Leuven-Haifa High-Resolution Fundus Image Dataset for Retinal Blood Vessel Segmentation and Glaucoma Diagnosis.

The Leuven-Haifa dataset contains 240 disc-centered fundus images of 224 unique patients (75 patients with normal tension glaucoma, 63 patients with high tension glaucoma, 30 patients with other eye diseases and 56 healthy controls) from the University Hospitals of Leuven. The arterioles and venules of these images were both annotated by master students in medicine and corrected by a senior annotator. All senior segmentation corrections are provided as well as the junior segmentations of the test set. An open-source toolbox for the parametrization of segmentations was developed. Diagnosis, age, sex, vascular parameters as well as a quality score are provided as metadata. Potential reuse is envisioned as the development or external validation of blood vessels segmentation algorithms or study of the vasculature in glaucoma and the development of glaucoma diagnosis algorithms. The dataset is available on the KU Leuven Research Data Repository (RDR).

LUNet: deep learning for the segmentation of arterioles and venules in high resolution fundus images.

Objective.This study aims to automate the segmentation of retinal arterioles and venules (A/V) from digital fundus images (DFI), as changes in the spatial distribution of retinal microvasculature are indicative of cardiovascular diseases, positioning the eyes as windows to cardiovascular health.Approach.We utilized active learning to create a new DFI dataset with 240 crowd-sourced manual A/V segmentations performed by 15 medical students and reviewed by an ophthalmologist. We then developed LUNet, a novel deep learning architecture optimized for high-resolution A/V segmentation. The LUNet model features a double dilated convolutional block to widen the receptive field and reduce parameter count, alongside a high-resolution tail to refine segmentation details. A custom loss function was designed to prioritize the continuity of blood vessel segmentation.Main Results.LUNet significantly outperformed three benchmark A/V segmentation algorithms both on a local test set and on four external test sets that simulated variations in ethnicity, comorbidities and annotators.Significance.The release of the new datasets and the LUNet model (www.aimlab-technion.com/lirot-ai) provides a valuable resource for the advancement of retinal microvasculature analysis. The improvements in A/V segmentation accuracy highlight LUNet's potential as a robust tool for diagnosing and understanding cardiovascular diseases through retinal imaging.

The Relevance of Arterial Blood Pressure in the Management of Glaucoma Progression: A Systematic Review.

BACKGROUND: Glaucoma is one of the leading causes of global blindness and is expected to co-occur more frequently with vascular morbidities in the upcoming years, as both are aging-related diseases. Yet, the pathogenesis of glaucoma is not entirely elucidated and the interplay between intraocular pressure, arterial blood pressure (BP) and ocular perfusion pressure is poorly understood. OBJECTIVES: This systematic review aims to provide clinicians with the latest literature regarding the management of arterial BP in glaucoma patients. METHODS: A systematic search was performed in Medline, Embase, Web of Science and Cochrane Library. Articles written in English assessing the influence of arterial BP and systemic antihypertensive treatment of glaucoma and its management were eligible for inclusion. Additional studies were identified by revising references included in selected articles. RESULTS: 80 Articles were included in this systemic review. A bimodal relation between BP and glaucoma progression was found. Both high and low BP increase the risk of glaucoma. Glaucoma progression was, possibly via ocular perfusion pressure variation, strongly associated with nocturnal dipping and high variability in the BP over 24 h. CONCLUSIONS: We concluded that systemic BP level associates with glaucomatous damage and provided recommendations for the management and study of arterial BP in glaucoma. Prospective clinical trials are needed to further support these recommendations.

Corrigendum: Progression of functional and structural glaucomatous damage in relation to diurnal and nocturnal dips in mean arterial pressure.

[This corrects the article DOI: 10.3389/fcvm.2022.1024044.].

High-intensity interval training in patients with glaucoma (HIT-GLAUCOMA): protocol for a multicenter randomized controlled exercise trial.

