Search Results (141)

The morphology of blood vessels in retinal fundus images is a key biomarker for diagnosing conditions such as glaucoma, hypertension, and diabetic retinopathy. This study introduces a deep learning-based method for automatic blood vessel segmentation, trained from scratch on 44 clinician-annotated images. The proposed architecture integrates Bidirectional Long Short-Term Memory (Bi-LSTM) layers with dropout to mitigate overfitting. A distinguishing feature of this approach is the column-wise processing, which improves feature extraction and segmentation accuracy. Additionally, a custom data augmentation technique tailored for retinal images is implemented to improve training performance. The results are presented in their raw form—without post-processing—to objectively assess the method’s effectiveness and limitations. Further refinements, including pre- and post-processing and the use of image rotations to combine multiple segmentation outputs, could significantly boost performance. Overall, this work offers a novel and effective approach to the still unresolved task of retinal vessel segmentation, contributing to more reliable automated analysis in ophthalmic diagnostics. Full article

One of the common microvascular complications in diabetic patients is diabetic retinopathy (DR), which primarily impacts the retinal blood vessels. As the course of diabetes progresses, the incidence of DR gradually increases, and, in serious situations, it can cause vision loss and even blindness. Diagnosing DR early is essential to mitigate its consequences, and deep learning models provide an effective approach. In this study, we propose an improved ResNet50 model, which replaces the 3 × 3 convolution in the residual structure by introducing an external attention mechanism, which improves the model’s awareness of global information and allows the model to grasp the characteristics of the input data more thoroughly. In addition, multiscale convolution is added to the residual branch, which further improves the ability of the model to extract local features and global features, and improves the processing accuracy of image details. In addition, the Sophia optimizer is introduced to replace the traditional Adam optimizer, which further optimizes the classification performance of the model. In this study, 3662 images from the Kaggle open dataset were used to generate 20,184 images for model training after image preprocessing and data augmentation. Experimental results show that the improved ResNet50 model achieves a classification accuracy of 96.68% on the validation set, which is 4.36% higher than the original architecture, and the Kappa value is increased by 5.45%. These improvements contribute to the early diagnosis of DR and decrease the likelihood of blindness among patients. Full article

Background: Hyperreflective retinal foci (HRF) are small, discrete, hyperreflective elements observed in the retina using optical coherence tomography (OCT). They appear in many retinal diseases and have been linked to disease progression, treatment response, and prognosis. However, their definition and clinical use vary widely, not just between different diseases, but also within a single disorder. Methods: This perspective is based on a review of peer-reviewed studies examining HRF across different retinal diseases. The studies included analyzed HRF morphology, distribution, and clinical relevance using OCT. Particular attention was given to histopathological correlations, disease-specific patterns, and advancements in automated quantification methods. Results: HRF distribution and features vary with disease type and even within the same disease. A variety of descriptions have been proposed with different characteristics in terms of dimensions, reflectivity, location, and association with back shadowing. Automated OCT analysis has enhanced HRF detection, enabling quantitative analysis that may expand their use in clinical practice. However, differences in software and methods can lead to inconsistent results between studies. HRF have been linked to microglial cells and may be defined as neuro-inflammatory cells (Inflammatory, I-HRF), migrating retinal pigment epithelium cells (Pigmentary, P-HRF), blood vessels (Vascular, V-HRF), and deposits of proteinaceous or lipid elements leaking from vessels (Exudative, E-HRF). Conclusions: HRF are emerging as valuable imaging biomarkers in retinal diseases. Four main types have been identified, with different morphological features, pathophysiological origin, and, therefore, different implications in the management of retinal diseases. Advances in imaging and computational analysis are promising for their incorporation into personalized treatment strategies. Full article

