KEYWORDS: Optical coherence tomography, Angiography, Veins, Arteries, RGB color model, Network architectures, Near infrared, Image segmentation, Eye, Control systems
Early disease diagnosis and effective treatment assessment are crucial to prevent vision loss. Retinal arteries and veins can be affected differently by different eye diseases, e.g., arterial narrowing and venous beading in diabetic retinopathy (DR). Therefore, differential artery-vein (AV) analysis can provide valuable information for early disease detection and better stage classification. However, manual, or semi-automated methods for AV identification are inefficient in a clinical setting. This study is to demonstrate the use of deep learning for automated AV classification in optical coherence tomography angiography (OCTA). We present ‘AV-Net’, a fully convolutional network (CNN) based on a modified Ushaped architecture. The input to AV-Net is a 2-channel system that combines grayscale enface OCT and OCTA. The enface OCT is a near infrared image, equivalent to a fundus image, which provides the vessel intensity profiles. In contrast, the OCTA contains the information of the blood flow strength, and vessel geometric features. The output of AV-Net is an RGB (red-green-blue) image, with R and B corresponding to arteries and veins, respectively, and the G channel represents the background. The dataset in this study is comprised of images from 50 individuals (20 controls and 30 DR patients). Transfer learning and regularization techniques, such as data augmentation and cross validation, were employed during training to prevent overfitting. The results reveal robust vessel segmentation and AV classification. A fully automated platform is essential for fostering efficient clinical deployment of AI-based screening, diagnosis, and treatment evaluation.
Early detection of diabetic retinopathy (DR) is an essential step to prevent vision losses. This study is the first effort to explore convolutional neural networks (CNNs) for transfer-learning based optical coherence tomography angiography (OCTA) detection and classification of DR. We employed transfer-learning using a pre-trained CNN, VGG16, based on the ImageNet dataset for classification of OCTA images. To prevent overfitting, data augmentation, e.g. rotations, flips, and zooming, and 5-fold cross-validation were implemented. A dataset comprising of 131 OCTA images from 20 control, 17 diabetic patients without DR (NoDR), and 60 nonproliferative DR (NPDR) patients were used for preliminary validation. Best classification performance was achieved with fine-tuning nine layers of the sixteen-layer CNN model.
Diabetic retinopathy (DR) is a major ocular manifestation of diabetes. DR can cause irreversible damage to the retina if not intervened timely. Therefore, early detection and reliable classification are essential for effective management of DR. As DR progresses into the proliferative stage (PDR), manifestation of localized neovascularization and complex capillary meshes are observed in the retina. These vascular complex structures can be quantified as biomarkers of transition of DR from no-proliferative to proliferative stage (NPDR). This study investigates four optical coherence tomography angiography (OCTA) features, i.e. vessel complexity index (VCI), fractal dimension (FD), four-point crossover (FCO), and blood vessel tortuosity (BVT), to quantify vascular complexity to distinguish NPDR from PDR eyes. OCTA images from 20 control, 60 NPDR and 56 PDR patients were analyzed. The univariate analysis showed that, with the progression of DR, all four complexity features increased with statistical significance (ANOVA, P < 0.05). A posthoc study showed that, only VCI and BVT were able to distinguish between NPDR and PDR. A multivariate logistic regression identified VCI and BVT as the most significant feature combination for NPDR vs PDR classification.
Diabetic retinopathy (DR) and other eye diseases can affect artery and vein differently. Therefore, differential artery-vein analysis can improve disease detection and treatment assessment. This study aims to establish color fundus image analysis guided artery-vein differentiation in OCTA, and to verify that differential artery-vein analysis can improve the sensitivity of OCTA detection and classification of DR. Briefly, optical density ratio (ODR) analysis and blood vessel tracking were combined to identify artery-vein in color fundus images. The fundus artery-vein map was used to register arteries and veins in corresponding OCTA images. Based on the fundus image guided artery-vein differentiation, quantitative analysis of arteries and veins in control and NPDR OCTA images were performed. The sensitivities of traditional mean blood vessel caliber (m-BVC) and artery-vein ratio of BVC (AVR-BVC) were quantitatively compared for DR classification. One way, multi-label analysis of variance (ANOVA) with Bonferroni’s test and Student t-test was employed for evaluating classification performance. Images from 20 eyes of 18 control subjects and 48 eyes of 35 NPDR patients (18 mild, 16 moderate and 14 severe NPDR) were used for this study. Compared to m-BVC, AVR-BVC provided enhanced sensitivity in differentiating NPDR stages. AVR-BVC was able to differentiate among control and three different NPDR groups. AVR-BVC could also differentiate control from mild NPDR, promising a unique OCTA biomarker for detecting early onset of NPDR.