Background: Glaucoma stands as a prominent global cause of irreversible blindness and the primary treatment approach involves reducing intraocular pressure (IOP). However, around one-third of patients exhibit disease progression despite effective IOP reduction. Microvascular endothelial function, chronic inflammation, and oxidative stress are known to affect retinal neuronal networks and have been associated with disease severity and progression. Exercise training has the potential to counteract these mechanisms as add-on treatment to usual care. Aims: The HIT-GLAUCOMA study will investigate the effects of a 6-month high-intensity interval training (HIIT) on intermediate endpoints such as local retinal microvascular and systemic large artery function, inflammation, and oxidative stress as well as clinical endpoints such as visual field indices, optic nerve rim assessment, retinal nerve fiber layer thickness, IOP, number of eye drops, vision-related quality of life and ocular surface disease symptomatology. Methods: The study is a multi-center randomized controlled clinical trial in patients with both normal tension and high-tension primary open angle glaucoma. Across two study centers, 128 patients will be enrolled and randomized on a 1:1 basis into an exercise intervention group and a usual care control group. The primary microvascular endpoints are retinal arteriolar and venular flicker light-induced dilation at 6 months. The primary endpoint in the systemic circulation is brachial artery flow-mediated dilation at 6 months. Anticipated results: We hypothesize that exercise therapy will improve retinal microvascular function and thus ocular blood flow in patients with glaucoma. As clinical outcomes, we will investigate the effect of exercise on visual field indices, optic nerve rim assessment, retinal nerve fiber layer thickness, IOP, number of eye drops, vision-related quality of life and ocular surface disease symptomatology. Discussion: HIT-GLAUCOMA is a blueprint trial design to study the effect of exercise training on neurodegenerative and cardiovascular diseases. Importantly, patients are also expected to benefit from improvements in general health and cardiovascular co-morbidities. If proven effective, exercise may offer a new add-on treatment strategy to slow glaucoma progression. Clinical Trial Registration Number: The trial is registered at Clinicaltrials.gov under the identifier NCT06058598 and is currently in the recruitment stage.

FundusQ-Net: A regression quality assessment deep learning algorithm for fundus images quality grading.

OBJECTIVE: Ophthalmological pathologies such as glaucoma, diabetic retinopathy and age-related macular degeneration are major causes of blindness and vision impairment. There is a need for novel decision support tools that can simplify and speed up the diagnosis of these pathologies. A key step in this process is to automatically estimate the quality of the fundus images to make sure these are interpretable by a human operator or a machine learning model. We present a novel fundus image quality scale and deep learning (DL) model that can estimate fundus image quality relative to this new scale. METHODS: A total of 1245 images were graded for quality by two ophthalmologists within the range 1-10, with a resolution of 0.5. A DL regression model was trained for fundus image quality assessment. The architecture used was Inception-V3. The model was developed using a total of 89,947 images from 6 databases, of which 1245 were labeled by the specialists and the remaining 88,702 images were used for pre-training and semi-supervised learning. The final DL model was evaluated on an internal test set (n=209) as well as an external test set (n=194). RESULTS: The final DL model, denoted FundusQ-Net, achieved a mean absolute error of 0.61 (0.54-0.68) on the internal test set. When evaluated as a binary classification model on the public DRIMDB database as an external test set the model obtained an accuracy of 99%. SIGNIFICANCE: the proposed algorithm provides a new robust tool for automated quality grading of fundus images.

Retinal OCT speckle as a biomarker for glaucoma diagnosis and staging.

This paper presents a novel image analysis strategy that increases the potential of macular Optical Coherence Tomography (OCT) by using speckle features as biomarkers in different stages of glaucoma. A large pool of features (480) were computed for a subset of macular OCT volumes of the Leuven eye study cohort. The dataset contained 258 subjects that were divided into four groups based on their glaucoma severity: Healthy (56), Mild (94), Moderate (48), and Severe (60). The OCT speckle features were categorized as statistical properties, statistical distributions, contrast, spatial gray-level dependence matrices, and frequency domain features. The averaged thicknesses of ten retinal layers were also collected. Kruskal-Wallis H test and multivariable regression models were used to infer the most significant features related to glaucoma severity classification and to the correlation with visual field mean deviation. Four features were selected as being the most relevant: the ganglion cell layer (GCL) and the inner plexiform layer (IPL) thicknesses, and two OCT speckle features, the data skewness computed on the retinal nerve fiber layer (RNFL) and the scale parameter (a) of the generalized gamma distribution fitted to the GCL data. Based on a significance level of 0.05, the regression models revealed that RNFL skewness exhibited the highest significance among the features considered for glaucoma severity staging (p-values of 8.6×10-6 for the logistic model and 2.8×10-7 for the linear model). Furthermore, it demonstrated a strong negative correlation with the visual field mean deviation (ρ=-0.64). The post hoc analysis revealed that, when distinguishing healthy controls from glaucoma subjects, GCL thickness is the most relevant feature (p-value of 8.7×10-5). Conversely, when comparing the Mild versus Moderate stages of glaucoma, RNFL skewness emerged as the only feature exhibiting statistical significance (p-value = 0.001). This work shows that macular OCT speckle contains information that is currently not used in clinical practice, and not only complements structural measurements (thickness) but also has a potential for glaucoma staging.

Retinal imaging biomarkers of neurodegenerative diseases.

The timely detection of neurodegenerative diseases is central to improving clinical care as well as enabling the development and deployment of disease-modifying therapies. Retinal imaging is emerging as a method to detect features of a number of neurodegenerative diseases, given the anatomical and functional similarities between the retina and the brain. This review provides an overview of the current status of retinal imaging biomarkers of neurodegenerative diseases including Alzheimer's disease, Parkinson's disease, Lewy body dementia, frontotemporal dementia, Huntington's disease and multiple sclerosis. Whilst research findings are promising, efforts to harmonise study designs and imaging methods will be important in translating these findings into clinical care. Doing so may mean that eye care providers will play important roles in the detection of a variety of neurodegenerative diseases in future.

