Recent developments in optical coherence tomography for imaging the retina

Hybrid attention structure preserving network for reconstruction of under-sampled OCT images

Optical coherence tomography (OCT) is a non-invasive, high-resolution imaging technology that provides cross-sectional images of tissues. Dense acquisition of A-scans along the fast axis is required to obtain high digital resolution images. However, the dense acquisition will increase the acquisition time, causing the discomfort of patients. In addition, the longer acquisition time may lead to motion artifacts, thereby reducing imaging quality. In this work, we proposed a hybrid attention structure preserving network (HASPN) to achieve super-resolution of under-sampled OCT images to speed up the acquisition. It utilized adaptive dilated convolution-based channel attention (ADCCA) and enhanced spatial attention (ESA) to better capture the channel and spatial information of the feature. Moreover, convolutional neural networks (CNNs) exhibit a higher sensitivity of low-frequency than high-frequency information, which may lead to a limited performance on reconstructing fine structures. To address this problem, we introduced an additional branch, i.e., textures & details branch, using high-frequency decomposition images to better super-resolve retinal structures. The superiority of our method was demonstrated by qualitative and quantitative comparisons with mainstream methods. Furthermore, HASPN was applied to three out-of-distribution datasets, validating its strong generalization capability.

In Vivo Imaging of Cobalt-Induced Ocular Toxicity in a Mouse Model

Cobalt is a trace element, crucial for red blood cell formation and neurological function. Cobalt toxicity is often only diagnosed after severe manifestations, including visual impairment. We aimed to investigate whether optical coherence tomography (OCT) and magnetic resonance imaging (MRI) can effectively detect cobalt-induced ocular toxicity in a murine model. Five wild-type mice (WT, C57Bl6) received daily intraperitoneal cobalt chloride injections for 28 days with a dosage of 12.5 mg/kg. Another 5 WT mice served as controls. After 28 days, all mice underwent manganese contrast-enhanced MRI and OCT examinations. Macroscopic and histological analysis of the enucleated eyes were performed. MRI revealed an increased signal in the optic nerves of injected mice. Anterion OCT provided in vivo visualization of the entire eye, demonstrating incipient cataract formation in the cobalt-injected mice. Both Spectralis domain OCT and Anterion, followed by histological analyses, confirmed preserved retinal structure with decreased thickness in the cobalt-injected group, with only minor neuronal damage and cell loss. Optic nerve analysis demonstrated myelin loss and increased inflammation with high levels of reactive gliosis. This study demonstrates optic neuropathy induced by cobalt toxicity, as shown by increased optic nerve signal on MRI without significant retinopathy. Anterion OCT showed incipient cataracts in the anterior segment.

Association of Lipopolysaccharide-Type Endotoxins with Retinal Neurodegeneration: The Alienor Study

Purpose

Lipopolysaccharide (LPS)-type endotoxins are naturally found in the gut microbiota and there is emerging evidence linking gut microbiota and neuroinflammation leading to retinal neurodegeneration. Thinning of the retinal nerve fiber layer (RNFL) is a biomarker of retinal neurodegeneration, and a hallmark of glaucoma, the second leading cause of blindness worldwide. We assessed the association of a blood biomarker of LPS with peripapillary RNFL thickness (RNFLT) and its longitudinal evolution up to 11 years.

Design

The Alienor study is a single center prospective population-based cohort study.

Subjects

The studied sample of this study includes 1062 eyes of 548 participants receiving ≥1 gradable RNFL measurement.

Methods

Plasma esterified 3-hydroxy fatty acids (3-OH FAs) were measured as a proxy of LPS burden. Retinal nerve fiber layer thickness was acquired using spectral-domain OCT imaging every 2 years from 2009 to 2020 (up to 5 visits).

Main Outcome Measures

Associations of plasma esterified 3-OH FAs with RNFLT were assessed using linear mixed models.

Results

Mean age of the included 548 participants was 82.4 ± 4.3 years and 62.6% were women. Higher plasma esterified 3-OH FAs was significantly associated with thinner RNFLT at baseline (coefficient beta = -1.42 microns for 1 standard deviation-increase in 3-OH FAs, 95% confidence interval [-2.56; -0.28], P = 0.02). This association remained stable after multivariate adjustment for potential confounders. No statistically significant association was found between 3-OH FAs and longitudinal RNFLT change.

Conclusions

Higher plasma esterified 3-OH FAs were associated with thinner RNFLT at baseline, indicating an involvement of LPS in the early processes of optic nerve neurodegeneration and highlighting the potential importance of the human microbiota in preserving retinal health.

Financial Disclosures

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Imaging the eye as a window to brain health: frontier approaches and future directions

Recent years have seen significant advances in diagnostic testing of central nervous system (CNS) function and disease. However, there remain challenges in developing a comprehensive suite of non- or minimally invasive assays of neural health and disease progression. Due to the direct connection with the CNS, structural changes in the neural retina, retinal vasculature and morphological changes in retinal immune cells can occur in parallel with disease conditions in the brain. The retina can also, uniquely, be assessed directly and non-invasively. For these reasons, the retina may prove to be an important "window" for revealing and understanding brain disease. In this review, we discuss the gross anatomy of the eye, focusing on the sensory and non-sensory cells of the retina, especially microglia, that lend themselves to diagnosing brain disease by imaging the retina. We include a history of ocular imaging to describe the different imaging approaches undertaken in the past and outline current and emerging technologies including retinal autofluorescence imaging, Raman spectroscopy, and artificial intelligence image analysis. These new technologies show promising potential for retinal imaging to be used as a tool for the diagnosis of brain disorders such as Alzheimer's disease and others and the assessment of treatment success.

Effect of Bergmeister papilla on disc parameters in spectral domain optical coherence tomography

OBJECTIVES

To investigate the morphological characteristics of Bergmeister papilla (BMP), a persistent hyaloid remnant tissue, and its effects on the measurements and repeatability of spectral-domain optical coherence tomography (OCT).

SUBJECTS/METHODS

The subjects of this prospective cross-sectional study including 83 patients with BMP and 76 unaffected individuals, all had open-angle structures. Images, including a 5-line raster and three consecutive optic disc cube scans centred on the optic disc, were acquired using the Cirrus high-definition OCT. BMP's structural characteristics were classified based on the raster scan images, and repeatability of acquiring optic nerve head and retinal nerve fibre layer parameters acquisition was analysed by calculating the test-retest standard deviation (Sw), coefficient of variance (CV), and intraclass correlation coefficient.

RESULTS

BMPs (n = 83) were categorised into lifting edge (LE) type (63.9%, n = 53), which partially covers the edge of the optic nerve head, and covering disc (CD) type (36.1%, n = 30), which completely covers the cupping area like a cap. The average cup-to-disc ratio (0.58 ± 0.21), vertical cup-to-disc ratio (0.55 ± 0.21), and cup volume (0.22 ± 0.22) of the CD type were lower than those of the LE type (0.66 ± 0.13, 0.64 ± 0.13, and 0.4 ± 0.27, respectively; all P < 0.05). Tolerability indices for repeatability of cup volume (Sw = 0.40 and CV = 0.36) and inferonasal (4 o'clock) retinal nerve fibre layer (Sw = 0.27 and CV = 0.25) in LE-type BMPs exceeded the cut-off value (0.22) and demonstrated stronger correlation with BMP location than that of the controls.

CONCLUSION

Caution should be exercised when interpreting OCT findings in eyes with BMP, as BMP can introduce a pitfall in OCT imaging.