Background and Objectives: Despite the growing amount of new research, the pathophysiology of glaucoma remains unclear. The aim of this study was to determine the relationship between intracranial pressure (ICP), ocular blood flow and structural optic nerve parameters. Materials and Methods: A prospective clinical study was conducted involving 24 patients with open-angle glaucoma and 25 healthy controls. Routine clinical examination was performed. Swept-source optical coherence tomography (SS-OCT) and OCT angiography (OCTA) images were taken (DRI-OCT Triton, Topcon). The vessel density (VD) values of the ONH were calculated around the optic nerve head (ONH). An orbital Doppler device (Vittamed 205, Kaunas, Lithuania) was used for non-invasive ICP measurements. Color Doppler imaging (CDI) (Mindray M7, Shenzhen, China) was used for retrobulbar blood flow measurements in the ophthalmic artery (OA), central retinal artery (CRA) and short posterior ciliary arteries (SPCAs). Results: ICP was 8.35 ± 2.8 mmHg in the glaucoma group and 8.45 ± 3.19 mmHg in the control group (p = 0.907). In the glaucoma group, the VD of the superficial vascular plexus in the inferior-nasal (NI) sector of the ONH showed a correlation with ICP (r = 0.451, p = 0.05). In contrast, the control group exhibited weaker correlations. CRA peak systolic velocity (PSV) demonstrated significant moderate correlations with VD in multiple retinal layers, including the avascular retina layer in the temporal (T) sector (r = 0.637, p = 0.001). Conclusions: Lower ICP was significantly associated with the lower VD of the superficial plexus layer in the inferior-nasal sector in the glaucoma group, with the control group exhibiting weaker correlations in all sectors. Further longitudinal studies with larger sample sizes are needed to establish associations between intracranial pressure, ocular blood flow and ONH parameters. Full article

Eye illness detection is important, yet it can be difficult and error-prone. In order to effectively and promptly diagnose eye problems, doctors must use cutting-edge technologies. The goal of this research paper is to develop a sophisticated model that will help physicians detect different eye conditions early on. These conditions include age-related macular degeneration (AMD), diabetic retinopathy, cataracts, myopia, and glaucoma. Common eye conditions include cataracts, which cloud the lens and cause blurred vision, and glaucoma, which can cause vision loss due to damage to the optic nerve. The two conditions that could cause blindness if treatment is not received are age-related macular degeneration (AMD) and diabetic retinopathy, a side effect of diabetes that destroys the blood vessels in the retina. Problems include myopic macular degeneration, glaucoma, and retinal detachment—severe types of nearsightedness that are typically defined as having a refractive error of –5 diopters or higher—are also more likely to occur in people with high myopia. We intend to apply a user-friendly approach that will allow for faster and more efficient examinations. Our research attempts to streamline the eye examination procedure, making it simpler and more accessible than traditional hospital approaches. Our goal is to use deep learning and machine learning to develop an extremely accurate model that can assess medical images, such as eye retinal scans. This was accomplished by using a huge dataset to train the machine learning and deep learning model, as well as sophisticated image processing techniques to assist the algorithm in identifying patterns of various eye illnesses. Following training, we discovered that the CNN, VggNet, MobileNet, and hybrid Deep Learning models outperformed the SVM and Random Forest machine learning models in terms of accuracy, achieving above 98%. Therefore, our model could assist physicians in enhancing patient outcomes, raising survival rates, and creating more effective treatment plans for patients with these illnesses. Full article

Optical coherence tomography angiography (OCTA) is an advanced non-invasive imaging technique that can generate three-dimensional images of retinal and choroidal vessels. It is of great value in the diagnosis and monitoring of a variety of ophthalmic diseases. However, most existing methods for blood vessel segmentation in OCTA images rely on an encoder–decoder architecture. This architecture typically involves a large number of parameters and leads to slower inference speeds. To address these challenges and improve segmentation efficiency, this paper proposes a lightweight full-resolution convolutional neural network named FRNet V2 for blood vessel segmentation in OCTA images. FRNet V2 combines the ConvNeXt V2 architecture with deep separable convolution and introduces a recursive mechanism. This mechanism enhances feature representation while reducing the amount of model parameters and computational complexity. In addition, we design a lightweight hybrid adaptive attention mechanism (DWAM) that further improves the segmentation accuracy of the model through the combination of channel self-attention blocks and spatial self-attention blocks. The experimental results show that on two well-known retinal image datasets (OCTA-500 and ROSSA), FRNet V2 can achieve Dice coefficients and accuracy comparable to other methods while reducing the number of parameters by more than 90%. In conclusion, FRNet V2 provides an efficient and lightweight solution for fast and accurate OCTA image blood vessel segmentation in resource-constrained environments, offering strong support for clinical applications. Full article

