Plexus-specific retinal vascular anatomy and pathologies as seen by projection-resolved optical coherence tomographic angiography

Optical coherence tomographic angiography (OCTA) is a novel technology capable of imaging retinal vasculature three-dimensionally at capillary scale without the need to inject any extrinsic dye contrast. However, projection artifacts cause superficial retinal vascular patterns to be duplicated in deeper layers, thus interfering with the clean visualization of some retinal plexuses and vascular pathologies. Projection-resolved OCTA (PR-OCTA) uses post-processing algorithms to reduce projection artifacts. With PR-OCTA, it is now possible to resolve up to 4 distinct retinal vascular plexuses in the living human eye. The technology also allows us to detect and distinguish between various retinal and optic nerve diseases. For example, optic nerve diseases such as glaucoma primarily reduces the capillary density in the superficial vascular complex, which comprises the nerve fiber layer plexus and the ganglion cell layer plexus. Outer retinal diseases such as retinitis pigmentosa primarily reduce the capillary density in the deep vascular complex, which comprises the intermediate capillary plexus and the deep capillary plexus. Retinal vascular diseases such as diabetic retinopathy and vein occlusion affect all plexuses, but with different patterns of capillary loss and vascular malformations. PR-OCTA is also useful in distinguishing various types of choroidal neovascularization and monitoring their response to anti-angiogenic medications. In retinal angiomatous proliferation and macular telangiectasia type 2, PR-OCTA can trace the pathologic vascular extension into deeper layers as the disease progress through stages. Plexus-specific visualization and measurement of retinal vascular changes are improving our ability to diagnose, stage, monitor, and assess treatment response in a wide variety of optic nerve and retinal diseases. These applications will be further enhanced with the continuing improvement of the speed and resolution of the OCT platforms, as well as the development of software algorithms to reduce artifacts, improve image quality, and make quantitative measurements.

Measuring lipid membrane viscosity using rotational and translational probe diffusion

The two-dimensional fluidity of lipid bilayers enables the motion of membrane-bound macromolecules and is therefore crucial to biological function. Microrheological methods that measure fluid viscosity via the translational diffusion of tracer particles are challenging to apply and interpret for membranes, due to uncertainty about the local environment of the tracers. Here, we demonstrate a new technique in which determination of both the rotational and translational diffusion coefficients of membrane-linked particles enables quantification of viscosity, measurement of the effective radii of the tracers, and assessment of theoretical models of membrane hydrodynamics. Surprisingly, we find a wide distribution of effective tracer radii, presumably due to a variable number of lipids linked to each tracer particle. Furthermore, we show for the first time that a protein involved in generating membrane curvature, the vesicle trafficking protein Sar1p, dramatically increases membrane viscosity. Using the rheological method presented here, therefore, we are able to reveal a class of previously unknown couplings between protein activity and membrane mechanics.

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.

Automated diagnosis and segmentation of choroidal neovascularization in OCT angiography using deep learning

Accurate identification and segmentation of choroidal neovascularization (CNV) is essential for the diagnosis and management of exudative age-related macular degeneration (AMD). Projection-resolved optical coherence tomographic angiography (PR-OCTA) enables both cross-sectional and en face visualization of CNV. However, CNV identification and segmentation remains difficult even with PR-OCTA due to the presence of residual artifacts. In this paper, a fully automated CNV diagnosis and segmentation algorithm using convolutional neural networks (CNNs) is described. This study used a clinical dataset, including both scans with and without CNV, and scans of eyes with different pathologies. Furthermore, no scans were excluded due to image quality. In testing, all CNV cases were diagnosed from non-CNV controls with 100% sensitivity and 95% specificity. The mean intersection over union of CNV membrane segmentation was as high as 0.88. By enabling fully automated categorization and segmentation, the proposed algorithm should offer benefits for CNV diagnosis, visualization monitoring.

Development and validation of a deep learning algorithm for distinguishing the nonperfusion area from signal reduction artifacts on OCT angiography

The capillary nonperfusion area (NPA) is a key quantifiable biomarker in the evaluation of diabetic retinopathy (DR) using optical coherence tomography angiography (OCTA). However, signal reduction artifacts caused by vitreous floaters, pupil vignetting, or defocus present significant obstacles to accurate quantification. We have developed a convolutional neural network, MEDnet-V2, to distinguish NPA from signal reduction artifacts in 6×6 mm² OCTA. The network achieves strong specificity and sensitivity for NPA detection across a wide range of DR severity and scan quality.