Assessment of area and structural irregularity of retinal layers in diabetic retinopathy using machine learning and image processing techniques

Diabetes retinopathy prevention necessitates early detection, monitoring, and treatment. Non-invasive optical coherence tomography (OCT) shows structural changes in the retinal layer. OCT image evaluation necessitates retinal layer segmentation. The ability of our automated retinal layer segmentation to distinguish between normal, non-proliferative (NPDR), and proliferative diabetic retinopathy (PDR) was investigated in this study using quantifiable biomarkers such as retina layer smoothness index (SI) and area (S) in horizontal and vertical OCT images for each zone (fovea, superior, inferior, nasal, and temporal). This research includes 84 eyes from 57 individuals. The study shows a significant difference in the Area (S) of inner nuclear layer (INL) and outer nuclear layer (ONL) in the horizontal foveal zone across the three groups (p < 0.001). In the horizontal scan, there is a significant difference in the smoothness index (SI) of the inner plexiform layer (IPL) and the upper border of the outer plexiform layer (OPL) among three groups (p < 0.05). There is also a significant difference in the area (S) of the OPL in the foveal zone among the three groups (p = 0.003). The area (S) of the INL in the foveal region of horizontal slabs performed best for distinguishing diabetic patients (NPDR and PDR) from normal individuals, with an accuracy of 87.6%. The smoothness index (SI) of IPL in the nasal zone of horizontal foveal slabs was the most accurate at 97.2% in distinguishing PDR from NPDR. The smoothness index of the top border of the OPL in the nasal zone of horizontal slabs was 84.1% accurate in distinguishing NPDR from PDR. Smoothness index of IPL in the temporal zone of horizontal slabs was 89.8% accurate in identifying NPDR from PDR patients. In conclusion, optical coherence tomography can assess the smoothness index and irregularity of the inner and outer plexiform layers, particularly in the nasal and temporal regions of horizontal foveal slabs, to distinguish non-proliferative from proliferative diabetic retinopathy. The evolution of diabetic retinopathy throughout severity levels and its effects on retinal layer irregularity need more study.

Multi-Scale-Denoising Residual Convolutional Network for Retinal Disease Classification Using OCT

Macular pathologies can cause significant vision loss. Optical coherence tomography (OCT) images of the retina can assist ophthalmologists in diagnosing macular diseases. Traditional deep learning networks for retinal disease classification cannot extract discriminative features under strong noise conditions in OCT images. To address this issue, we propose a multi-scale-denoising residual convolutional network (MS-DRCN) for classifying retinal diseases. Specifically, the MS-DRCN includes a soft-denoising block (SDB), a multi-scale context block (MCB), and a feature fusion block (FFB). The SDB can determine the threshold for soft thresholding automatically, which removes speckle noise features efficiently. The MCB is designed to capture multi-scale context information and strengthen extracted features. The FFB is dedicated to integrating high-resolution and low-resolution features to precisely identify variable lesion areas. Our approach achieved classification accuracies of 96.4% and 96.5% on the OCT2017 and OCT-C4 public datasets, respectively, outperforming other classification methods. To evaluate the robustness of our method, we introduced Gaussian noise and speckle noise with varying PSNRs into the test set of the OCT2017 dataset. The results of our anti-noise experiments demonstrate that our approach exhibits superior robustness compared with other methods, yielding accuracy improvements ranging from 0.6% to 2.9% when compared with ResNet under various PSNR noise conditions.

Automatic Detection of AMD and DME Retinal Pathologies Using Deep Learning

Diabetic macular edema (DME) and age-related macular degeneration (AMD) are two common eye diseases. They are often undiagnosed or diagnosed late. This can result in permanent and irreversible vision loss. Therefore, early detection and treatment of these diseases can prevent vision loss, save money, and provide a better quality of life for individuals. Optical coherence tomography (OCT) imaging is widely applied to identify eye diseases, including DME and AMD. In this work, we developed automatic deep learning-based methods to detect these pathologies using SD-OCT scans. The convolutional neural network (CNN) from scratch we developed gave the best classification score with an accuracy higher than 99% on Duke dataset of OCT images.

Adaptive Optics Imaging of Inherited Retinal Disease

The human retina is amenable to direct, noninvasive visualization using a wide array of imaging modalities. In the ∼140 years since the publication of the first image of the living human retina, there has been a continued evolution of retinal imaging technology. Advances in image acquisition and processing speed now allow real-time visualization of retinal structure, which has revolutionized the diagnosis and management of eye disease. Enormous advances have come in image resolution, with adaptive optics (AO)-based systems capable of imaging the retina with single-cell resolution. In addition, newer functional imaging techniques provide the ability to assess function with exquisite spatial and temporal resolution. These imaging advances have had an especially profound impact on the field of inherited retinal disease research. Here we will review some of the advances and applications of AO retinal imaging in patients with inherited retinal disease.

RetiFluidNet: A Self-Adaptive and Multi-Attention Deep Convolutional Network for Retinal OCT Fluid Segmentation

Optical coherence tomography (OCT) helps ophthalmologists assess macular edema, accumulation of fluids, and lesions at microscopic resolution. Quantification of retinal fluids is necessary for OCT-guided treatment management, which relies on a precise image segmentation step. As manual analysis of retinal fluids is a time-consuming, subjective, and error-prone task, there is increasing demand for fast and robust automatic solutions. In this study, a new convolutional neural architecture named RetiFluidNet is proposed for multi-class retinal fluid segmentation. The model benefits from hierarchical representation learning of textural, contextual, and edge features using a new self-adaptive dual-attention (SDA) module, multiple self-adaptive attention-based skip connections (SASC), and a novel multi-scale deep self-supervision learning (DSL) scheme. The attention mechanism in the proposed SDA module enables the model to automatically extract deformation-aware representations at different levels, and the introduced SASC paths further consider spatial-channel interdependencies for concatenation of counterpart encoder and decoder units, which improve representational capability. RetiFluidNet is also optimized using a joint loss function comprising a weighted version of dice overlap and edge-preserved connectivity-based losses, where several hierarchical stages of multi-scale local losses are integrated into the optimization process. The model is validated based on three publicly available datasets: RETOUCH, OPTIMA, and DUKE, with comparisons against several baselines. Experimental results on the datasets prove the effectiveness of the proposed model in retinal OCT fluid segmentation and reveal that the suggested method is more effective than existing state-of-the-art fluid segmentation algorithms in adapting to retinal OCT scans recorded by various image scanning instruments.

Retinal Disease Detection Using Deep Learning Techniques: A Comprehensive Review

Millions of people are affected by retinal abnormalities worldwide. Early detection and treatment of these abnormalities could arrest further progression, saving multitudes from avoidable blindness. Manual disease detection is time-consuming, tedious and lacks repeatability. There have been efforts to automate ocular disease detection, riding on the successes of the application of Deep Convolutional Neural Networks (DCNNs) and vision transformers (ViTs) for Computer-Aided Diagnosis (CAD). These models have performed well, however, there remain challenges owing to the complex nature of retinal lesions. This work reviews the most common retinal pathologies, provides an overview of prevalent imaging modalities and presents a critical evaluation of current deep-learning research for the detection and grading of glaucoma, diabetic retinopathy, Age-Related Macular Degeneration and multiple retinal diseases. The work concluded that CAD, through deep learning, will increasingly be vital as an assistive technology. As future work, there is a need to explore the potential impact of using ensemble CNN architectures in multiclass, multilabel tasks. Efforts should also be expended on the improvement of model explainability to win the trust of clinicians and patients.

Intraocular delivery considerations of ocular biologic products and key preclinical determinations

INTRODUCTION

Ophthalmic diseases of the retina are a significant cause of vision loss globally. Despite much progress, there remains an unmet need for durable, long-acting treatment options. While biologic therapies show great promise, they present many challenges, including complexities in biochemical properties, mechanism of action, manufacturing considerations, preclinical evaluation, and delivery mechanism; these are confounded by the unique anatomy and physiology of the eye itself.

AREAS COVERED

This review describes the current development status of intravitreally administered drugs for the treatment of ophthalmic disease, outlines the range of approaches that can be considered for sustained drug delivery to the eye, and discusses key preclinical considerations for the evaluation of ocular biologics.

