VAMPIRE: Vessel assessment and measurement platform for images of the REtina

Is Primary Open-Angle Glaucoma a Vascular Disease? Assessment of the Relationship between Retinal Arteriolar Morphology and Glaucoma Severity Using Adaptive Optics

BACKGROUND

Retinal vascular abnormalities may be associated with glaucomatous damage. Adaptive optics (AO) is a new technology that enables the analysis of retinal vasculature at the cellular level in vivo. The purpose of this study was to evaluate retinal arteriolar parameters using the rtx1 adaptive optics fundus camera (AO-FC) in patients with primary open-angle glaucoma (POAG) at different stages and to investigate the relationship between these parameters and changes in spectral-domain optical coherence tomography (SD-OCT) and perimetry.

METHODS

Parameters of the retinal supratemporal and infratemporal arterioles (wall thickness (WT), lumen diameter (LD), total diameter (TD), wall-to-lumen ratio (WLR), and cross-sectional area of the vascular wall (WCSA)) were analysed with the rtx1 in 111 POAG eyes, which were divided into three groups according to the severity of the disease, and 70 healthy eyes. The associations between RTX1 values and the cup-to-disk ratio, SD-OCT parameters, and visual field parameters were assessed.

RESULTS

Compared with the control group, the POAG groups showed significantly smaller TD and LD values (p < 0.05) and significantly higher WLR and WT values (p < 0.05) for the supratemporal and infratemporal arterioles. TD was significantly positively correlated with the retinal nerve fibre layer (RNFL) and ganglion cell complex (GCC) (p < 0.05). LD was significantly positively correlated with the RNFL, GCC, and rim area (p < 0.05). The WLR was significantly negatively correlated with the RNFL, GCC, rim area, and MD (p < 0.05), while it was significantly positively correlated with the cup-to-disc ratio and PSD (p < 0.05).

CONCLUSIONS

The results suggest that vascular dysfunction is present in POAG, even at a very early stage of glaucoma, and increases with the severity of the disease.

Retinal microvascular abnormalities in major depression

BACKGROUND

The aim of our study was to find a possible association between retinal microvascular abnormality and major depression in a non-geriatric population.

METHOD

The participants with major depression were hospitalised at the University Hospital in Hradec Kralove, Department of Psychiatry. Retinal images were obtained using a stationary Fundus camera FF450 by Zeiss and a hand-held camera by oDocs.

RESULTS

Fifty patients (men n=18, women n=32) aged 16 to 55 (men's average age 33.7±9.9 years, women's average age 37.9±11.5 years) were compared with fifty mentally healthy subjects (men n=28, women n=22) aged 18 to 61 (men's average age 35.3±9.2 years, women's average age 36.6±10.6 years) in a cross-sectional design. The patients were diagnosed with a single depressive episode (n=26) or a recurrent depressive disorder (n=24) according to the ICD-10 classification. Our results confirmed significant microvascular changes in the retina in patients with depressive disorder in comparison to the control group of mentally healthy subjects, with significantly larger arteriolar (P<0.0001) as well as venular (P<0.001-0.0001) calibres in major depression.

CONCLUSION

According to the literature, acute and chronic neuroinflammation is associated with changes in microvascular form and function. The endothelium becomes a major participant in the inflammatory response damaging the surrounding tissue and its function. Because the retina and brain tissue share a common embryonic origin, we suspect similar microvascular pathology in the retina and in the brain in major depression. Our results may contribute to a better understanding of depression etiopathogenesis and to its personalized treatment.

Retinal vessel architecture and geometry are not impaired in normal-tension glaucoma

To investigate the associations between retinal vessel parameters and normal-tension glaucoma (NTG). We conducted a case-control study with a prospective cohort, allowing to record 23 cases of NTG. We matched NTG patient with one primary open-angle glaucoma (POAG) and one control per case by age, systemic hypertension, diabetes, and refraction. Central retinal artery equivalent (CRAE), central retinal venule equivalent (CRVE), Arteriole-To-Venule ratio (AVR), Fractal Dimension and tortuosity of the vascular network were measured using VAMPIRE software. Our sample consisted of 23 NTG, 23 POAG, and 23 control individuals, with a median age of 65 years (25-75th percentile, 56-74). No significant differences were observed in median values for CRAE (130.6 µm (25-75th percentile, 122.8; 137.0) for NTG, 128.4 µm (124.0; 132.9) for POAG, and 135.3 µm (123.3; 144.8) for controls, P = .23), CRVE (172.1 µm (160.0; 188.3), 172.8 µm (163.3; 181.6), and 175.9 µm (167.6; 188.4), P = .43), AVR (0.76, 0.75, 0.74, P = .71), tortuosity and fractal parameters across study groups. Vascular morphological parameters were not significantly associated with retinal nerve fiber layer thickness or mean deviation for the NTG and POAG groups. Our results suggest that vascular dysregulation in NTG does not modify the architecture and geometry of the retinal vessel network.