Artifacts and artifact removal in optical coherence tomographic angiography

Optical coherence tomographic angiography (OCTA) enables rapid imaging of retinal vasculature in three dimensions. While the technique has provided quantification of healthy vessels as well as pathology in several diseases, it is not unusual for OCTA data to contain artifacts that may influence measurement outcomes or defy image interpretation. In this review, we discuss the sources of several OCTA artifacts-including projection, motion, and signal reduction-as well as strategies for their removal. Artifact compensation can improve the accuracy of OCTA measurements, and the most effective use of the technology will incorporate hardware and software that can perform such correction.

Automated Segmentation of Retinal Fluid Volumes From Structural and Angiographic Optical Coherence Tomography Using Deep Learning

Purpose

We proposed a deep convolutional neural network (CNN), named Retinal Fluid Segmentation Network (ReF-Net), to segment retinal fluid in diabetic macular edema (DME) in optical coherence tomography (OCT) volumes.

Methods

The 3- × 3-mm OCT scans were acquired on one eye by a 70-kHz OCT commercial AngioVue system (RTVue-XR; Optovue, Inc., Fremont, CA, USA) from 51 participants in a clinical diabetic retinopathy (DR) study (45 with retinal edema and six healthy controls, age 61.3 ± 10.1 (mean ± SD), 33% female, and all DR cases were diagnosed as severe NPDR or PDR). A CNN with U-Net-like architecture was constructed to detect and segment the retinal fluid. Cross-sectional OCT and angiography (OCTA) scans were used for training and testing ReF-Net. The effect of including OCTA data for retinal fluid segmentation was investigated in this study. Volumetric retinal fluid can be constructed using the output of ReF-Net. Area-under-receiver-operating-characteristic-curve, intersection-over-union (IoU), and F1-score were calculated to evaluate the performance of ReF-Net.

Results

ReF-Net shows high accuracy (F1 = 0.864 ± 0.084) in retinal fluid segmentation. The performance can be further improved (F1 = 0.892 ± 0.038) by including information from both OCTA and structural OCT. ReF-Net also shows strong robustness to shadow artifacts. Volumetric retinal fluid can provide more comprehensive information than the two-dimensional (2D) area, whether cross-sectional or en face projections.

Conclusions

A deep-learning-based method can accurately segment retinal fluid volumetrically on OCT/OCTA scans with strong robustness to shadow artifacts. OCTA data can improve retinal fluid segmentation. Volumetric representations of retinal fluid are superior to 2D projections.

Translational Relevance

Using a deep learning method to segment retinal fluid volumetrically has the potential to improve the diagnostic accuracy of diabetic macular edema by OCT systems.

Reconstruction of high-resolution 6×6-mm OCT angiograms using deep learning

Typical optical coherence tomographic angiography (OCTA) acquisition areas on commercial devices are 3×3- or 6×6-mm. Compared to 3×3-mm angiograms with proper sampling density, 6×6-mm angiograms have significantly lower scan quality, with reduced signal-to-noise ratio and worse shadow artifacts due to undersampling. Here, we propose a deep-learning-based high-resolution angiogram reconstruction network (HARNet) to generate enhanced 6×6-mm superficial vascular complex (SVC) angiograms. The network was trained on data from 3×3-mm and 6×6-mm angiograms from the same eyes. The reconstructed 6×6-mm angiograms have significantly lower noise intensity, stronger contrast and better vascular connectivity than the original images. The algorithm did not generate false flow signal at the noise level presented by the original angiograms. The image enhancement produced by our algorithm may improve biomarker measurements and qualitative clinical assessment of 6×6-mm OCTA.

Maximum value projection produces better en face OCT angiograms than mean value projection

Optical coherence tomography angiography (OCTA) images rely on en face data projections for both qualitative and quantitative interpretation. Both maximum value and mean value projections are commonly used, and many researchers consider them essentially interchangeable approaches. On the contrary, we find that maximum value projection achieves a consistently higher signal-to-noise ratio and higher image contrast across multiple vascular layers, in both healthy eyes and for each disease examined.