EXPERT OPINION

The required frequency of dosing in the eye results in a great burden on both patients and the health care system, with direct intraocular administration remaining the most reliable and predictable route. Sustained and controlled ophthalmic drug delivery systems will go a long way in reducing this burden. Sustained delivery can directly dose target tissues, improving bioavailability and reducing off-target systemic effects. Maintaining stability and activity of compounds can prevent aggregation and enable extended duration of release, while sustaining dosage and preventing residual polymer after drug depletion.

Quantitative approaches in multimodal fundus imaging: State of the art and future perspectives

When it first appeared, multimodal fundus imaging revolutionized the diagnostic workup and provided extremely useful new insights into the pathogenesis of fundus diseases. The recent addition of quantitative approaches has further expanded the amount of information that can be obtained. In spite of the growing interest in advanced quantitative metrics, the scientific community has not reached a stable consensus on repeatable, standardized quantitative techniques to process and analyze the images. Furthermore, imaging artifacts may considerably affect the processing and interpretation of quantitative data, potentially affecting their reliability. The aim of this survey is to provide a comprehensive summary of the main multimodal imaging techniques, covering their limitations as well as their strengths. We also offer a thorough analysis of current quantitative imaging metrics, looking into their technical features, limitations, and interpretation. In addition, we describe the main imaging artifacts and their potential impact on imaging quality and reliability. The prospect of increasing reliance on artificial intelligence-based analyses suggests there is a need to develop more sophisticated quantitative metrics and to improve imaging technologies, incorporating clear, standardized, post-processing procedures. These measures are becoming urgent if these analyses are to cross the threshold from a research context to real-life clinical practice.

Retinal thickness changes in different subfields reflect the volume change of cerebral white matter hyperintensity

Purpose

To investigate the relationship between the retinal thickness in different subfields and the volume of white matter hyperintensity (WMH), with the hope to provide new evidence for the potential association between the retina and the brain.

Methods

A total of 185 participants aged over 40 years were included in our study. Magnetic resonance imaging (MRI) was used to image the WMH, and WMH volume was quantitatively measured by a specific toolbox. The thickness of the total retina, the retinal nerve fiber layer (RNFL), and the ganglion cell and inner plexiform layer (GCIP) was measured by optical coherence tomography (OCT) in nine subfields. The association between retinal thickness and WMH volume was demonstrated using binary logistic regression and Pearson correlation analysis.

Results

Participants were divided into two groups by the WMH volume (‰, standardized WMH volume) median. In the quartile-stratified binary logistic regression analysis, we found that the risk of higher WMH volume showed a positive linear trend correlation with the thickness of total retina (95% CI: 0.848 to 7.034; P for trend = 0.044)/ GCIP (95% CI: 1.263 to 10.549; P for trend = 0.038) at the central fovea, and a negative linear trend correlation with the thickness of nasal inner RNFL (95% CI: 0.086 to 0.787; P for trend = 0.012), nasal outer RNFL (95% CI: 0.058 to 0.561; P for trend = 0.004), and inferior outer RNFL (95% CI: 0.081 to 0.667; P for trend = 0.004), after adjusting for possible confounders. Correlation analysis results showed that WMH volume had a significant negative correlation with superior outer RNFL thickness (r = −0.171, P = 0.02) and nasal outer RNFL thickness (r = −0.208, P = 0.004).

Conclusion

It is suggested that central fovea and outer retina thickness are respectively associated with WMH volume. OCT may be a biological marker for early detection and longitudinal monitoring of WMH.

MULTI-SCALE DIRECTED ACYCLIC GRAPH-CNN FOR AUTOMATED CLASSIFICATION OF DIABETIC RETINOPATHY FROM OCT IMAGES

Diabetic Retinopathy (DR) is the principal cause of vision loss that interrupts the regular interaction of vascular, neural, and retinal constituents leading to impaired neuronal function and retinal abnormalities. Diagnosis of DR from Optical Coherence Tomography (OCT) image is difficult and time-consuming because several small features must be identified and graded, which results in a strenuous diagnosis when integrated with the complexity of the grading system. This study focuses on classifying DR from normal Spectral Domain-OCT (SD-OCT) images using the Directed Acyclic Graph (DAG) network without any pre-processing techniques. The proposed DAG-CNN model comprises 16 convolutional blocks, which learns multi-scale features automatically from multiple layers in the convolutional network and combines them effectively for the DR and normal prediction. The proposed model is tested on the public OCTID_DR and private LFH_DR SD-OCT databases containing DR and healthy OCT images. The model achieved an accuracy, precision, recall, F1-score, and AUC on OCTID_DR database of 0.9841, 0.9727, 0.9818, 0.9772, and 0.9836, respectively; and on LFH_DR database the respective values are 0.9988, 1, 0.9976, 0.9988, and 0.9988 with only 0.1569 Million of learnable parameters. This method significantly reduces the number of learnable parameters and the model’s computational complexity in terms of memory required and FLoating point OPerations (FLOPs). Guided Gradient-weighted Class Activation Mapping (Grad-CAM) is performed to highlight the regions of SD-OCT images that contribute to the decision of the classifier. Our model significantly surpasses the accuracy of the existing models with lower resource consumption and higher real-time performance.

Research on Semantic Segmentation Method of Macular Edema in Retinal OCT Images Based on Improved Swin-Unet

Optical coherence tomography (OCT), as a new type of tomography technology, has the characteristics of non-invasive, real-time imaging and high sensitivity, and is currently an important medical imaging tool to assist ophthalmologists in the screening, diagnosis, and follow-up treatment of patients with macular disease. In order to solve the problem of irregular occurrence area of diabetic retinopathy macular edema (DME), multi-scale and multi-region cluster of macular edema, which leads to inaccurate segmentation of the edema area, an improved Swin-Unet networks model was proposed for automatic semantic segmentation of macular edema lesion areas in OCT images. Firstly, in the deep bottleneck of the Swin-Unet network, the Resnet network layer was used to increase the extraction of pairs of sub-feature images. Secondly, the Swin Transformer block and skip connection structure were used for global and local learning, and the regions after semantic segmentation were morphologically smoothed and post-processed. Finally, the proposed method was performed on the macular edema patient dataset publicly available at Duke University, and was compared with previous segmentation methods. The experimental results show that the proposed method can not only improve the overall semantic segmentation accuracy of retinal macular edema, but also further to improve the semantic segmentation effect of multi-scale and multi-region edema regions.

Advances in the development of new biomarkers for Alzheimer's disease

Alzheimer's disease (AD) is a complex, heterogeneous, progressive disease and is the most common type of neurodegenerative dementia. The prevalence of AD is expected to increase as the population ages, placing an additional burden on national healthcare systems. There is a large need for new diagnostic tests that can detect AD at an early stage with high specificity at relatively low cost. The development of modern analytical diagnostic tools has made it possible to determine several biomarkers of AD with high specificity, including pathogenic proteins, markers of synaptic dysfunction, and markers of inflammation in the blood. There is a considerable potential in using microRNA (miRNA) as markers of AD, and diagnostic studies based on miRNA panels suggest that AD could potentially be determined with high accuracy for individual patients. Studies of the retina with improved methods of visualization of the fundus are also showing promising results for the potential diagnosis of the disease. This review focuses on the recent developments of blood, plasma, and ocular biomarkers for the diagnosis of AD.

Automatic Detection of Age-Related Macular Degeneration Based on Deep Learning and Local Outlier Factor Algorithm

Age-related macular degeneration (AMD) is a retinal disorder affecting the elderly, and society’s aging population means that the disease is becoming increasingly prevalent. The vision in patients with early AMD is usually unaffected or nearly normal but central vision may be weakened or even lost if timely treatment is not performed. Therefore, early diagnosis is particularly important to prevent the further exacerbation of AMD. This paper proposed a novel automatic detection method of AMD from optical coherence tomography (OCT) images based on deep learning and a local outlier factor (LOF) algorithm. A ResNet-50 model with L2-constrained softmax loss was retrained to extract features from OCT images and the LOF algorithm was used as the classifier. The proposed method was trained on the UCSD dataset and tested on both the UCSD dataset and Duke dataset, with an accuracy of 99.87% and 97.56%, respectively. Even though the model was only trained on the UCSD dataset, it obtained good detection accuracy when tested on another dataset. Comparison with other methods also indicates the efficiency of the proposed method in detecting AMD.

