Domain Optical Coherence Tomography (SD-OCT) images, with deep learning architectures for effective diagnosis of retinal disorders. The proposed system assimilates a range of techniques including RoI localization and feature extraction, followed by classification and diagnosis. An efficient feature fusion phase has been introduced for combining the OCT image features, extracted by Deep Convolutional Neural Network (CNN), with the features extracted from the RoI segmentation phase. This fused feature set is used to predict multiclass OCT retina disorders. The proposed segmentation phase of retinal RoI regions adds substantial contribution as it draws attention to the most significant areas that are candidate for diagnosis. A new modified deep learning architecture (Norm-VGG16) is introduced integrating a kernel regularizer. Norm-VGG16 is trained from scratch on a large benchmark dataset and used in RoI localization and segmentation. Various experiments have been carried out to illustrate the performance of the proposed system. Large Dataset of Labeled Optical Coherence Tomography (OCT) v3 benchmark is used to validate the efficiency of the model compared with others in literature. The experimental results show that the proposed model achieves relatively high-performance in terms of accuracy, sensitivity and specificity. An average accuracy, sensitivity and specificity of 98.8%, 99.4% and 98.2% is achieved, respectively. The remarkable performance achieved reflects that the fusion phase can effectively improve the identification ratio of the urgent patients’ diagnostic images and clinical data. In addition, an outstanding performance is achieved compared to others in literature.

Characterization of Drusen and Hyperreflective Foci as Biomarkers for Disease Progression in Age-Related Macular Degeneration Using Artificial Intelligence in Optical Coherence Tomography

Importance

The morphologic changes and their pathognomonic distribution in progressing age-related macular degeneration (AMD) are not well understood.

Objectives

To characterize the pathognomonic distribution and time course of morphologic patterns in AMD and to quantify changes distinctive for progression to macular neovascularization (MNV) and macular atrophy (MA).

Design, Setting, and Participants

This cohort study included optical coherence tomography (OCT) volumes from study participants with early or intermediate AMD in the fellow eye in the HARBOR (A Study of Ranibizumab Administered Monthly or on an As-needed Basis in Patients With Subfoveal Neovascular Age-Related Macular Degeneration) trial. Patients underwent imaging monthly for 2 years (July 1, 2009, to August 31, 2012) following a standardized protocol. Data analysis was performed from June 1, 2018, to January 21, 2020.

Main Outcomes and Measures

To obtain topographic correspondence between patients and over time, all scans were mapped into a joint reference frame. The time of progression to MNV and MA was established, and drusen volumes and hyperreflective foci (HRF) volumes were automatically segmented in 3 dimensions using validated artificial intelligence algorithms. Topographically resolved population means of these markers were constructed by averaging quantified drusen and HRF maps in the patient subgroups.

Results

Of 1097 patients enrolled in HARBOR, 518 (mean [SD] age, 78.1 [8.2] years; 309 [59.7%] female) had early or intermediate AMD in the fellow eye at baseline. During the 24-month follow-up period, 135 (26%) eyes developed MNV, 50 eyes (10%) developed MA, and 333 (64%) eyes did not progress to advanced AMD. Drusen and HRF had distinct topographic patterns. Mean drusen thickness at the fovea was 29.6 μm (95% CI, 20.2-39.0 μm) for eyes progressing to MNV, 17.2 μm (95% CI, 9.8-24.6 μm) for eyes progressing to MA, and 17.1 μm (95% CI, 12.5-21.7 μm) for eyes without disease progression. At 0.5-mm eccentricity, mean drusen thickness was 25.8 μm (95% CI, 19.1-32.5 μm) for eyes progressing to MNV, 21.7 μm (95% CI, 14.6-28.8 μm) for eyes progressing to MA, and 14.4 μm (95% CI, 11.2-17.6 μm) for eyes without disease progression. The mean HRF thickness at the foveal center was 0.072 μm (95% CI, 0-0.152 μm) for eyes progressing to MNV, 0.059 μm (95% CI, 0-0.126 μm) for eyes progressing to MA, and 0.044 μm (95% CI, 0.007-0.081) for eyes without disease progression. At 0.5-mm eccentricity, the largest mean HRF thickness was seen in eyes progressing to MA (0.227 μm; 95% CI, 0.104-0.349 μm) followed by eyes progressing to MNV (0.161 μm; 95% CI, 0.101-0.221 μm) and eyes without disease progression (0.085 μm; 95% CI, 0.058-0.112 μm).