The mechanism of post-acute sequelae of SARS-CoV-2 (PASC) is unknown. Using optical coherence tomography angiography (OCT-A), we compared retinal foveal avascular zone (FAZ), vessel density (VD), and vessel length density (VLD) in non-hospitalized Neuro-PASC patients with those in healthy controls in an effort to elucidate the mechanism underlying this debilitating condition. Neuro-PASC patients with a positive SARS-CoV-2 test and neurological symptoms lasting ≥6 weeks were included. Those with prior COVID-19 hospitalization were excluded. Subjects underwent OCT-A with segmentation of the full retinal slab into the superficial (SCP) and deep (DCP) capillary plexus. The FAZ was manually delineated on the full slab in ImageJ. An ImageJ macro was used to measure VD and VLD. OCT-A variables were analyzed using linear mixed-effects models with fixed effects for Neuro-PASC, age, and sex, and a random effect for patient to account for measurements from both eyes. The coefficient of Neuro-PASC status was used to determine statistical significance; p-values were adjusted using the Benjamani–Hochberg procedure. Neuro-PASC patients (N = 30; 60 eyes) exhibited a statistically significant (p = 0.005) reduction in DCP VLD compared to healthy controls (N = 44; 80 eyes). The sole reduction in DCP VLD in Neuro-PASC may suggest preferential involvement of the smallest blood vessels. Full article

Retinitis pigmentosa (RP) is a progressive inherited retinal dystrophy (IRD) that primarily affects rod photoreceptor cells, leading to the degeneration of photoreceptors and the gradual loss of vision. While RP is one of the most studied IRDs, other neurodegenerative diseases affecting the retina and optic nerve, such as glaucoma, also involve common mechanisms of cellular stress and degeneration. Current therapeutic approaches under investigation include gene therapy, retina prosthesis, and neuroprotection. Among these approaches, gene therapy has shown promise, though challenges related to viral vector tropism and transduction efficiency persist. The adeno-associated virus (AAV) vector is commonly employed for gene delivery, but novel serotypes and engineered variants are being explored to improve specificity and efficacy. This study evaluates the gene transfer efficiency of the AAV6.2 vector following intravitreal injection into the murine retina. Male C57BL/6 mice (9 weeks old) were intravitreally injected with 1 µL of AAV2-CMV-EGFP, AAV6-CMV-EGFP, or AAV6.2-CMV-EGFP at a titer of 3.2 × 10¹² vg/mL per eye. Retinal transduction was assessed using in vivo fluorescence imaging, flat-mount imaging, and immunohistochemistry. EGFP expression in retinal ganglion cells, Müller cells, amacrine cells, and bipolar cells was quantitatively analyzed. All three AAV serotypes effectively transduced retinal ganglion cells, but AAV6.2 exhibited enhanced transduction in Müller cells and other neuronal retinal cells, including bipolar and amacrine cells. AAV6.2 demonstrated more localized expression around retinal blood vessels compared to the diffuse expression observed with AAV2. Immunohistochemical analysis revealed that AAV6.2 had significantly higher transduction efficiency in Müller cells (p < 0.001) compared to AAV2 and AAV6. AAV6.2 shows superior transduction efficiency in Müller cells, positioning it as a promising vector for gene therapies targeting retinal degenerative diseases such as RP. Its ability to effectively transduce Müller cells suggests potential applications in neuroprotection and gene replacement therapies. Full article