Normal tension glaucoma: A dynamic optical coherence tomography angiography study.

Purpose: Vascular dysregulation seems to play a role in the pathogenesis of glaucoma, in particular normal tension glaucoma (NTG). The development of optical coherence tomography angiography (OCTA) enabled the measurement of the retinal microvasculature non-invasively and with high repeatability. Nonetheless, only a few studies transformed OCTA into a dynamic examination employing a sympathomimetic stimulus. The goal of this study was to use this dynamic OCTA exam (1) to differentiate healthy individuals from glaucoma patients and (2) to distinguish glaucoma subcategories, NTG and high-tension primary open angle glaucoma (POAG). Methods: Retinal vessel density (VD) in NTG patients (n = 16), POAG patients (n = 12), and healthy controls (n = 14) was compared before and during a hand grip test with a hydraulic dynamometer. Results: At baseline, mean peripapillary VD was lower in POAG and NTG (42.6 and 48.5%) compared to healthy controls (58.1%; p < 0.001) and higher in NTG compared to POAG (p = 0.024) when corrected for mean arterial pressure (MAP). Peripapillary and macular (superficial and deep) VD differences were found for gender, age, and baseline MAP. No change in VD occurred (pre-/post-stimulus) in any of the groups. Conclusion: Retinal VD loss in glaucoma patients was confirmed and the necessity to correct for gender, age and especially MAP was established. Although replication in a larger population is necessary, OCTA might not be the most suitable method to dynamically evaluate the retinal microvasculature.

Automatic Segmentation of the Optic Nerve Head Region in Optical Coherence Tomography: A Methodological Review.

The optic nerve head (ONH) represents the intraocular section of the optic nerve, which is prone to damage by intraocular pressure (IOP). The advent of optical coherence tomography (OCT) has enabled the evaluation of novel ONH parameters, namely the depth and curvature of the lamina cribrosa (LC). Together with the Bruch's membrane minimum-rim-width (BMO-MRW), these seem to be promising ONH parameters for diagnosis and monitoring of retinal diseases such as glaucoma. Nonetheless, these OCT derived biomarkers are mostly extracted through manual segmentation, which is time-consuming and prone to bias, thus limiting their usability in clinical practice. The automatic segmentation of ONH in OCT scans could further improve the current clinical management of glaucoma and other diseases. This review summarizes the current state-of-the-art in automatic segmentation of the ONH in OCT. PubMed and Scopus were used to perform a systematic review. Additional works from other databases (IEEE, Google Scholar and ARVO IOVS) were also included, resulting in a total of 29 reviewed studies. For each algorithm, the methods, the size and type of dataset used for validation, and the respective results were carefully analysed. The results show a lack of consensus regarding the definition of segmented regions, extracted parameters and validation approaches, highlighting the importance and need of standardized methodologies for ONH segmentation. Only with a concrete set of guidelines, these automatic segmentation algorithms will build trust in data-driven segmentation models and be able to enter clinical practice.

Alternative indications for corneal crosslinking.

Corneal crosslinking (CXL) is the current mainstay treatment for progressive keratoconus. In the past 15 years, a variety of other indications have been tested. A systematic review was conducted to examine these alternative indications for CXL. In total, 143 papers on crosslinking as a treatment for infectious keratitis, bullous keratopathy, pellucid marginal degeneration, and postlaser in situ keratomileusis (LASIK) ectasia and as a way to improve vision either on its own or in combination with other interventions were included. Post-LASIK ectasia is a definite indication for crosslinking. Surprisingly, only limited research has been performed on pellucid marginal degeneration, with no randomized trials available to date. Other interesting applications are the combined use of refractive lasers and crosslinking for suspicious or ectatic corneas and crosslinking as a standalone intervention for minor refractive errors. CXL might offer a solution for refractory bacterial keratitis. In bullous keratopathy, it seems to offer only a transient benefit.

Optical Coherence Tomography Imaging of the Lamina Cribrosa: Structural Biomarkers in Nonglaucomatous Diseases.