Conclusions and Relevance

In this study, drusen and HRF represented imaging biomarkers of disease progression in AMD, demonstrating distinct topographic patterns over time that differed between eyes progressing to MNV, eyes progressing to MA, or eyes without disease progression. Automated localization and precise quantification of these factors may help to develop reliable methods of predicting future disease progression.

Semi-supervised deep learning based 3D analysis of the peripapillary region

Optical coherence tomography (OCT) has become an essential tool in the evaluation of glaucoma, typically through analyzing retinal nerve fiber layer changes in circumpapillary scans. Three-dimensional OCT volumes enable a much more thorough analysis of the optic nerve head (ONH) region, which may be the site of initial glaucomatous optic nerve damage. Automated analysis of this region is of great interest, though large anatomical variations and the termination of layers make the requisite peripapillary layer and Bruch's membrane opening (BMO) segmentation a challenging task. Several machine learning-based segmentation methods have been proposed for retinal layer segmentation, and a few for the ONH region, but they typically depend on either heavily averaged or pre-processed B-scans or a large amount of annotated data, which is a tedious task and resource-intensive. We evaluated a semi-supervised adversarial deep learning method for segmenting peripapillary retinal layers in OCT B-scans to take advantage of unlabeled data. We show that the use of a generative adversarial network and unlabeled data can improve the performance of segmentation. Additionally, we use a Faster R-CNN architecture to automatically segment the BMO. The proposed methods are then used for the 3D morphometric analysis of both control and glaucomatous ONH volumes to demonstrate the potential for clinical utility.

Correction propagation for user-assisted optical coherence tomography segmentation: general framework and application to Bruch's membrane segmentation

Optical coherence tomography (OCT) is a commonly used ophthalmic imaging modality. While OCT has traditionally been viewed cross-sectionally (i.e., as a sequence of B-scans), higher A-scan rates have increased interest in en face OCT visualization and analysis. The recent clinical introduction of OCT angiography (OCTA) has further spurred this interest, with chorioretinal OCTA being predominantly displayed via en face projections. Although en face visualization and quantitation are natural for many retinal features (e.g., drusen and vasculature), it requires segmentation. Because manual segmentation of volumetric OCT data is prohibitively laborious in many settings, there has been significant research and commercial interest in developing automatic segmentation algorithms. While these algorithms have achieved impressive results, the variability of image qualities and the variety of ocular pathologies cause even the most robust automatic segmentation algorithms to err. In this study, we develop a user-assisted segmentation approach, complementary to fully-automatic methods, wherein correction propagation is used to reduce the burden of manually correcting automatic segmentations. The approach is evaluated for Bruch's membrane segmentation in eyes with advanced age-related macular degeneration.

The spatial relation of diabetic retinal neurodegeneration with diabetic retinopathy

Purpose

Diabetic retinal neurodegeneration (DRN) has been demonstrated in eyes of patients with diabetes mellitus (DM), even in the absence of diabetic retinopathy (DR). However, no studies have looked at the rate of change in retinal layers and presence/development of DR over time per quadrant of the macula. In this longitudinal study, we aimed to clarify whether the rate of DRN is associated with the development/presence of DR within 4 different quadrants of the retina.

Methods

80 eyes of 40 patients with type 1 DM and no/minimal DR were included. At 4 visits over 6 years, SD-OCT and fundus images were acquired. Thickness of the Retinal Nerve Fiber Layer (RNFL), Ganglion Cell Layer (GCL) and Inner Plexiform Layer (IPL) was measured in a 1-6mm circle around the fovea overall and for each quadrant (superior, nasal, inferior, temporal). Fundus images were scored for the presence/absence of DR in these areas. Multilevel analyses were performed to determine the rate of change for each layer overall and per quadrant for eyes/quadrants without and with DR during the follow-up period.

Results

RNFL and GCL showed significant thinning over time, IPL significant thickening. These changes were more pronounced for GCL and IPL in eyes/quadrants with DR during the follow-up period.