The impact of the image conversion factor and image centration on retinal vessel geometric characteristics

Background

This study aims to use fundus image material from a long-term retinopathy follow-up study to identify problems created by changing imaging modalities or imaging settings (e.g., image centering, resolution, viewing angle, illumination wavelength). Investigating the relationship of image conversion factor and imaging centering on retinal vessel geometric characteristics (RVGC), offers solutions for longitudinal retinal vessel analysis for data obtained in clinical routine.

Methods

Retinal vessel geometric characteristics were analyzed in scanned fundus photographs with Singapore-I-Vessel-Assessment using a constant image conversion factor (ICF) and an individual ICF, applying them to macula centered (MC) and optic disk centered (ODC) images. The ICF is used to convert pixel measurements into μm for vessel diameter measurements and to establish the size of the measuring zone. Calculating a constant ICF, the width of all analyzed optic disks is included, and it is used for all images of a cohort. An individual ICF, in turn, uses the optic disk diameter of the eye analyzed. To investigate agreement, Bland-Altman mean difference was calculated between ODC images analyzed with individual and constant ICF and between MC and ODC images.

Results

With constant ICF (n = 104 eyes of 52 patients) the mean central retinal equivalent was 160.9 ± 17.08 μm for arteries (CRAE) and 208.7 ± 14.7.4 μm for veins (CRVE). The individual ICFs resulted in a mean CRAE of 163.3 ± 15.6 μm and a mean CRVE of 219.0 ± 22.3 μm. On Bland–Altman analysis, the individual ICF RVGC are more positive, resulting in a positive mean difference for most investigated parameters. Arteriovenous ratio (p = 0.86), simple tortuosity (p = 0.08), and fractal dimension (p = 0.80) agreed well between MC and ODC images, while the vessel diameters were significantly smaller in MC images (p &lt; 0.002).

Conclusion

Scanned images can be analyzed using vessel assessment software. Investigations of individual ICF versus constant ICF point out the asset of utilizing an individual ICF. Image settings (ODC vs. MC) were shown to have good agreement.

Quantitative Analysis of Vascular Abnormalities in Full-Term Infants With Mild Familial Exudative Vitreoretinopathy

Purpose

Our goal was to build a system that combined deep convolutional neural networks (DCNNs) and feature extraction algorithms, which automatically extracted and quantified vascular abnormalities in posterior pole retinal images of full-term infants clinically diagnosed with mild familial exudative retinopathy (FEVR).

Methods

Using posterior pole retinal images taken from 4628 full-term infants with a total of 9256 eyes, we created data sets, trained DCNNs, and performed tests and comparisons. With the segmented images, our system extracted peripapillary vascular densities, mean tortuosities, and maximum diameter ratios within the region of interest. We also compared them with normal eyes statistically.

Results

In the test data set, the trained system obtained a sensitivity of 0.78 and a specificity of 0.98 for vascular segmentation, with 0.94 and 0.99 for optic disc, respectively. While in the comparison data set, compared with normal, we found a significant increase in vascular densities in retinal images with mild FEVR (5.3211% ± 0.7600% vs. 4.5998% ± 0.6586%) and a significant increase in the maximum diameter ratios (1.8805 ± 0.3197 vs. 1.5087 ± 0.2877), while the mean tortuosities significantly decreased (2.1018 ± 0.2933 [104 cm-3] vs. 3.3344 ± 0.3890 [104 cm-3]). All values were statistically significantly different.

Conclusions

Our system could automatically segment the posterior pole retinal images and extract from vascular features associated with mild FEVR. Quantitative analysis of these parameters may help ophthalmologists in the early detection of FEVR.

Translational Relevance

This system may contribute to the early detection of FEVR and facilitate the promotion of artificial intelligence-assisted diagnostic techniques in clinical applications.

The RETA Benchmark for Retinal Vascular Tree Analysis

Topological and geometrical analysis of retinal blood vessels could be a cost-effective way to detect various common diseases. Automated vessel segmentation and vascular tree analysis models require powerful generalization capability in clinical applications. In this work, we constructed a novel benchmark RETA with 81 labelled vessel masks aiming to facilitate retinal vessel analysis. A semi-automated coarse-to-fine workflow was proposed for vessel annotation task. During database construction, we strived to control inter-annotator and intra-annotator variability by means of multi-stage annotation and label disambiguation on self-developed dedicated software. In addition to binary vessel masks, we obtained other types of annotations including artery/vein masks, vascular skeletons, bifurcations, trees and abnormalities. Subjective and objective quality validations of the annotated vessel masks demonstrated significantly improved quality over the existing open datasets. Our annotation software is also made publicly available serving the purpose of pixel-level vessel visualization. Researchers could develop vessel segmentation algorithms and evaluate segmentation performance using RETA. Moreover, it might promote the study of cross-modality tubular structure segmentation and analysis.