Robust non-perfusion area detection in three retinal plexuses using convolutional neural network in OCT angiography

Non-perfusion area (NPA) is a quantitative biomarker useful for characterizing ischemia in diabetic retinopathy (DR). Projection-resolved optical coherence tomographic angiography (PR-OCTA) allows visualization of retinal capillaries and quantify NPA in individual plexuses. However, poor scan quality can make current NPA detection algorithms unreliable and inaccurate. In this work, we present a robust NPA detection algorithm using convolutional neural network (CNN). By merging information from OCT angiograms and OCT reflectance images, the CNN could exclude signal reduction and motion artifacts and detect the avascular features from local to global with the resolution preserved. Across a wide range of signal strength indices, and on both healthy and DR eyes, the algorithm achieved high accuracy and repeatability.

DcardNet: Diabetic Retinopathy Classification at Multiple Levels Based on Structural and Angiographic Optical Coherence Tomography

OBJECTIVE

Optical coherence tomography (OCT) and its angiography (OCTA) have several advantages for the early detection and diagnosis of diabetic retinopathy (DR). However, automated, complete DR classification frameworks based on both OCT and OCTA data have not been proposed. In this study, a convolutional neural network (CNN) based method is proposed to fulfill a DR classification framework using en face OCT and OCTA.

METHODS

A densely and continuously connected neural network with adaptive rate dropout (DcardNet) is designed for the DR classification. In addition, adaptive label smoothing was proposed and used to suppress overfitting. Three separate classification levels are generated for each case based on the International Clinical Diabetic Retinopathy scale. At the highest level the network classifies scans as referable or non-referable for DR. The second level classifies the eye as non-DR, non-proliferative DR (NPDR), or proliferative DR (PDR). The last level classifies the case as no DR, mild and moderate NPDR, severe NPDR, and PDR.

RESULTS

We used 10-fold cross-validation with 10% of the data to assess the network's performance. The overall classification accuracies of the three levels were 95.7%, 85.0%, and 71.0% respectively.

CONCLUSION/SIGNIFICANCE

A reliable, sensitive and specific automated classification framework for referral to an ophthalmologist can be a key technology for reducing vision loss related to DR.

High-resolution wide-field OCT angiography with a self-navigation method to correct microsaccades and blinks

In this study, we demonstrate a novel self-navigated motion correction method that suppresses eye motion and blinking artifacts on wide-field optical coherence tomographic angiography (OCTA) without requiring any hardware modification. Highly efficient GPU-based, real-time OCTA image acquisition and processing software was developed to detect eye motion artifacts. The algorithm includes an instantaneous motion index that evaluates the strength of motion artifact on en face OCTA images. Areas with suprathreshold motion and eye blinking artifacts are automatically rescanned in real-time. Both healthy eyes and eyes with diabetic retinopathy were imaged, and the self-navigated motion correction performance was demonstrated.

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.

75-degree non-mydriatic single-volume optical coherence tomographic angiography

Optical coherence tomography (OCT) and OCT angiography (OCTA) enable three-dimensional, high-resolution imaging of the eye. Yet, while they provide unprecedented structural and angiographic imaging detail, both have only limited fields of view in comparison to other imaging modalities like fundus photography. In this paper, we present a high-speed, high-sensitivity, swept source laser-based system that can acquire non-mydriatic 75-degree field of view OCT and OCTA images in a single complete scan without resorting to montaging techniques. The system uses an optimized scanning protocol and achieves capillary-level image quality. Such data may improve early detection of pathology and provide valuable information during disease monitoring.

An Open-Source Deep Learning Network for Reconstruction of High-Resolution OCT Angiograms of Retinal Intermediate and Deep Capillary Plexuses

Purpose

We propose a deep learning-based image reconstruction algorithm to produce high-resolution optical coherence tomographic angiograms (OCTA) of the intermediate capillary plexus (ICP) and deep capillary plexus (DCP).

Methods

In this study, 6-mm × 6-mm macular scans with a 400 × 400 A-line sampling density and 3-mm × 3-mm scans with a 304 × 304 A-line sampling density were acquired on one or both eyes of 180 participants (including 230 eyes with diabetic retinopathy and 44 healthy controls) using a 70-kHz commercial OCT system (RTVue-XR; Optovue, Inc., Fremont, California, USA). Projection-resolved OCTA algorithm removed projection artifacts in voxel. ICP and DCP angiograms were generated by maximum projection of the OCTA signal within the respective plexus. We proposed a deep learning-based method, which receives inputs from registered 3-mm × 3-mm ICP and DCP angiograms with proper sampling density as the ground truth reference to reconstruct 6-mm × 6-mm high-resolution ICP and DCP en face OCTA. We applied the same network on 3-mm × 3-mm angiograms to enhance these images further. We evaluated the reconstructed 3-mm × 3-mm and 6-mm × 6-mm angiograms based on vascular connectivity, Weber contrast, false flow signal (flow signal erroneously generated from background), and the noise intensity in the foveal avascular zone.