Conclusions

RNFL and GCL both showed thinning over time, which was more pronounced in eyes with DR for GCL. This holds true even in regional parts of the retina, as quadrant analyses showed similar results, showing that structural DRN is associated with DR per quadrant independently.

Adaptive-Guided-Coupling-Probability Level Set for Retinal Layer Segmentation

Quantitative assessment of retinal layer thickness in spectral domain-optical coherence tomography (SD-OCT) images is vital for clinicians to determine the degree of ophthalmic lesions. However, due to the complex retinal tissues, high-level speckle noises and low intensity constraint, how to accurately recognize the retinal layer structure still remains a challenge. To overcome this problem, this paper proposes an adaptive-guided-coupling-probability level set method for retinal layer segmentation in SD-OCT images. Specifically, based on Bayes's theorem, each voxel probability representation is composed of two probability terms in our method. The first term is constructed as neighborhood Gaussian fitting distribution to characterize intensity information for each intra-retinal layer. The second one is boundary probability map generated by combining anatomical priors and adaptive thickness information to ensure surfaces evolve within a proper range. Then, the voxel probability representation is introduced into the proposed segmentation framework based on coupling probability level set to detect layer boundaries. A total of 1792 retinal B-scan images from 4 SD-OCT cubes in healthy eyes, 5 cubes in abnormal eyes with central serous chorioretinaopathy and 5 SD-OCT cubes in abnormal eyes with age-related macular disease are used to evaluate the proposed method. The experiment demonstrates that the segmentation results obtained by the proposed method have a good consistency with ground truth, and the proposed method outperforms six methods in the layer segmentation of uneven retinal SD-OCT images.

Optic Nerve Head Volumetry by Optical Coherence Tomography in Papilledema Related to Idiopathic Intracranial Hypertension

Purpose

Idiopathic intracranial hypertension (IIH) leads to optic nerve head swelling and optic atrophy if left untreated. We wanted to assess an easy to perform volumetric algorithm to detect and quantify papilledema in comparison to retinal nerve fiber layer (RNFL) analysis using optical coherence tomography (OCT).

Methods

Participants with and without IIH underwent visual acuity testing at different contrast levels and static perimetry. Spectralis-OCT measurements comprised standard imaging of the peripapillary RNFL and macular ganglion cell layer (GCL). The optic nerve head volume (ONHV) was determined using the standard segmentation software and the 3.45 mm early treatment diabetic retinopathy study (ETDRS) grid, necessitating manual correction within Bruch membrane opening. Three neuro-ophthalmologists graded fundus images according to the Frisén scale. A mixed linear model (MLM) was used to determine differences between study groups. Sensitivity and specificity was evaluated using the area under the receiver-operating characteristic (ROC).

Results

Twenty-one patients with IIH had an increased ONHV of 6.46 ± 2.36 mm³ as compared to 25 controls with 3.20 ± 0.25 mm³ (P < 0.001). The ONHV cutoff distinguishing IIH from controls was 3.97 mm³ (i.e. no patient with IIH had an ONHV below and no healthy individual above this value). The area under the curve (AUC) for ONHV was 0.99 and for the RNFL at 3.5 mm 0.90. The Frisén scale grading correlated higher with the ONHV (r = 0.90) than with the RNFL thickness (r = 0.68). ONHV measurements were highly reproducible in both groups (coefficient of variation <0.01%).

Conclusions

OCT-based volumetry of the optic nerve head discriminates very accurately between individuals with and without IIH. It may serve as a useful adjunct to the rating with the subjective and ordinal Frisén scale.

Translational Relevance

A simple OCT protocol run on the proprietary software of a commercial OCT device can reliably discriminate between normal optic nerve heads or pseudo-papilledema and true papilledema while being highly reproducible. Our normative data and OCT preset may be used in further clinical studies.

Thickness of individual layers at the macula and associated factors: the Beijing Eye Study 2011

BACKGROUND

Diagnosis and follow-up of retinal diseases may be improved if the thickness of the various retinal layers, in addition to the total retinal thickness, is taken into account. Here we measured the thickness of the macular retinal layers in a population-based study group to assess the normative values and their associations.