Clinical Evaluation of an Instrument-Integrated OCT-Based Distance Sensor for Robotic Vitreoretinal Surgery

PURPOSE

To assess the efficacy of an instrument-integrated OCT (iiOCT)-based distance sensor during robotic vitreoretinal surgery using the Preceyes Surgical System (PSS; Preceyes B.V.).

DESIGN

Single-center interventional study.

PARTICIPANTS

Patients requiring vitreoretinal surgery.

METHODS

Five patients were enrolled. Standard preoperative OCT images were obtained. After vitrectomy, a predefined set of actions was performed using the iiOCT-based sensor. Images then were processed to assess the signal-to-noise ratio (SNR) at various angles to the retina and at different distances between the instrument tip and the retinal surface. Preoperative and intraoperative OCT images were compared qualitatively and quantitatively.

MAIN OUTCOMES MEASURES

The feasibility in performing surgical tasks using the iiOCT-based sensor during vitreoretinal surgery, the SNR when imaging the retina, differences among intraoperative and preoperative OCT images, and characteristics of intraoperative retinal movements detected with the iiOCT-based probe.

RESULTS

Surgeons were able to perform all the tasks but one. The PSS was able to maintain a fixed distance. The SNR of the iiOCT-based sensor signal was adequate to determine the distance to the retina and to control the PSS. Analysis of iiOCT-based sensor A-scans identified 3 clearly distinguishable retinal layers, including the inner retinal boundary and the interface at the retinal pigment epithelium-Bruch's membrane. Thickness values differed by less than 5% from that measured by preoperative OCT, indicating its accuracy. The Fourier analysis of iiOCT-based sensor recordings identified anteroposterior retinal movements attributed to heartbeat and respiration.

CONCLUSIONS

This iiOCT-based sensor was tested successfully and promises reliable use during robot-assisted surgery. An iiOCT-based sensor is a promising step toward OCT-guided robotic retinal surgery.

Human Foveal Cone and Müller Cells Examined by Adaptive Optics Optical Coherence Tomography

Purpose

The purpose of this study was to image and investigate the foveal microstructure of human cone and Müller cells using adaptive optics-optical coherence tomography.

Methods

Six healthy subjects underwent the prototype adaptive optics-optical coherence tomography imaging, which allowed an axial resolution of 3.4 µm and a transverse resolution of approximately 3 µm. The morphological features of the individual retinal cells observed in the foveola were qualitatively and quantitatively evaluated.

Results

In the six healthy subjects, the image B-scans showed hyper-reflective dots that were densely packed in the outer nuclear layer. The mean number, diameter, and density of hyper-reflective dots in the foveola were 250.8 ± 59.6, 12.7 ± 59.6 µm, and 6966 ± 1833/mm2, respectively. These qualitative and quantitative findings regarding the hyper-reflective dots were markedly consistent with the morphological features of the foveal cone cell nuclei. Additionally, the images showed the funnel-shaped hyporeflective bodies running vertically and obliquely between the inner and external limiting membranes, illustrating the cell morphology of the foveal Müller cells.

Conclusions

Using adaptive optics, we succeeded in visualizing cross-sectional images of the individual cone and Müller cells of the human retina in vivo.

Translational Relevance

Adaptive optics-optical coherence tomography would help to improve our understanding of the pathogenesis of macular diseases.

Statistical modeling of retinal optical coherence tomography using the Weibull mixture model

In this paper, a novel statistical model is proposed for retinal optical coherence tomography (OCT) images. According to the layered structure of the retina, a mixture of six Weibull distributions is proposed to describe the main statistical features of OCT images. We apply Weibull distribution to establish a more comprehensive model but with fewer parameters that has better goodness of fit (GoF) than previous models. Our new model also takes care of features such as asymmetry and heavy-tailed nature of the intensity distribution of retinal OCT data. In order to test the effectiveness of this new model, we apply it to improve the low quality of the OCT images. For this purpose, the spatially constrained Gaussian mixture model (SCGMM) is implemented. Since SCGMM is designed for data with Gaussian distribution, we convert our Weibull mixture model to a Gaussian mixture model using histogram matching before applying SCGMM. The denoising results illustrate the remarkable performance in terms of the contrast to noise ratio (CNR) and texture preservation (TP) compared to other peer methods. In another test to evaluate the efficiency of our proposed model, the parameters and GoF criteria are considered as a feature vector for support vector machine (SVM) to classify the healthy retinal OCT images from pigment epithelial detachment (PED) disease. The confusion matrix demonstrates the impact of the proposed model in our preliminary study on the OCT classification problem.

OCT Variability Prevents Their Use as Robust Biomarkers in Multiple Sclerosis

Objective

To evaluate the agreement between the peripapillary retinal nerve fiber layer (pRNFL) and foveal thickness (FT) measurements among three different spectral domain-optical coherence tomography (SD-OCT) instruments in a sample of multiple sclerosis (MS) patients and a healthy age-matched control group.

Methods

An observational cross-sectional study with three groups: healthy subjects and MS patients w/w a previous clinical diagnosis of optic neuritis (ON) was conducted. The pRNFL and FT were measured using three different SD-OCT instruments (OCT PRIMUS 200 and OCT CIRRUS 500 SD-OCT [Carl Zeiss Meditec] and OCT 3D 2000 [Topcon]).

Results

Twenty eyes from 10 healthy subjects matched in age with MS patients without a previous history of eye disease and 62 MS eyes from 31 MS patients (29 eyes without history of ON and 33 eyes with history of ON) were enrolled. Healthy subjects and MS patients without ON did not show differences between the pRNFL and FT thickness (P>0.99) with any of the instruments. However, MS eyes with a previous episode of ON showed thinner pRNFL and FT (P<0.01). PRIMUS and CIRRUS OCT showed better agreement of the pRNLF and FT in both healthy and MS eyes. However, 3D OCT showed less agreement in the pRNFL measurement with CIRRUS in both healthy and MS eyes.

Interpretation

Although OCT is a valuable technology to improve MS patient assessment, differences between devices must be taken into account. It is necessary to create an international group that standardizes the measurement conditions and above all that provides reference bases for normal subjects.

Optical Coherence Tomography Can Be Used to Assess Glaucomatous Optic Nerve Damage in Most Eyes With High Myopia

PRECIS

It is generally assumed that optical coherence tomography (OCT) cannot be used to diagnose glaucomatous optic neuropathy (GON) in high myopes. However, this study presents evidence that there is sufficient information in OCT scans to allow for accurate diagnosis of GON in most eyes with high myopia.

PURPOSE

The purpose of this study was to test the hypothesis that glaucomatous damage can be accurately diagnosed in most high myopes via an assessment of the OCT results.

PATIENTS AND METHODS

One hundred eyes from 60 glaucoma patients or suspects, referred for OCT scans and evaluation, had corrected spherical refractive errors worse than -6 D and/or axial lengths ≥26.5 mm. An OCT specialist judged whether the eye had GON, based upon OCT circle scans of the disc and cube scans centered on the macula. A glaucoma specialist made the same judgement using all available information (eg, family history, repeat visits, intraocular pressure, 10-2 and 24-2 visual fields, OCT). A reference standard was created based upon the glaucoma specialist's classifications. In addition, the glaucoma specialist judged whether the eyes had peripapillary atrophy (PPA), epiretinal membrane (ERM), tilted disc (TD), and/or a paravascular inner retinal defect (PIRD).

RESULTS

The OCT specialist correctly identified 97 of the 100 eyes using the OCT information. In 63% of the cases, the inner circle scan alone was sufficient. For the rest, additional scans were requested. In addition, 81% of the total eyes had: PPA (79%), ERM (18%), PIRD (26%), and/or TD (48%).