Results

Compared to the originals, the Deep Capillary Angiogram Reconstruction Network (DCARnet)-enhanced 6-mm × 6-mm angiograms had significantly reduced noise intensity (ICP, 7.38 ± 25.22, P < 0.001; DCP, 11.20 ± 22.52, P < 0.001), improved vascular connectivity (ICP, 0.95 ± 0.01, P < 0.001; DCP, 0.96 ± 0.01, P < 0.001), and enhanced Weber contrast (ICP, 4.25 ± 0.10, P < 0.001; DCP, 3.84 ± 0.84, P < 0.001), without generating false flow signal when noise intensity lower than 650. The DCARnet-enhanced 3-mm × 3-mm angiograms also reduced noise, improved connectivity, and enhanced Weber contrast in 3-mm × 3-mm ICP and DCP angiograms from 101 eyes. In addition, DCARnet preserved the appearance of the dilated vessels in the reconstructed angiograms in diabetic eyes.

Conclusions

DCARnet can enhance 3-mm × 3-mm and 6-mm × 6-mm ICP and DCP angiogram image quality without introducing artifacts.

Translational Relevance

The enhanced 6-mm × 6-mm angiograms may be easier for clinicians to interpret qualitatively.

Two-Point Microrheology of Phase-Separated Domains in Lipid Bilayers

Though the importance of membrane fluidity for cellular function has been well established for decades, methods for measuring lipid bilayer viscosity remain challenging to devise and implement. Recently, approaches based on characterizing the Brownian dynamics of individual tracers such as colloidal particles or lipid domains have provided insights into bilayer viscosity. For fluids in general, however, methods based on single-particle trajectories provide a limited view of hydrodynamic response. The technique of two-point microrheology, in which correlations between the Brownian dynamics of pairs of tracers report on the properties of the intervening medium, characterizes viscosity at length-scales that are larger than that of individual tracers and has less sensitivity to tracer-induced distortions, but has never been applied to lipid membranes. We present, to our knowledge, the first two-point microrheological study of lipid bilayers, examining the correlated motion of domains in phase-separated lipid vesicles and comparing one- and two-point results. We measure two-point correlation functions in excellent agreement with the forms predicted by two-dimensional hydrodynamic models, analysis of which reveals a viscosity intermediate between those of the two lipid phases, indicative of global fluid properties rather than the viscosity of the local neighborhood of the tracer.

A Diabetic Retinopathy Classification Framework Based on Deep-Learning Analysis of OCT Angiography

Purpose

Reliable classification of referable and vision threatening diabetic retinopathy (DR) is essential for patients with diabetes to prevent blindness. Optical coherence tomography (OCT) and its angiography (OCTA) have several advantages over fundus photographs. We evaluated a deep-learning-aided DR classification framework using volumetric OCT and OCTA.

Methods

Four hundred fifty-six OCT and OCTA volumes were scanned from eyes of 50 healthy participants and 305 patients with diabetes. Retina specialists labeled the eyes as non-referable (nrDR), referable (rDR), or vision threatening DR (vtDR). Each eye underwent a 3 × 3-mm scan using a commercial 70 kHz spectral-domain OCT system. We developed a DR classification framework and trained it using volumetric OCT and OCTA to classify eyes into rDR and vtDR. For the scans identified as rDR or vtDR, 3D class activation maps were generated to highlight the subregions which were considered important by the framework for DR classification.

Results

For rDR classification, the framework achieved a 0.96 ± 0.01 area under the receiver operating characteristic curve (AUC) and 0.83 ± 0.04 quadratic-weighted kappa. For vtDR classification, the framework achieved a 0.92 ± 0.02 AUC and 0.73 ± 0.04 quadratic-weighted kappa. In addition, the multiple DR classification (non-rDR, rDR but non-vtDR, or vtDR) achieved a 0.83 ± 0.03 quadratic-weighted kappa.

Conclusions

A deep learning framework only based on OCT and OCTA can provide specialist-level DR classification using only a single imaging modality.

Translational Relevance

The proposed framework can be used to develop clinically valuable automated DR diagnosis system because of the specialist-level performance showed in this study.