METHODS

Using spectral-domain optical coherence tomographic images (Spectralis®, wavelength: 870 nm; Heidelberg Engineering Co, Heidelberg, Germany), we measured the thickness of the macular retinal layers in participants of the population-based Beijing Eye Study without ocular diseases and without systematic diseases, such as arterial hypertension, hyperlipidemia, diabetes mellitus, cardiovascular diseases, previous myocardial infarction, cerebral trauma and stroke. Segmentation and measurement of the retinal layers was performed automatically in each of the horizontal scans.

RESULTS

The study included 384 subjects (mean age:60.0 ± 8.0 years). The mean thickness of the whole retina, outer plexiform layer, outer nuclear layer,retinal pigment epithelium, inner retinal layer and photoreceptor layer was 259.8 ± 18.9 μm, 19.4 ± 3.9 μm, 93.4 ± 9.6 μm, 17.6 ± 1.9 μm, 169.8 ± 18.6 μm, and 90.0 ± 4.2 μm, respectively. In multivariable analysis, the thickness of the foveola and of all retinal layers in the foveal, parafoveal and perifoveal region decreased with older age (all P < 0.05), except for the thickness of the parafoveal outer plexiform layer which increased with age. Men as compared to women had higher thickness measurements of the photoreceptor layer and outer nuclear layer in all areas, and of all layers between the retinal nerve fiber layer and inner nuclear layer in the parafoveal area (all P < 0.05). The associations between the macular retinal layers thickness and axial length were not consistent. The inner plexiform layer was thicker, and the ganglion cell layer and inner nuclear layer were thinner, in the temporal areas than in the nasal areas, CONCLUSIONS: The associations between decreasing thickness of most retinal layers with older age and the correlation of a higher thickness of some retinal layers with male gender may clinically be taken into account.

Three-dimensional curvelet-based dictionary learning for speckle noise removal of optical coherence tomography

Optical coherence tomography (OCT) is a recently emerging non-invasive diagnostic tool useful in several medical applications such as ophthalmology, cardiology, gastroenterology and dermatology. One of the major problems with OCT pertains to its low contrast due to the presence of multiplicative speckle noise, which limits the signal-to-noise ratio (SNR) and obscures low-intensity and small features. In this paper, we recommend a new method using the 3D curvelet based K-times singular value decomposition (K-SVD) algorithm for speckle noise reduction and contrast enhancement of the intra-retinal layers of 3D Spectral-Domain OCT (3D-SDOCT) images. In order to benefit from the near-optimum properties of curvelet transform (such as good directional selectivity) on top of dictionary learning, we propose a new plan in dictionary learning by using the curvelet atoms as the initial dictionary. For this reason, the curvelet transform of the noisy image is taken and then the noisy coefficients matrix in each scale, rotation and spatial coordinates is passed through the K-SVD denoising algorithm with predefined 3D initial dictionary that is adaptively selected from thresholded coefficients in the same subband of the image. During the denoising of curvelet coefficients, we can also modify them for the purpose of contrast enhancement of intra-retinal layers. We demonstrate the ability of our proposed algorithm in the speckle noise reduction of 17 publicly available 3D OCT data sets, each of which contains 100 B-scans of size 512×1000 with and without neovascular age-related macular degeneration (AMD) images acquired using SDOCT, Bioptigen imaging systems. Experimental results show that an improvement from 1.27 to 7.81 in contrast to noise ratio (CNR), and from 38.09 to 1983.07 in equivalent number of looks (ENL) is achieved, which would outperform existing state-of-the-art OCT despeckling methods.

OCTRexpert:A Feature-based 3D Registration Method for Retinal OCT Images

Medical image registration can be used for studying longitudinal and cross-sectional data, quantitatively monitoring disease progression and guiding computer assisted diagnosis and treatments. However, deformable registration which enables more precise and quantitative comparison has not been well developed for retinal optical coherence tomography (OCT) images. This paper proposes a new 3D registration approach for retinal OCT data called OCTRexpert. To the best of our knowledge, the proposed algorithm is the first full 3D registration approach for retinal OCT images which can be applied to longitudinal OCT images for both normal and serious pathological subjects. In this approach, a pre-processing method is first performed to remove eye motion artifact and then a novel design-detection-deformation strategy is applied for the registration. In the design step, a couple of features are designed for each voxel in the image. In the detection step, active voxels are selected and the point-to-point correspondences between the subject and template images are established. In the deformation step, the image is hierarchically deformed according to the detected correspondences in multi-resolution. The proposed method is evaluated on a dataset with longitudinal OCT images from 20 healthy subjects and 4 subjects diagnosed with serious Choroidal Neovascularization (CNV). Experimental results show that the proposed registration algorithm consistently yields statistically significant improvements in both Dice similarity coefficient and the average unsigned surface error compared with the other registration methods.