CONCLUSIONS

For most eyes with high myopia, there is sufficient information in OCT scans to allow for accurate diagnosis of GON. However, the optimal use of the OCT will depend upon training to read OCT scans, which includes taking into consideration myopia related OCT artifacts and segmentation errors, as well as PPA, ERM, PIRD, and TD.

Gold nanoparticles to enhance ophthalmic imaging

The use of gold nanoparticles as diagnostic tools is burgeoning, especially in the cancer community with a focus on theranostic applications to both cancer diagnosis and treatment. Gold nanoparticles have also demonstrated great potential for use in diagnostic and therapeutic approaches in ophthalmology. Although many ophthalmic imaging modalities are available, there is still a considerable unmet need, in particular for ophthalmic molecular imaging for the early detection of eye disease before morphological changes are more grossly visible. An understanding of how gold nanoparticles are leveraged in other fields could inform new ways they could be utilized in ophthalmology. In this paper, we review current ophthalmic imaging techniques and then identify optical coherence tomography (OCT) and photoacoustic imaging (PAI) as the most promising technologies amenable to the use of gold nanoparticles for molecular imaging. Within this context, the development of gold nanoparticles as OCT and PAI contrast agents are reviewed, with the most recent developments described in detail.

Risk Factors for Recurrent Myopic Macular Hole Retinal Detachment after Silicone Oil Removal in Patients with Open Flat Macular Hole

OBJECTIVE

To evaluate the risks that might be associated with recurrent macular hole retinal detachment (MHRD) after silicone oil (S.O) removal in myopic patients with open flat macular hole (MH).

METHODS

In this retrospective series, we assessed the different factors that might be associated with recurrent MHRD after S.O removal in 48 eyes with open flat MH that underwent S.O removal after successful MHRD repair. We divided the enrolled eyes into 2 groups: group 1 included 38 eyes with flat open MH and flat retina after S.O removal, and group 2 included 10 eyes with flat open MH and recurrent MHRD after S.O removal.

RESULTS

Ten of 48 eyes (20.8%) with open flat MH developed recurrent MHRD after S.O removal. Univariate logistic regression analysis revealed that MH at the apex of PS, MH minimum diameter, hole form factor (HFF), and MH index (MHI) were significant risk factors for recurrent MHRD after S.O removal in myopic patients with open flat MH.

CONCLUSIONS

If there is a "flat open" MH that is large, located at the apex of PS, or with an HHF or MHI <0.9-0.5, there is a high chance of recurrent MHRD after S.O removal.

Retinal nerve fiber layer changes in migraine: a systematic review and meta-analysis

BACKGROUND

Migraine is one of the most common disabling diseases in the world. Its recurrent attacks may lead to abnormalities in the structure of the brain and retina. An increasing number of studies have investigated retinal nerve fiber layer (RNFL) thickness alterations in migraine by the optical coherence tomography (OCT); however, no consensus has yet reached.

METHOD

We searched Pubmed, Embase, and Web of Science databases to identify studies that investigated RNFL thickness in migraine by OCT measurement and performed a meta-analysis of eligible studies.

RESULTS

Twenty-six studies were included in the meta-analysis, comprising 1530 migraine patients and 1105 healthy controls. The mean RNFL thickness was thinner in the migraine group compared to the control group (SMD =- 0.53). In the subgroup analyses, RNFL thickness were decreased most significantly in the superior (SMD = - 0.71) and inferior (SMD = - 0.63) quadrants among all quadrants. Migraine with aura (SMD = - 0.91) showed a greater effect size of RNFL thickness reduction than migraine without aura (SMD =- 0.47). Spectral-domain OCT (SMD = - 0.55) seems more sensitive to detect RNFL thickness reduction than time-domain OCT (SMD = - 0.44). In addition, age, sex, disease duration, attack frequency, and intraocular pressure were not significantly associated with RNFL thickness.

CONCLUSIONS

The findings from our comprehensive meta-analysis with large datasets strengthen the clinical evidence of the RNFL thickness reduction in migraine. RNFL thickness via spectral-domain OCT measurement demonstrates the potential role in differentiating patients with migraine, especially migraine with aura, from healthy controls.

Correlations among metamorphopsia test scores, optical coherence tomography findings and multifocal electroretinogram responses in epiretinal membrane patients

PURPOSE

To quantify metamorphopsia with a novel objective method in patients with epiretinal membrane (ERM) and to compare the relationships among metamorphopsia scores, spectral-domain optical coherence tomography (OCT) findings, and multifocal electroretinogram (mfERG) results.

METHODS

This study included 52 eyes of 52 patients with idiopathic ERM who underwent comprehensive ophthalmologic examinations, including measurement of best-corrected visual acuity (BCVA), OCT, and mfERG. The degree of metamorphopsia was quantified using MonPack One® (Metrovision, Perenchies, France). On the topographic map of the early treatment diabetic retinopathy (ETDRS) grid, retinal thickness in the central, superior, inferior, nasal, and temporal subfields were measured, and metamorphopsia scores for each corresponding subfield were also obtained. The amplitudes and implicit times of mERG were elicited from each subfield. Then, the correlations among metamorphopsia scores, OCT findings, and mfERG responses were analyzed.

RESULTS

The mean age of the patients was 65.3 ± 18.5 y, and the average metamorphopsia score of the individual subfields was 2.03 ± 1.18. Initial BCVA was 0.50 ± 0.12 logMAR, but there was no significant correlation between metamorphopsia scores and BCVA. The metamorphopsia scores from the central subfields showed significant correlations with central retinal thickness (CRT) (p = 0.001). The mean metamorphopsia scores in the central subfield showed a significant relationship with the mean N1 and P1 amplitudes (p = 0.001, p = 0.048, respectively), while no relationship was observed between metamorphopsia scores and mfERG amplitudes in other subfields.

CONCLUSIONS

The degree of metamorphopsia in patients with ERM could be objectively quantified in each subfield using a novel metamorphopsia test. The metamorphopsia scores were significantly correlated with retinal thickness, especially at the central subfields, and the scores in the central subfields were significantly correlated with the N1 and P1 amplitudes of mfERG. Thus, the metamorphopsia test can be a useful method to evaluate metamorphopsia symptoms for patients with ERM.

Quantitative measurement of mechanical properties in wound healing processes in a corneal stroma model by using vibrational optical coherence elastography (OCE)

Corneal wound healing, caused by frequent traumatic injury to the cornea and increasing numbers of refractive surgeries, has become a vital clinical problem. In the cornea, wound healing is an extremely complicated process. However, little is known about how the biomechanical changes in wound healing response of the cornea. Collagen-based hydrogels incorporating corneal cells are suitable for replicating a three-dimensional (3D) equivalent of the cornea in-vitro. In this study, the mechanical properties of corneal stroma models were quantitatively monitored by a vibrational optical coherence elastography (OCE) system during continuous culture periods. Specifically, human corneal keratocytes were seeded at 5 × 10⁵ cells/mL in the hydrogels with a collagen concentration of 3.0 mg/mL. The elastic modulus of the unwounded constructs increased from 2.950 ± 0.2 kPa to 11.0 ± 1.4 kPa, and the maximum thickness decreased from 1.034 ± 0.1 mm to 0.464 ± 0.09 mm during a 15-day culture period. Furthermore, a traumatic wound in the construct was introduced with a size of 500 µm. The elastic modulus of the neo-tissue in the wound area increased from 1.488 ± 0.4 kPa to 6.639 ± 0.3 kPa over 13 days. This study demonstrates that the vibrational OCE system is capable of quantitative monitoring the changes in mechanical properties of a corneal stroma wound model during continuous culture periods and improves our understanding on corneal wound healing processes.