Deep-Learning-Aided Diagnosis of Diabetic Retinopathy, Age-Related Macular Degeneration, and Glaucoma Based on Structural and Angiographic OCT

Purpose

Timely diagnosis of eye diseases is paramount to obtaining the best treatment outcomes. OCT and OCT angiography (OCTA) have several advantages that lend themselves to early detection of ocular pathology; furthermore, the techniques produce large, feature-rich data volumes. However, the full clinical potential of both OCT and OCTA is stymied when complex data acquired using the techniques must be manually processed. Here, we propose an automated diagnostic framework based on structural OCT and OCTA data volumes that could substantially support the clinical application of these technologies.

Design

Cross sectional study.

Participants

Five hundred twenty-six OCT and OCTA volumes were scanned from the eyes of 91 healthy participants, 161 patients with diabetic retinopathy (DR), 95 patients with age-related macular degeneration (AMD), and 108 patients with glaucoma.

Methods

The diagnosis framework was constructed based on semisequential 3-dimensional (3D) convolutional neural networks. The trained framework classifies combined structural OCT and OCTA scans as normal, DR, AMD, or glaucoma. Fivefold cross-validation was performed, with 60% of the data reserved for training, 20% for validation, and 20% for testing. The training, validation, and test data sets were independent, with no shared patients. For scans diagnosed as DR, AMD, or glaucoma, 3D class activation maps were generated to highlight subregions that were considered important by the framework for automated diagnosis.

Main Outcome Measures

The area under the curve (AUC) of the receiver operating characteristic curve and quadratic-weighted kappa were used to quantify the diagnostic performance of the framework.

Results

For the diagnosis of DR, the framework achieved an AUC of 0.95 ± 0.01. For the diagnosis of AMD, the framework achieved an AUC of 0.98 ± 0.01. For the diagnosis of glaucoma, the framework achieved an AUC of 0.91 ± 0.02.

Conclusions

Deep learning frameworks can provide reliable, sensitive, interpretable, and fully automated diagnosis of eye diseases.

Financial Disclosures

Proprietary or commercial disclosure may be found after the references.

Imaging retinal structures at cellular-level resolution by visible-light optical coherence tomography

In vivo high-resolution images are the most direct way to understand retinal function and diseases. Here we report the use of visible-light optical coherence tomography with volumetric registration and averaging to achieve cellular-level retinal structural imaging in a rat eye, covering the entire depth of the retina. Vitreous fibers, nerve fiber bundles, and vasculature were clearly revealed, as well as at least three laminar sublayers in the inner plexiform layer. We also successfully visualized ganglion cell somas in the ganglion cell layer, cells in the inner nuclear layer, and photoreceptors in the outer nuclear layer and ellipsoid zone. This technique provides, to the best of our knowledge, a new means to visualize the retina in vivo at a cellular resolution and may enable detection or discovery of cellular neuronal biomarkers to help better diagnose ocular disease.

An end-to-end network for segmenting the vasculature of three retinal capillary plexuses from OCT angiographic volumes

The segmentation of en face retinal capillary angiograms from volumetric optical coherence tomographic angiography (OCTA) usually relies on retinal layer segmentation, which is time-consuming and error-prone. In this study, we developed a deep-learning-based method to segment vessels in the superficial vascular plexus (SVP), intermediate capillary plexus (ICP), and deep capillary plexus (DCP) directly from volumetric OCTA data. The method contains a three-dimensional convolutional neural network (CNN) for extracting distinct retinal layers, a custom projection module to generate three vascular plexuses from OCTA data, and three parallel CNNs to segment vasculature. Experimental results on OCTA data from rat eyes demonstrated the feasibility of the proposed method. This end-to-end network has the potential to simplify OCTA data processing on retinal vasculature segmentation. The main contribution of this study is that we propose a custom projection module to connect retinal layer segmentation and vasculature segmentation modules and automatically convert data from three to two dimensions, thus establishing an end-to-end method to segment three retinal capillary plexuses from volumetric OCTA without any human intervention.

Signal attenuation-compensated projection-resolved OCT angiography

Projection artifacts are a significant limitation of optical coherence tomographic angiography (OCTA). Existing techniques to suppress these artifacts are sensitive to image quality, becoming less reliable on low-quality images. In this study, we propose a novel signal attenuation-compensated projection-resolved OCTA (sacPR-OCTA) algorithm. In addition to removing projection artifacts, our method compensates for shadows beneath large vessels. The proposed sacPR-OCTA algorithm improves vascular continuity, reduces the similarity of vascular patterns in different plexuses, and removes more residual artifacts compared to existing methods. In addition, the sacPR-OCTA algorithm better preserves flow signal in choroidal neovascular lesions and shadow-affected areas. Because sacPR-OCTA processes the data along normalized A-lines, it provides a general solution for removing projection artifacts agnostic to the platform.