Differential artery-vein analysis is valuable for early detection of diabetic retinopathy (DR) and other eye diseases. As a new optical coherence tomography (OCT) imaging modality, emerging OCT angiography (OCTA) provides capillary level resolution for accurate examination of retinal vasculatures. However, differential artery-vein analysis in OCTA, particularly for macular region in which blood vessels are small, is challenging. In coordination with an automatic vessel tracking algorithm, we report here the feasibility of using near infrared OCT oximetry to guide artery-vein classification in OCTA of macular region.
In conventional fundus photography, illuminating light is delivered to the interior of the eye through the pupil. To avoid reflection from cornea and crystalline lens, peripheral area of the pupil is used for delivering illumination light and only the central part of the pupil can be used for collecting imaging light. Therefore, the optical design of conventional fundus cameras is sophisticated, the field of view is limited, and pupil dilation is required for evaluating the retinal periphery which is frequently affected by diabetic retinopathy (DR), retinopathy of premature (ROP), and other chorioretinal conditions. Trans-scleral illumination has been proposed as one alternative illumination method to achieve wide field fundus examination not requiring pharmacologic pupil dilation. However, clinical deployment of trans-scleral illumination failed due to the contact mode illumination and imaging, and complication of instrument operation. Here we report a nonmydriatic wide field fundus camera employing trans-pars-planar illumination which delivers illuminating light through the pars plana, an area outside of the pupil without contacting the eye. Trans-pars-planar illumination frees the entire pupil for imaging purpose only, and thus wide field fundus photography can be readily achieved with less pupil dilation. For proof-of-concept testing, using all off-the-shelf components a prototype instrument that can achieve 90° fundus view coverage in single-shot fundus images, without the need of pharmacologic pupil dilation was demonstrated.
It is known that retinopathies may affect arteries and veins differently. Therefore, reliable differentiation of arteries and veins is essential for computer-aided analysis of fundus images. The purpose of this study is to validate one automated method for robust classification of arteries and veins (A-V) in digital fundus images. We combine optical density ratio (ODR) analysis and blood vessel tracking algorithm to classify arteries and veins. A matched filtering method is used to enhance retinal blood vessels. Bottom hat filtering and global thresholding are used to segment the vessel and skeleton individual blood vessels. The vessel tracking algorithm is used to locate the optic disk and to identify source nodes of blood vessels in optic disk area. Each node can be identified as vein or artery using ODR information. Using the source nodes as starting point, the whole vessel trace is then tracked and classified as vein or artery using vessel curvature and angle information. 50 color fundus images from diabetic retinopathy patients were used to test the algorithm. Sensitivity, specificity, and accuracy metrics were measured to assess the validity of the proposed classification method compared to ground truths created by two independent observers. The algorithm demonstrated 97.52% accuracy in identifying blood vessels as vein or artery. A quantitative analysis upon A-V classification showed that average A-V ratio of width for NPDR subjects with hypertension decreased significantly (43.13%).
KEYWORDS: Super resolution, In vivo imaging, Spatial frequencies, Retinal scanning, Image resolution, Retina, Eye, Spatial resolution, Signal to noise ratio, Microscopy
High resolution is important for sensitive detection of subtle distortions of retinal morphology at an early stage of eye diseases. We demonstrate virtually structured detection (VSD) as a feasible method to achieve in vivo super-resolution ophthalmoscopy. A line-scanning strategy was employed to achieve a super-resolution imaging speed up to 127 frames/s with a frame size of 512×512 pixels. The proof-of-concept experiment was performed on anesthetized frogs. VSD-based super-resolution images reveal individual photoreceptors and nerve fiber bundles unambiguously. Both image contrast and signal-to-noise ratio are significantly improved due to the VSD implementation.
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