Improving statistical power of glaucoma clinical trials using an ensemble of cyclical generative adversarial networks

Albeit spectral-domain OCT (SDOCT) is now in clinical use for glaucoma management, published clinical trials relied on time-domain OCT (TDOCT) which is characterized by low signal-to-noise ratio, leading to low statistical power. For this reason, such trials require large numbers of patients observed over long intervals and become more costly. We propose a probabilistic ensemble model and a cycle-consistent perceptual loss for improving the statistical power of trials utilizing TDOCT. TDOCT are converted to synthesized SDOCT and segmented via Bayesian fusion of an ensemble of GANs. The final retinal nerve fibre layer segmentation is obtained automatically on an averaged synthesized image using label fusion. We benchmark different networks using i) GAN, ii) Wasserstein GAN (WGAN) (iii) GAN + perceptual loss and iv) WGAN + perceptual loss. For training and validation, an independent dataset is used, while testing is performed on the UK Glaucoma Treatment Study (UKGTS), i.e. a TDOCT-based trial. We quantify the statistical power of the measurements obtained with our method, as compared with those derived from the original TDOCT. The results provide new insights into the UKGTS, showing a significantly better separation between treatment arms, while improving the statistical power of TDOCT on par with visual field measurements.

Correlation between retinal nerve fiber layer thickness and IOP variation in glaucoma suspects and patients with primary open-angle glaucoma

PURPOSE

To analyze the relationship between retinal nerve fiber layer thickness (RNFLT) and intraocular pressure (IOP) variation in glaucoma suspects (GS) and patients with primary open-angle glaucoma (POAG).

METHODS

Thirty-one GS and 34 POAG patients underwent ophthalmologic examination and 24-h IOP measurements. GS had IOPs ranging from 19 to 24 mmHg and/or suspicious appearance of the optic nerve. POAG patients had reproducible abnormal visual fields. We only included patients who presented with short-term IOP fluctuation >6 mm Hg (∆IOP). Only one eye per patient was included through a randomized process. Peripapillary RNFLT was assessed by spectral-domain optical coherence tomography. We correlated RNFLT with IOP parameters.

RESULTS

Mean IOP was similar between GS and POAG groups (15.6 ± 3.47 vs 15.6 ± 2.83 mmHg, p = 0.90) as was IOP peak at 6 AM (21.7 ± 3.85 vs 21.3 ± 3.80 mmHg, p = 0.68). Statistically significant negative correlations were found in POAG group between IOP at 6 AM and RNFLT in global (r_s = -0.543; p < 0.001), inferior (r_s = -0.540; p < 0.001), superior (r_s = -0.405; p = 0.009), and nasal quadrants (r_s = -0.561; p < 0.001). Negative correlations were also found between ∆IOP and RNFLT in global (r_s = -0.591; p < 0.001), and all other sectors (p < 0.05). In GS IOP at 6 AM correlated only with inferior quadrant (r_s = -0.307; p = 0.047).

CONCLUSION

IOP at 6 AM and ∆IOP had negative correlations with RNFLT quadrants in POAG. In GS this correlation occurred between IOP at 6 AM and inferior quadrant. These findings may indicate potential risk factors for glaucoma progression.

Choroidal changes in intermediate age-related macular degeneration patients with drusen or pseudodrusen

Reticular pseudodrusen are associated with a thinner choroid. The aim of our study was to determine the differences in central choroidal thickness and choriocapillaris vascular flow area between eyes with and without reticular pseudodrusen using swept-source optical coherence tomography and swept-source optical coherence tomography angiography. We conducted a retrospective case control study which included 27 eyes from 27 consecutive patients with intermediate age-related macular degeneration and 17 eyes from 17 healthy participants. Complete ophthalmic examinations were carried out including axial length measurements; fundus color retinography; fundus autofluorescence; swept-source optical coherence tomography and swept-source optical coherence tomography angiography; central choroidal thickness and choriocapillaris vascular flow area. Patients were classified as no reticular pseudodrusen, mild reticular pseudodrusen, and severe reticular pseudodrusen. Mean central choroidal thickness in patients exhibiting severe reticular pseudodrusen (110 ± 56 μm) was significantly smaller than in patients with no reticular pseudodrusen (201 ± 76 μm, p < 0.01). Mean choriocapillaris vascular flow area in severe reticular pseudodrusen patients (45.2% ± 3.0%) was also significantly less than in patients with no (47.9% ± 1.6%, p < 0.001) and mild reticular pseudodrusen (47.7% ± 1.0%, p < 0.05). Stepwise multiple regression models confirmed the association of reticular pseudodrusen with central choroidal thickness (p < 0.001) and choriocapillaris vascular flow area (p < 0.01) even after accounting for age, axial length, and refractive error. Soft drusen were not associated with changes in either central choroidal thickness (p = 0.13) nor choriocapillaris vascular flow area (p = 0.29). A significant, positive relationship was found between central choroidal thickness and choriocapillaris vascular flow area (r = 0.44, p = 0.01). Therefore, both central choroidal thickness and choriocapillaris vascular flow area are decreased in eyes with reticular pseudodrusen, as compared to healthy eyes and intermediate age-related macular degeneration eyes not exhibiting reticular pseudodrusen. In addition, central choroidal thickness and choriocapillaris vascular flow area are related, and the reduction of either is directly associated to the severity of reticular pseudodrusen. Further studies are needed to assess the clinical significance of these findings.

Quality factor based correction for SD-OCT measurements in cataract patients

PURPOSE

To correct peripapillary retinal nerve fibre layer (pRNFL) measurements performed with spectral domain optical coherence tomography (SD-OCT) for low image quality factor (QF) in patients with cataract, using measurements before and after cataract surgery.

METHODS

SD-OCT (Topcon 3DOCT-2000) volume scans of the optic disc of 13 cataract patients were used. A set of three reflective filters with optical density ranging from 0.11 to 0.54 were used. The correlation was calculated between the change in thickness measurements and the change in image quality factor. Changes before and after cataract surgery were analysed. A correction for scans with a lower QF was calculated using an equation which was formulated based on the relationship between the change in thickness measurements and the change in image quality factor.

RESULTS

Thirteen right eyes of thirteen cataract patients were included in this study. pRNFL thickness measurements before and after cataract differed significantly (96 versus 99 micron, p < 0.01). Preoperative linear regression lines showed a different slope than postoperative regression lines. Corrected pRNFL thickness measurements of before cataract surgery differed significantly with pRNFL thickness measurements after cataract surgery.

CONCLUSIONS

The presence of cataract influences the QF-pRNFL relationship. The lower the image QF, the higher the pRNFL thickness underestimation. We found a rather curvilinear relationship between QF and pRNFL. Our corrected measurements of the pRNFL thickness in case of lower image QF due to cataract still differed significantly from the pRNFL thickness measurements after cataract surgery.

Interpretation of OCT and OCTA images from a histological approach: Clinical and experimental implications

Optical coherence tomography (OCT) and OCT angiography (OCTA) have been a technological breakthrough in the diagnosis, treatment, and follow-up of many retinal diseases, thanks to its resolution and its ability to inform of the retinal state in seconds, which gives relevant information about retinal degeneration. In this review, we present an immunohistochemical description of the human and mice retina and we correlate it with the OCT bands in health and pathological conditions. Here, we propose an interpretation of the four outer hyperreflective OCT bands with a correspondence to retinal histology: the first and innermost band as the external limiting membrane (ELM), the second band as the cone ellipsoid zone (EZ), the third band as the outer segment tips phagocytosed by the pigment epithelium (PhaZ), and the fourth band as the mitochondria in the basal portion of the RPE (RPEmitZ). The integrity of these bands would reflect the health of photoreceptors and retinal pigment epithelium. Moreover, we describe how the vascular plexuses vary in different regions of the healthy human and mice retina, using OCTA and immunohistochemistry. In humans, four, three, two or one plexuses can be observed depending on the distance from the fovea. Also, specific structures such as vascular loops in the intermediate capillary plexus, or spider-like structures of interconnected capillaries in the deep capillary plexus are found. In mice, three vascular plexuses occupy the whole retina, except in the most peripheral retina where only two plexuses are found. These morphological issues should be considered when assessing a pathology, as some retinal diseases are associated with structural changes in blood vessels. Therefore, the analysis of OCT bands and OCTA vascular plexuses may be complementary for the diagnosis and prognosis of retinal degenerative processes, useful to assess therapeutic approaches, and it is usually correlated to visual acuity.