A Deep Learning Network for Classifying Arteries and Veins in Montaged Widefield OCT Angiograms

Purpose

To propose a deep-learning-based method to differentiate arteries from veins in montaged widefield OCT angiography (OCTA).

Design

Cross-sectional study.

Participants

A total of 232 participants, including 109 participants with diabetic retinopathy (DR), 64 participants with branch retinal vein occlusion (BRVO), 27 participants with diabetes but without DR, and 32 healthy participants.

Methods

We propose a convolutional neural network (CAVnet) to classify retinal blood vessels on montaged widefield OCTA en face images as arteries and veins. A total of 240 retinal angiograms from 88 eyes were used to train CAVnet, and 302 retinal angiograms from 144 eyes were used for testing. This method takes the OCTA images as input and outputs the segmentation results with arteries and veins down to the level of precapillary arterioles and postcapillary venules. The network also identifies their intersections. We evaluated the agreement (in pixels) between segmentation results and the manually graded ground truth using sensitivity, specificity, F1-score, and Intersection over Union (IoU). Measurements of arterial and venous caliber or tortuosity are made on our algorithm's output of healthy and diseased eyes.

Main Outcome Measures

Classification of arteries and veins, arterial and venous caliber, and arterial and venous tortuosity.

Results

For classification and identification of arteries, the algorithm achieved average sensitivity of 95.3%, specificity of 99.6%, F1 score of 94.2%, and IoU of 89.3%. For veins, the algorithm achieved average sensitivity of 94.4%, specificity of 99.7%, F1 score of 94.1%, and IoU of 89.2%. We also achieved an average sensitivity of 76.3% in identifying intersection points. The results show CAVnet has high accuracy on differentiating arteries and veins in DR and BRVO cases. These classification results are robust across 2 instruments and multiple scan volume sizes. Outputs of CAVnet were used to measure arterial and venous caliber or tortuosity, and pixel-wise caliber and tortuosity maps were generated. Differences between healthy and diseased eyes were demonstrated, indicating potential clinical utility.

Conclusions

The CAVnet can classify arteries and veins and their branches with high accuracy and is potentially useful in the analysis of vessel type-specific features on diseases such as branch retinal artery occlusion and BRVO.

Monitoring retinal responses to acute intraocular pressure elevation in rats with visible light optical coherence tomography

Elevated intraocular pressure (IOP) is an important risk factor for glaucoma. However, the role of IOP in glaucoma progression, as well as retinal physiology in general, remains incompletely understood. We demonstrate the use of visible light optical coherence tomography to measure retinal responses to acute IOP elevation in Brown Norway rats. We monitored retinal responses in reflectivity, angiography, blood flow, oxygen saturation ( sO 2 ), and oxygen metabolism over a range of IOP from 10 to 100 mmHg. As IOP was elevated, nerve fiber layer reflectivity was found to decrease. Vascular perfusion in the three retinal capillary plexuses remained steady until IOP exceeded 70 mmHg and arterial flow was noted to reverse periodically at high IOPs. However, a significant drop in total retinal blood flow was observed first at 40 mmHg. As IOP increased, the venous sO 2 demonstrated a gradual decrease despite steady arterial sO 2 , which is consistent with increased arterial-venous oxygen extraction across the retinal capillary beds. Calculated total retinal oxygen metabolism was steady, reflecting balanced responses of blood flow and oxygen extraction, until IOP exceeded 40 mmHg, and fell to 0 at 70 and 80 mmHg. Above this, measurements were unattainable. All measurements reverted to baseline when the IOP was returned to 10 mmHg, indicating good recovery following acute pressure challenge. These results demonstrate the ability of this system to monitor retinal oxygen metabolism noninvasively and how it can help us understand retinal responses to elevated IOP.

OCT angiography and its retinal biomarkers [Invited]

Optical coherence tomography angiography (OCTA) is a high-resolution, depth-resolved imaging modality with important applications in ophthalmic practice. An extension of structural OCT, OCTA enables non-invasive, high-contrast imaging of retinal and choroidal vasculature that are amenable to quantification. As such, OCTA offers the capability to identify and characterize biomarkers important for clinical practice and therapeutic research. Here, we review new methods for analyzing biomarkers and discuss new insights provided by OCTA.