Alterations of Retinal Pigment Epithelium-Photoreceptor Complex in Patients with Type 2 Diabetes Mellitus without Diabetic Retinopathy: A Cross-Sectional Study

AIM

A cross-sectional study was performed to examine the alterations of the retinal pigment epithelium- (RPE-) photoreceptor complex layer in type 2 diabetes mellitus (DM) without diabetic retinopathy (DR), using spectral-domain optical coherence tomography (SD-OCT).

METHODS

Patients with type 2 DM without DR and healthy controls without DM were recruited. All participants underwent examinations including SD-OCT. The thickness measurements of the retinal neural layers were calculated after automatic segmentation. An independent-sample t-test was used to compare the means of the thickness of retinal neural layers in patients with DM and healthy controls.

RESULTS

Sixty-seven eyes from 67 patients with DM and 30 eyes from 30 healthy controls were included in this study. No significant differences were found in age (P = 0.601), gender (P = 0.601), gender (P = 0.601), gender (P = 0.601), gender (P = 0.601), gender (P = 0.601), gender (P = 0.601), gender (P = 0.601), gender (.

CONCLUSION

Lesions in the RPE-photoreceptor complex are present without vascular abnormalities, which may precede the alterations of ganglion cells in patients with type 2 DM.

Thinning of Macular Neuroretinal Layers Contributes to Sleep Disorder in Patients With Type 2 Diabetes Without Clinical Evidences of Neuropathy and Retinopathy

Aims: To investigate the impact of thinning at individual grids of macular neuroretinal layers, clinical factors, and inadequate light exposure on the specific components of sleep disorder in patients with type 2 diabetes. Methods: One hundred twenty-four patients with type 2 diabetes without clinical evidences of diabetic retinopathy and neuropathy (HbA1c: 8.3%, diabetes duration; 8.7 years) and 54 age- and sex-matched control subjects (HbA1c: 5.6%) underwent detailed clinical, neurological, and ophthalmological examinations. The sleep disorder was assessed by the Pittsburgh Sleep Quality Index Japanese Version (PSQI-J). The temporal structures of daily life were assessed by the Munich Chronotype Questionnaire Japanese Version. The thickness at nine grids defined by the Early Treatment Diabetic Retinopathy Study of nine macular neuroretinal layers was determined by swept-source optical coherence tomography and OCT-Explorer. The associations between the individual components of sleep disorders and the thickness at each grid of macular neuroretinal layers, clinical factors, or the temporal structures of daily life were examined. Results: The prevalence of the sleep disorder, global score, and four individual PSQI-J scores in patients with type 2 diabetes were higher than control subjects. The thickness of two and five grids of two inner retinal layers and four to seven grids of four outer retinal layers in patients with type 2 diabetes was thinner than those in control subjects. The thickness at one to eight grids of four outer retinal layers in type 2 diabetic patients was inversely associated with global score and five individual scores of sleep disorder. The thinning at one to two grids of the inner plexiform layer was related to three high individual scores of sleep disorder. The inappropriate light exposure was associated with the sleep disorder and altered macular neuroretinal layers. The high HbA1c and LDL-cholesterol levels were related to the high global score and two individual scores of sleep disorder, respectively. Conclusion: In patients with type 2 diabetes, the thinning at grids of the inner plexiform layer and outer retinal layers was associated with the high scores of specific components of the sleep disorder. The sleep disorder was also related to hyperglycemia, dyslipidemia, and inappropriate light exposure.

Distribution of macular ganglion cell layer thickness in foveal hypoplasia: A new diagnostic criterion for ocular albinism

Background/Aims

To analyse the distribution of macular ganglion cell layer thickness (GCLT) in patients with foveal hypoplasia (FH) with or without albinism to obtain new insights into visual pathway anomalies in albinos.