Medical Image Enhancement Using Super Resolution Methods

Deep Learning image processing methods are gradually gaining popularity in a number of areas including medical imaging. Classification, segmentation, and denoising of images are some of the most demanded tasks. In this study, we aim at enhancing optic nerve head images obtained by Optical Coherence Tomography (OCT). However, instead of directly applying noise reduction techniques, we use multiple state-of-the-art image Super-Resolution (SR) methods. In SR, the low-resolution (LR) image is upsampled to match the size of the high-resolution (HR) image. With respect to image enhancement, the upsampled LR image can be considered as low quality, noisy image, and the HR image would be the desired enhanced version of it. We experimented with several image SR architectures, such as super-resolution Convolutional Neural Network (SRCNN), very deep Convolutional Network (VDSR), deeply recursive Convolutional Network (DRCN), and enhanced super-resolution Generative Adversarial Network (ESRGAN). Quantitatively, in terms of peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM), the SRCNN, VDSR, and DRCN significantly improved the test images. Although the ERSGAN showed the worst PSNR and SSIM, qualitatively, it was the best one.

Automated Deformation-Based Analysis of 3D Optical Coherence Tomography in Diabetic Retinopathy

Diabetic retinopathy (DR) is a significant microvascular complication of diabetes mellitus and a leading cause of vision impairment in working age adults. Optical coherence tomography (OCT) is a routinely used clinical tool to observe retinal structural and thickness alterations in DR. Pathological changes that alter the normal anatomy of the retina, such as intraretinal edema, pose great challenges for conventional layer-based analysis of OCT images. We present an alternative approach for the automated analysis of OCT volumes in DR research based on nonlinear registration. In this paper, we first obtain an anatomically consistent volume of interest (VOI) in different OCT images via carefully designed masking and affine registration. After that, efficient B-spline transformations are computed using stochastic gradient descent optimization. Using the OCT volumes of normal controls, for which layer-based segmentation works well, we demonstrate the accuracy of our registration-based analysis in aligning layer boundaries. By nonlinearly registering the OCT volumes of DR subjects to an atlas constructed from normal controls and measuring the Jacobian determinant of the deformation, we can simultaneously visualize tissue contraction and expansion due to DR pathology. Tensor-based morphometry (TBM) can also be performed for quantitative analysis of local structural changes. In our experimental results, we apply our method to a dataset of 105 subjects and demonstrate that volumetric OCT registration and TBM analysis can successfully detect local retinal structural alterations due to DR.

Deep learning for quality assessment of retinal OCT images

Optical coherence tomography (OCT) is a promising high-speed, non-invasive imaging modality providing high-resolution retinal scans. However, a variety of external factors such as light occlusion and patient movement can seriously degrade OCT image quality, which complicates manual retinopathy detection and computer-aided diagnosis. As such, this study first presents an OCT image quality assessment (OCT-IQA) system, capable of automatic classification based on signal completeness, location, and effectiveness. Four CNN architectures (VGG-16, Inception-V3, ResNet-18, and ResNet-50) from the ImageNet classification task were used to train the proposed OCT-IQA system via transfer learning. The ResNet-50 with the best performance was then integrated into the final OCT-IQA network. The usefulness of this approach was evaluated using retinopathy detection results. A retinopathy classification network was first trained by fine-tuning Inception-V3 model. The model was then applied to two test datasets, created randomly from the original dataset, one of which was screened by the OCT-IQA system and only included high quality images while the other was mixed by high and low quality images. Results showed that retinopathy detection accuracy and area under curve (AUC) were 3.75% and 1.56% higher, respectively, for the filtered data (compared with the unfiltered data). These experimental results demonstrate the effectiveness of the proposed OCT-IQA system and suggest that deep learning could be applied to the design of computer-aided systems (CADSs) for automatic retinopathy detection.

A Deep Learning Approach to Denoise Optical Coherence Tomography Images of the Optic Nerve Head

Optical coherence tomography (OCT) has become an established clinical routine for the in vivo imaging of the optic nerve head (ONH) tissues, that is crucial in the diagnosis and management of various ocular and neuro-ocular pathologies. However, the presence of speckle noise affects the quality of OCT images and its interpretation. Although recent frame-averaging techniques have shown to enhance OCT image quality, they require longer scanning durations, resulting in patient discomfort. Using a custom deep learning network trained with 2,328 'clean B-scans' (multi-frame B-scans; signal averaged), and their corresponding 'noisy B-scans' (clean B-scans + Gaussian noise), we were able to successfully denoise 1,552 unseen single-frame (without signal averaging) B-scans. The denoised B-scans were qualitatively similar to their corresponding multi-frame B-scans, with enhanced visibility of the ONH tissues. The mean signal to noise ratio (SNR) increased from 4.02 ± 0.68 dB (single-frame) to 8.14 ± 1.03 dB (denoised). For all the ONH tissues, the mean contrast to noise ratio (CNR) increased from 3.50 ± 0.56 (single-frame) to 7.63 ± 1.81 (denoised). The mean structural similarity index (MSSIM) increased from 0.13 ± 0.02 (single frame) to 0.65 ± 0.03 (denoised) when compared with the corresponding multi-frame B-scans. Our deep learning algorithm can denoise a single-frame OCT B-scan of the ONH in under 20 ms, thus offering a framework to obtain superior quality OCT B-scans with reduced scanning times and minimal patient discomfort.

Correlation between B-scan optical coherence tomography, en face thickness map ring and hyperautofluorescent ring in retinitis pigmentosa patients

OBJECTIVE

To evaluate and compare the B-scan OCT loss of ellipsoid zone, OCT en face thickness map constriction, and hyperautofluorescent ring constriction in RP patients.

METHODS

Retrospective case series study. Forty-eight eyes of 24 RP patients with a parafoveal hyperautofluorescent ring were studied. The diagnosis of RP was established by the presence of rod response impairment and a prevalent decrease of scotopic over photopic responses on electroretinography. The FAF and spectral-domain optical coherence tomography (SD-OCT) images were obtained from 24 patients with RP. The measurements of the EZ line width on B-scan OCT, hyperautofluorescent ring area on FAF, and hyperautofluorescent ring area on en face thickness map were performed by two independent graders. The measurements of these three parameters were correlated.

RESULTS

The mean age of study patients was 46 years old (sd = 19). The external and internal FAF rings involving the fovea were identified in all study eyes. The area of the thickness ring decreased at an average rate of 0.5 (sd 0.4) mm² per year (P < 0.001). The average rate of EZ-line constriction was estimated to be 123 (sd 63) μm per year (P < 0.001). The hyperautofluorescent ring area decreased at an average rate of 0.9 (sd 0.98) mm² per year (P < 0.001). The strongest correlation was observed between hyperautofluorescent ring area and EZ-line width (r = 0.78).

CONCLUSIONS

We observed that the hyperautofluorescent ring area exhibits a faster progression rate than the area of the thickness ring. In addition, we found that the EZ-line width had a high positive correlation with the hyperautofluorescent ring area and a moderate positive correlation with area of the thickness ring.

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

TOPIC

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

CLINICAL RELEVANCE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Optimized Deep Convolutional Neural Networks for Identification of Macular Diseases from Optical Coherence Tomography Images

Finetuning pre-trained deep neural networks (DNN) delicately designed for large-scale natural images may not be suitable for medical images due to the intrinsic difference between the datasets. We propose a strategy to modify DNNs, which improves their performance on retinal optical coherence tomography (OCT) images. Deep features of pre-trained DNN are high-level features of natural images. These features harm the training of transfer learning. Our strategy is to remove some deep convolutional layers of the state-of-the-art pre-trained networks: GoogLeNet, ResNet and DenseNet. We try to find the optimized deep neural networks on small-scale and large-scale OCT datasets, respectively, in our experiments. Results show that optimized deep neural networks not only reduce computational burden, but also improve classification accuracy.