Concordance between SIVA, IVAN, and VAMPIRE Software Tools for Semi-Automated Analysis of Retinal Vessel Caliber

We aimed to compare measurements from three of the most widely used software packages in the literature and to generate conversion algorithms for measurement of the central retinal artery equivalent (CRAE) and central retinal vein equivalent (CRVE) between SIVA and IVAN and between SIVA and VAMPIRE. We analyzed 223 retinal photographs from 133 human participants using both SIVA, VAMPIRE and IVAN independently for computing CRAE and CRVE. Agreement between measurements was assessed using Bland–Altman plots and intra-class correlation coefficients. A conversion algorithm between measurements was carried out using linear regression, and validated using bootstrapping and root-mean-square error. The agreement between VAMPIRE and IVAN was poor to moderate: The mean difference was 20.2 µm (95% limits of agreement, LOA, −12.2–52.6 µm) for CRAE and 21.0 µm (95% LOA, −17.5–59.5 µm) for CRVE. The agreement between VAMPIRE and SIVA was also poor to moderate: the mean difference was 36.6 µm (95% LOA, −12.8–60.4 µm) for CRAE, and 40.3 µm (95% LOA, 5.6–75.0 µm) for CRVE. The agreement between IVAN and SIVA was good to excellent: the mean difference was 16.4 µm (95% LOA, −4.25–37.0 µm) for CRAE, and 19.3 µm (95% LOA, 0.09–38.6 µm) for CRVE. We propose an algorithm converting IVAN and VAMPIRE measurements into SIVA-estimated measurements, which could be used to homogenize sets of vessel measurements obtained with different software packages.

A Deep Learning System for Fully Automated Retinal Vessel Measurement in High Throughput Image Analysis

Motivation

Retinal microvasculature is a unique window for predicting and monitoring major cardiovascular diseases, but high throughput tools based on deep learning for in-detail retinal vessel analysis are lacking. As such, we aim to develop and validate an artificial intelligence system (Retina-based Microvascular Health Assessment System, RMHAS) for fully automated vessel segmentation and quantification of the retinal microvasculature.

Results

RMHAS achieved good segmentation accuracy across datasets with diverse eye conditions and image resolutions, having AUCs of 0.91, 0.88, 0.95, 0.93, 0.97, 0.95, 0.94 for artery segmentation and 0.92, 0.90, 0.96, 0.95, 0.97, 0.95, 0.96 for vein segmentation on the AV-WIDE, AVRDB, HRF, IOSTAR, LES-AV, RITE, and our internal datasets. Agreement and repeatability analysis supported the robustness of the algorithm. For vessel analysis in quantity, less than 2 s were needed to complete all required analysis.

AlzEye: longitudinal record-level linkage of ophthalmic imaging and hospital admissions of 353 157 patients in London, UK

PURPOSE

Retinal signatures of systemic disease ('oculomics') are increasingly being revealed through a combination of high-resolution ophthalmic imaging and sophisticated modelling strategies. Progress is currently limited not mainly by technical issues, but by the lack of large labelled datasets, a sine qua non for deep learning. Such data are derived from prospective epidemiological studies, in which retinal imaging is typically unimodal, cross-sectional, of modest number and relates to cohorts, which are not enriched with subpopulations of interest, such as those with systemic disease. We thus linked longitudinal multimodal retinal imaging from routinely collected National Health Service (NHS) data with systemic disease data from hospital admissions using a privacy-by-design third-party linkage approach.

PARTICIPANTS

Between 1 January 2008 and 1 April 2018, 353 157 participants aged 40 years or older, who attended Moorfields Eye Hospital NHS Foundation Trust, a tertiary ophthalmic institution incorporating a principal central site, four district hubs and five satellite clinics in and around London, UK serving a catchment population of approximately six million people.

FINDINGS TO DATE

Among the 353 157 individuals, 186 651 had a total of 1 337 711 Hospital Episode Statistics admitted patient care episodes. Systemic diagnoses recorded at these episodes include 12 022 patients with myocardial infarction, 11 735 with all-cause stroke and 13 363 with all-cause dementia. A total of 6 261 931 retinal images of seven different modalities and across three manufacturers were acquired from 1 54 830 patients. The majority of retinal images were retinal photographs (n=1 874 175) followed by optical coherence tomography (n=1 567 358).