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder of the brain associated with ageing and is the most prevalent form of dementia, affecting an estimated 55 million people worldwide, with projections suggesting this number will exceed 150 million by 2050. With its increasing prevalence, AD represents a significant global health challenge with potentially serious social and economic consequences. Diagnosing AD is particularly challenging as it requires timely recognition. Currently, there is no effective therapy for AD; however, certain medications may help slow its progression. Existing diagnostic methods such as magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), and biomarker analysis in cerebrospinal fluid tend to be expensive and invasive, making them impractical for widespread use. Consequently, research into non-invasive biomarkers that enable early detection and screening for AD is a crucial area of contemporary clinical investigation. One promising approach for the early diagnosis of AD may be retinal imaging. As an extension of the central nervous system, the retina offers a distinctive opportunity for non-invasive brain structure and function assessment. Considering their shared embryological origins and the vascular and immunological similarities between the eye and brain, alterations in the retina may indicate pathological changes in the brain, including those specifically related to AD. Studies suggest that structural and vascular changes in the retina, particularly within the neuronal network and blood vessels, may act as markers of cerebral changes caused by AD. These retinal alterations have the potential to act as biomarkers for early diagnosis. Since AD is typically diagnosed only after a significant neuronal loss has occurred, identifying early diagnostic markers could enable timely intervention and help prevent disease progression. Non-invasive retinal imaging techniques, such as optical coherence tomography (OCT) and OCT angiography, provide accessible methods for the early detection of changes linked to AD. This review article focuses on the potential of retinal imaging as a non-invasive biomarker for early diagnosis of AD. Investigating the ageing of the retina and its connections to neurodegenerative processes could significantly enhance the diagnosis, monitoring, and treatment of AD, paving the way for new diagnostic and therapeutic approaches. Full article

Background: Retinal blood vessel segmentation plays an important role in diagnosing retinal diseases such as diabetic retinopathy, glaucoma, and hypertensive retinopathy. Accurate segmentation of blood vessels in retinal images presents a challenging task due to noise, low contrast, and the complex morphology of blood vessel structures. Methods: In this study, we propose a novel ensemble learning framework combining four deep learning architectures: U-Net, ResNet50, U-Net with a ResNet50 backbone, and U-Net with a transformer block. Each architecture is customized to enhance feature extraction and segmentation performance. The models are trained on the DRIVE and STARE datasets to improve the degree of generalization and evaluated using the performance metric accuracy, F1-Score, sensitivity, specificity, and AUC. Results: The ensemble meta-model integrates predictions from these architectures using a stacking approach, achieving state-of-the-art performance with an accuracy of 0.9778, an AUC of 0.9912, and an F1-Score of 0.8231. These results demonstrate the performance of the proposed technique in identifying thin retinal blood vessels. Conclusions: A comparative analysis using qualitative and quantitative results with individual models highlights the robustness of the ensemble framework, especially under conditions of noise and poor visibility. Full article

Background/Objectives: Glaucomatous neuropathy, a progressive deterioration of retinal ganglion cells, is the leading cause of irreversible blindness worldwide. While elevated intraocular pressure (IOP) is a well-established modifiable risk factor, increasing attention is being directed towards IOP-independent factors, such as vascular alterations. Color Doppler imaging (CDI) is a prominent technique for investigating blood flow parameters in extraocular vessels. This prospective, nonrandomized clinical trial aimed to assess the impact of ab externo canaloplasty on ocular blood flow parameters in patients with primary open-angle glaucoma (POAG) at a three-month follow-up. Methods: Twenty-five eyes of twenty-five patients with early or moderate POAG underwent canaloplasty with simultaneous cataract removal. CDI was used to measure peak systolic velocity (PSV), end-diastolic velocity (EDV), and resistive index (RI) in the ophthalmic artery (OA), central retinal artery (CRA), and short posterior ciliary arteries (SPCAs) before and after surgery. Results: The results showed a significant reduction in IOP and improvement in mean deviation at three months post-surgery. Best corrected visual acuity and retinal nerve fiber layer thickness significantly increased at each postoperative control visit. However, no significant changes were observed in PSV, EDV, and RI in the studied vessels. Conclusions: In conclusion, while canaloplasty effectively reduced IOP and medication burden, it did not significantly improve blood flow parameters in vessels supplying the optic nerve at three months post-surgery. Careful patient selection considering glaucoma severity and vascular risk factors is crucial when choosing between canaloplasty and more invasive procedures like trabeculectomy. Further larger studies are needed to comprehensively analyze this issue. Full article