The lamina cribrosa (LC) is an active structure that responds to the strain by changing its morphology. Abnormal changes in LC morphology are usually associated with, and indicative of, certain pathologies such as glaucoma, intraocular hypertension, and myopia. Recent developments in optical coherence tomography (OCT) have enabled detailed in vivo studies about the architectural characteristics of the LC. Structural characteristics of the LC have been widely explored in glaucoma management. However, information about which LC biomarkers could be useful for the diagnosis, and follow-up, of other diseases besides glaucoma is scarce. Hence, this literature review aims to summarize the role of the LC in nonophthalmic and ophthalmic diseases other than glaucoma. PubMed was used to perform a systematic review on the LC features that can be extracted from OCT images. All imaging features are presented and discussed in terms of their importance and applicability in clinical practice. A total of 56 studies were included in this review. Overall, LC depth (LCD) and thickness (LCT) have been the most studied features, appearing in 75% and 45% of the included studies, respectively. These biomarkers were followed by the prelaminar tissue thickness (21%), LC curvature index (5.4%), LC global shape index (3.6%), LC defects (3.6%), and LC strains/deformations (1.8%). Overall, the disease groups showed a thinner LC (smaller LCT) and a deeper ONH cup (larger LCD), with some exceptions. A large variability between approaches used to compute LC biomarkers has been observed, highlighting the importance of having automated and standardized methodologies in LC analysis. Moreover, further studies are needed to identify the pathologies where LC features have a diagnostic and/or prognostic value.

Advanced vascular examinations of the retina and optic nerve head in glaucoma.

Recent technological breakthroughs have facilitated vascular research in the field of glaucoma. In this chapter, we review several of these vascular-oriented technologies, with a special focus given to optical coherence tomography angiography. An update is given regarding recent findings in glaucoma, but also on the improvements needed to bring vascular assessments closer to everyday clinical practice.

Hyperspectral Imaging and the Retina: Worth the Wave?

Purpose: Hyperspectral imaging is gaining attention in the biomedical field because it generates additional spectral information to study physiological and clinical processes. Several technologies have been described; however an independent, systematic literature overview is lacking, especially in the field of ophthalmology. This investigation is the first to systematically overview scientific literature specifically regarding retinal hyperspectral imaging. Methods: A systematic literature review was conducted, in accordance with PRISMA Statement 2009 criteria, in four bibliographic databases: Medline, Embase, Cochrane Database of Systematic Reviews, and Web of Science. Results: Fifty-six articles were found that meet the review criteria. A range of techniques was reported: Fourier analysis, liquid crystal tunable filters, tunable laser sources, dual-slit monochromators, dispersive prisms and gratings, computed tomography, fiber optics, and Fabry-Perrot cavity filter covered complementary metal oxide semiconductor. We present a narrative synthesis and summary tables of findings of the included articles, because methodologic heterogeneity and diverse research topics prevented a meta-analysis being conducted. Conclusions: Application in ophthalmology is still in its infancy. Most previous experiments have been performed in the field of retinal oximetry, providing valuable information in the diagnosis and monitoring of various ocular diseases. To date, none of these applications have graduated to clinical practice owing to the lack of sufficiently large validation studies. Translational Relevance: Given the promising results that smaller studies show for hyperspectral imaging (e.g., in Alzheimer's disease), advanced research in larger validation studies is warranted to determine its true clinical potential.

Combination of snapshot hyperspectral retinal imaging and optical coherence tomography to identify Alzheimer's disease patients.

INTRODUCTION: The eye offers potential for the diagnosis of Alzheimer's disease (AD) with retinal imaging techniques being explored to quantify amyloid accumulation and aspects of neurodegeneration. To assess these changes, this proof-of-concept study combined hyperspectral imaging and optical coherence tomography to build a classification model to differentiate between AD patients and controls. METHODS: In a memory clinic setting, patients with a diagnosis of clinically probable AD (n = 10) or biomarker-proven AD (n = 7) and controls (n = 22) underwent non-invasive retinal imaging with an easy-to-use hyperspectral snapshot camera that collects information from 16 spectral bands (460-620 nm, 10-nm bandwidth) in one capture. The individuals were also imaged using optical coherence tomography for assessing retinal nerve fiber layer thickness (RNFL). Dedicated image preprocessing analysis was followed by machine learning to discriminate between both groups. RESULTS: Hyperspectral data and retinal nerve fiber layer thickness data were used in a linear discriminant classification model to discriminate between AD patients and controls. Nested leave-one-out cross-validation resulted in a fair accuracy, providing an area under the receiver operating characteristic curve of 0.74 (95% confidence interval [0.60-0.89]). Inner loop results showed that the inclusion of the RNFL features resulted in an improvement of the area under the receiver operating characteristic curve: for the most informative region assessed, the average area under the receiver operating characteristic curve was 0.70 (95% confidence interval [0.55, 0.86]) and 0.79 (95% confidence interval [0.65, 0.93]), respectively. The robust statistics used in this study reduces the risk of overfitting and partly compensates for the limited sample size. CONCLUSIONS: This study in a memory-clinic-based cohort supports the potential of hyperspectral imaging and suggests an added value of combining retinal imaging modalities. Standardization and longitudinal data on fully amyloid-phenotyped cohorts are required to elucidate the relationship between retinal structure and cognitive function and to evaluate the robustness of the classification model.