Methods

Patients with FH who presented at our institution between 2013 and 2018 were retrospectively drawn for analysis. Mean GCLT was calculated after automated segmentation of spectral domain-optical coherence tomography (SD-OCT) scans. Patients with FH due to albinism (n = 13, termed ‘albinism FH’) or other kinds (n = 10, termed ‘non-albinism FH’) were compared with control subjects (n = 15). The areas: fovea (central), parafovea (nasal I, temporal I) and perifovea (nasal II, temporal II) along the horizontal meridian were of particular interest. Primary endpoints of this study were the ratios (GCLT-I- and GCLT-II-Quotient) between the GCLT measured in the temporal I or II and nasal I or II areas.

Results

There was a significant difference between the GCLT-I-Quotient of healthy controls and albinism FH (p<0.001), as well as between non-albinism FH and albinism FH (p = 0.004). GCLT-II-Quotient showed significant differences between healthy controls and albinism FH (p<0.001) and between non-albinism FH and albinism FH (p = 0.006). The best measure for distinguishing between non-albinism FH and albinism FH was the calculation of GCLT-II-Quotient (area temporal II divided by area nasal II), indicating albinism at a cut-off of <0.7169. The estimated specificity and sensitivity for this cut-off were 84.6% and 100.0%, respectively. The estimated area under the curve (AUC) was 0.892 [95%CI: 0.743–1.000, p = 0.002].

Conclusion

Macular GCLT-distribution showed a characteristic temporal to central shift in patients with FH due to albinism. Calculation of the GCLT-II-Quotient at a cut-off of <0.7169 presents a new diagnostic criterion for identification of ocular albinism.

Automated segmentation of peripapillary retinal boundaries in OCT combining a convolutional neural network and a multi-weights graph search

Quantitative analysis of the peripapillary retinal layers and capillary plexuses from optical coherence tomography (OCT) and OCT angiography images depend on two segmentation tasks - delineating the boundary of the optic disc and delineating the boundaries between retinal layers. Here, we present a method combining a neural network and graph search to perform these two tasks. A comparison of this novel method's segmentation of the disc boundary showed good agreement with the ground truth, achieving an overall Dice similarity coefficient of 0.91 ± 0.04 in healthy and glaucomatous eyes. The absolute error of retinal layer boundaries segmentation in the same cases was 4.10 ± 1.25 µm.

Increasing Accuracy of Optimal Surfaces Using Min-Marginal Energies

Optimal surface methods are a class of graph cut methods posing surface estimation as an n-ary ordered labeling problem. They are used in medical imaging to find interacting and layered surfaces optimally and in low order polynomial time. Representing continuous surfaces with discrete sets of labels, however, leads to discretization errors and, if graph representations are made dense, excessive memory usage. Limiting memory usage and computation time of graph cut methods are important and graphs that locally adapt to the problem has been proposed as a solution. Min-marginal energies computed using dynamic graph cuts offer a way to estimate solution uncertainty and these uncertainties have been used to decide where graphs should be adapted. Adaptive graphs, however, introduce extra parameters, complexity, and heuristics. We propose a way to use min-marginal energies to estimate continuous solution labels that does not introduce extra parameters and show empirically on synthetic and medical imaging datasets that it leads to improved accuracy. The increase in accuracy was consistent and in many cases comparable with accuracy otherwise obtained with graphs up to eight times denser, but with proportionally less memory usage and improvements in computation time.

Segmentation of mouse skin layers in optical coherence tomography image data using deep convolutional neural networks

Optical coherence tomography (OCT) enables the non-invasive acquisition of high-resolution three-dimensional cross-sectional images at micrometer scale and is mainly used in the field of ophthalmology for diagnosis as well as monitoring of eye diseases. Also in other areas, such as dermatology, OCT is already well established. Due to its non-invasive nature, OCT is also employed for research studies involving animal models. Manual evaluation of OCT images of animal models is a challenging task due to the lack of imaging standards and the varying anatomy among models. In this paper, we present a deep learning algorithm for the automatic segmentation of several layers of mouse skin in OCT image data using a deep convolutional neural network (CNN). The architecture of our CNN is based on the U-net and is modified by densely connected convolutions. We compared our adapted CNN with our previous algorithm, a combination of a random forest classification and a graph-based refinement, and a baseline U-net. The results showed that, on average, our proposed CNN outperformed our previous algorithm and the baseline U-net. In addition, a reduction of outliers could be observed through the use of densely connected convolutions.