Objective: This study used optical attenuation coefficient (OAC)-based optical coherence tomography (OCT) en face images to assess the ellipsoid zone (EZ) in the foveal region. Methods: This retrospective, observational, cross-sectional study of 41 healthy volunteers and 34 patients with retinal diseases included imaging data acquired using a prototype swept-source OCT system. EZ en face images were generated from OCT raster scan volumes based on OAC, followed by denoising and binarization to quantify the percentage of EZ structural normality or abnormality relative to the total imaging area. We evaluated the reliability of the OAC-based method compared with the OCT signal intensity-based method in healthy and diseased eyes. In addition, the validated program was applied to patients with various retinal conditions. Results: The estimated normal EZ area in healthy eyes was 96.2 ± 5.6% using the OAC-based method versus 89.3 ± 18.8% for the intensity-based method. The OAC-based method effectively mitigated various artifacts caused by retinal blood vessels and other factors in both healthy and diseased eyes. In a pilot study involving six diseased eyes, the area exhibiting EZ structural abnormalities was 27.5–99.6%. Conclusions: The OAC-based EZ assessment robustly suppressed image artifacts and reliably characterized structural abnormalities in the EZ from OCT volumes. Full article

Retinal image segmentation plays a critical role in diagnosing and monitoring ophthalmic diseases such as diabetic retinopathy and age-related macular degeneration. We propose a deep learning-based approach utilizing the U-Net network for the accurate and efficient segmentation of retinal images. U-Net, a convolutional neural network widely used for its performance in medical image segmentation, is employed to segment key retinal structures, including the optic disc and blood vessels. We evaluate the proposed model on a publicly available retinal image dataset, demonstrating interesting performance in automatic retina segmentation, thus showing the effectiveness of the proposed method. Our proposal provides a promising method for automated retinal image analysis, aiding in early disease detection and personalized treatment planning. Full article

Dye-based angiography is the main imaging modality in evaluating the vasculature of the eye. Although most commonly used to assess retinal vasculature, it can also delineate normal and abnormal blood vessels in the anterior segment diseases—but is limited due to its invasive, time-consuming methods. Thus, anterior segment optical coherence tomography angiography (AS-OCTA) is a useful non-invasive modality capable of producing high-resolution images to evaluate the cornea and ocular surface vasculature. AS-OCTA has demonstrated the potential to detect and delineate blood vessels in the anterior segment with quality images comparable to dye-based angiography. AS-OCTA has a diverse range of applications for the cornea and ocular surface, such as objective assessment of corneal neovascularization and response to various treatments; diagnosis and evaluation of ocular surface squamous neoplasia; and evaluation of ocular surface disease including limbal stem cell deficiency and ischemia. Our review aims to summarize the new developments and clinical applications of AS-OCTA for the cornea and ocular surface. Full article

Many major diseases can cause changes in the morphology of blood vessels, and the segmentation of retinal blood vessels is of great significance for preventing these diseases. Obtaining complete, continuous, and high-resolution segmentation results is very challenging due to the diverse structures of retinal tissues, the complex spatial structures of blood vessels, and the presence of many small ships. In recent years, deep learning networks like UNet have been widely used in medical image processing. However, the continuous down-sampling operations in UNet can result in the loss of a significant amount of information. Although skip connections between the encoder and decoder can help address this issue, the encoder features still contain a large amount of irrelevant information that cannot be efficiently utilized by the decoder. To alleviate the irrelevant information, this paper proposes a feature selection module between the decoder and encoder that utilizes the self-attention mechanism of transformers to accurately and efficiently select the relevant encoder features for the decoder. Additionally, a lightweight Residual Global Context module is proposed to obtain dense global contextual information and establish dependencies between pixels, which can effectively preserve vascular details and segment small vessels accurately and continuously. Experimental results on three publicly available color fundus image datasets (DRIVE, CHASE, and STARE) demonstrate that the proposed algorithm outperforms existing methods in terms of both performance metrics and visual quality. Full article