Longitudinal Morphological and Physiological Monitoring of Three-dimensional Tumor Spheroids Using Optical Coherence Tomography

Tumor spheroids have been developed as a three-dimensional (3D) cell culture model in cancer research and anti-cancer drug discovery. However, currently, high-throughput imaging modalities utilizing bright field or fluorescence detection, are unable to resolve the overall 3D structure of the tumor spheroid due to limited light penetration, diffusion of fluorescent dyes and depth-resolvability. Recently, our lab demonstrated the use of optical coherence tomography (OCT), a label-free and non-destructive 3D imaging modality, to perform longitudinal characterization of multicellular tumor spheroids in a 96-well plate. OCT was capable of obtaining 3D morphological and physiological information of tumor spheroids growing up to about 600 µm in height. In this article, we demonstrate a high-throughput OCT (HT-OCT) imaging system that scans the whole multi-well plate and obtains 3D OCT data of tumor spheroids automatically. We describe the details of the HT-OCT system and construction guidelines in the protocol. From the 3D OCT data, one can visualize the overall structure of the spheroid with 3D rendered and orthogonal slices, characterize the longitudinal growth curve of the tumor spheroid based on the morphological information of size and volume, and monitor the growth of the dead-cell regions in the tumor spheroid based on optical intrinsic attenuation contrast. We show that HT-OCT can be used as a high-throughput imaging modality for drug screening as well as characterizing biofabricated samples.

Anterior segment optical coherence tomography and its clinical applications

Anterior segment optical coherence tomography (AS-OCT) has become one of the cornerstones of non-contact imaging modalities for assessing such structures as the cornea, anterior chamber angle, aqueous outflow pathway, sclera, and ocular surface structures. As such, it has a broad range of clinical applications, which have been independently reported in the literature. This paper aims to present a review of extant literature on the utility of AS-OCT and its efficacy in clinical applications, and to evaluate the quality of available evidence. The following databases were searched from inception to 24 June 2018: Medline via Ovid, Cochrane Central Register of Controlled Trials, PubMed, World Health Organization International Clinical Trials Registry Platform, EMBASE, and CINAHL. Bibliographies of identified papers were hand searched. Inclusion criteria: articles describing or assessing the use of OCT for visualising the AS. The authors excluded studies without an identified primary outcome variable. One author independently selected studies, extracted data, and assessed for risk of bias using PRISMA guidelines. This review included 82 studies, of which there were 11 cohort studies, 37 case series, 10 case studies, 21 comparative observational studies, and three non-systematic review articles. Primary outcome variables included anterior chamber angle, angle opening distance, angle recess area, trabecular iris angle, trabecula-iris space area, corneal thickness, tear meniscus height, tear meniscus area, tear meniscus volume, and the morphology of AS structures, including the ocular surface, blebs, flaps, and graft sites. This review attempts to encompass the breadth and depth of evidence for AS-OCT in the arena of diagnostics, therapeutics, and prognostics. At the same time, it brings to light the dearth of high-level evidence on this topic, suggesting the important role of randomised controlled trials and meta-analyses for the future validation of this technology.

BKC and CME: Is benzalkonium chloride hindering our efforts to achieve the desired postoperative visual acuity?

PURPOSE

To evaluate and compare the change in postoperative central macular thickness in patients receiving benzalkonium chloride (BKC)-preserved and BKC-free medications after uneventful phacoemulsification.

SETTING

V.M.M.C & Safdarjung Hospital, New Delhi (a tertiary health care hospital).

STUDY DESIGN

Prospective randomized comparative observational study.

MATERIALS AND METHODS

Once patients were selected, the baseline standard ophthalmic examination was done.

SAMPLE SIZE

140 eyes were enrolled and randomly divided into two groups. (a) Group I: receive BKC-preserved topical medications and (b) Group II: receive BKC-free topical medications of same constituents postoperatively. Group I patients received topical BKC-preserved moxifloxacin 0.5% + dexamethasone 0.1% eye drops six times a day, timolol maleate 0.5% twice daily, tropicamide 0.8% + phenylephrine 5% once a day for 6 weeks, and Group II received same BKC-free topical eye drops for 6 weeks. Postoperatively, the patients were reviewed at day 1, week 1, week 6 for same parameters.

STATISTICS

Quantitative variables: paired and unpaired t test. p value < 0.05 was considered statistically significant.

RESULTS

The mean CMT in μm at 1 week in Group I was 269.39 ± 14.56 and in Group II was 270.04 ± 6.56. The mean CMT in µm at 6 weeks in Group I was 270.39 ± 17.18 and in Group II was 270.90 ± 7.00.

CONCLUSION

Neither do BKC-preserved topical medications have any independent role in increasing the central macular thickness after uneventful surgery nor do they have any role in causing pseudophakic CME.

Confocal microscopy of corneal nerve plexus as an early marker of eye involvement in patients with type 2 diabetes

PURPOSE

To measure the thickness and length of corneal nerves and the peri-papillary retinal nerve fiber layer (RNFL) thickness in patients recently diagnosed with diabetes mellitus (DM).

METHODS

Twenty-two eyes of 22 patients recently diagnosed with type 2 DM and 22 eyes of 22 healthy individuals were consecutively enrolled. Central corneal sensitivity was measured using a Cochet-Bonnet esthesiometer, and corneal nerve length (CNL) and thickness (CNT) were evaluated through in vivo confocal microscopy. The confocal images were examined using software that could semi-automatically trace the corneal nerve pathway. Spectral domain optical coherence tomography (SD-OCT) was performed to quantify the overall and sectorial RNFL thickness.

RESULTS

Mean DM duration was 3.5 ± 1.7 months, whereas the mean glycemia and HbA1c levels were 180.5 ± 73.13 mg/dl and 8.6 ± 1.7% (65.2 ± 19.7 mmol/mol), respectively. Corneal sensation threshold was significantly lower in the DM group compared to control group (p = 0.003). CNL and CNT were reduced in the DM group (p = 0.043 and p = 0.004, respectively). Significant correlations were found between CNT and HbA1c levels (p = 0.04; r = -0.47), and between CNT and the corneal sensation threshold (p = 0.04; r = 0.69). RNFL thickness was significantly reduced in the temporal quadrants, but no correlation was found with CNT and CNL changes (p > 0.05).

CONCLUSIONS

CNL and CNT changes are evident even in the early stages of DM, and RNFL reduction was recorded in the temporal quadrants. These findings indicate that, in the eye with diabetes, neuropathy may represent an early marker of the disease.

Macular OCT Classification Using a Multi-Scale Convolutional Neural Network Ensemble

Computer-aided diagnosis (CAD) of retinal pathologies is a current active area in medical image analysis. Due to the increasing use of retinal optical coherence tomography (OCT) imaging technique, a CAD system in retinal OCT is essential to assist ophthalmologist in the early detection of ocular diseases and treatment monitoring. This paper presents a novel CAD system based on a multi-scale convolutional mixture of expert (MCME) ensemble model to identify normal retina, and two common types of macular pathologies, namely, dry age-related macular degeneration, and diabetic macular edema. The proposed MCME modular model is a data-driven neural structure, which employs a new cost function for discriminative and fast learning of image features by applying convolutional neural networks on multiple-scale sub-images. MCME maximizes the likelihood function of the training data set and ground truth by considering a mixture model, which tries also to model the joint interaction between individual experts by using a correlated multivariate component for each expert module instead of only modeling the marginal distributions by independent Gaussian components. Two different macular OCT data sets from Heidelberg devices were considered for the evaluation of the method, i.e., a local data set of OCT images of 148 subjects and a public data set of 45 OCT acquisitions. For comparison purpose, we performed a wide range of classification measures to compare the results with the best configurations of the MCME method. With the MCME model of four scale-dependent experts, the precision rate of 98.86%, and the area under the receiver operating characteristic curve (AUC) of 0.9985 were obtained on average.