FUTURE PLANS

AlzEye combines the world's largest single institution retinal imaging database with nationally collected systemic data to create an exceptional large-scale, enriched cohort that reflects the diversity of the population served. First analyses will address cardiovascular diseases and dementia, with a view to identifying hidden retinal signatures that may lead to earlier detection and risk management of these life-threatening conditions.

Comparing Measurements of Vascular Diameter Using Adaptative Optics Imaging and Conventional Fundus Imaging

The aim of this prospective study was to compare retinal vascular diameter measurements taken from standard fundus images and adaptive optics (AO) images. We analysed retinal images of twenty healthy subjects with 45-degree funduscopic colour photographs (CR-2 Canon fundus camera, Canon™) and adaptive optics (AO) fundus images (rtx1 camera, Imagine Eyes^®). Diameters were measured using three software applications: the VAMPIRE (Vessel Assessment and Measurement Platform for Images of the REtina) annotation tool, IVAN (Interactive Vessel ANalyzer) for funduscopic colour photographs, and AO_Detect_Artery™ for AO images. For the arterial diameters, the mean difference between AO_Detect_Artery™ and IVAN was 9.1 µm (−27.4 to 9.2 µm, p = 0.005) and the measurements were significantly correlated (r = 0.79). The mean difference between AO_Detect_Artery™ and VAMPIRE annotation tool was 3.8 µm (−34.4 to 26.8 µm, p = 0.16) and the measurements were poorly correlated (r = 0.12). For the venous diameters, the mean difference between the AO_Detect_Artery™ and IVAN was 3.9 µm (−40.9 to 41.9 µm, p = 0.35) and the measurements were highly correlated (r = 0.83). The mean difference between the AO_Detect_Artery™ and VAMPIRE annotation tool was 0.4 µm (−17.44 to 25.3 µm, p = 0.91) and the correlations were moderate (r = 0.41). We found that the VAMPIRE annotation tool, an entirely manual software, is accurate for the measurement of arterial and venular diameters, but the correlation with AO measurements is poor. On the contrary, IVAN, a semi-automatic software tool, presents slightly greater differences with AO imaging, but the correlation is stronger. Data from arteries should be considered with caution, since IVAN seems to significantly under-estimate arterial diameters.

Detection and Grading of Hypertensive Retinopathy Using Vessels Tortuosity and Arteriovenous Ratio

Hypertensive retinopathy (HR) refers to changes in the morphological diameter of the retinal vessels due to persistent high blood pressure. Early detection of such changes helps in preventing blindness or even death due to stroke. These changes can be quantified by computing the arteriovenous ratio and the tortuosity severity in the retinal vasculature. This paper presents a decision support system for detecting and grading HR using morphometric analysis of retinal vasculature, particularly measuring the arteriovenous ratio (AVR) and retinal vessel tortuosity. In the first step, the retinal blood vessels are segmented and classified as arteries and veins. Then, the width of arteries and veins is measured within the region of interest around the optic disk. Next, a new iterative method is proposed to compute the AVR from the caliber measurements of arteries and veins using Parr-Hubbard and Knudtson methods. Moreover, the retinal vessel tortuosity severity index is computed for each image using 14 tortuosity severity metrics. In the end, a hybrid decision support system is proposed for the detection and grading of HR using AVR and tortuosity severity index. Furthermore, we present a new publicly available retinal vessel morphometry (RVM) dataset to evaluate the proposed methodology. The RVM dataset contains 504 retinal images with pixel-level annotations for vessel segmentation, artery/vein classification, and optic disk localization. The image-level labels for vessel tortuosity index and HR grade are also available. The proposed methods of iterative AVR measurement, tortuosity index, and HR grading are evaluated using the new RVM dataset. The results indicate that the proposed method gives superior performance than existing methods. The presented methodology is a novel advancement in automated detection and grading of HR, which can potentially be used as a clinical decision support system.

Changes in retinal vascular diameters in senior and geriatric cats in association with variation in systemic blood pressure

OBJECTIVES

Early diagnosis of arterial hypertension is essential to prevent target organ damage. In humans, retinal arteriolar narrowing predicts hypertension. This blinded prospective observational study investigated the retinal vessel diameters in senior and geriatric cats of varying systolic blood pressure (SBP) status and evaluated retinal vascular changes in hypertensive cats after treatment.

METHODS

Cats with a median age of 14 years (range 9.1-22 years) were categorised into five groups: group 1, healthy normotensive (SBP <140 mmHg; n = 40) cats; group 2, pre-hypertensive (SBP 140-160 mmHg; n = 14) cats; group 3, cats with chronic kidney disease (CKD) and normotensive (n = 26); group 4, cats with CKD and pre-hypertensive (n = 13); and group 5, hypertensive cats (SBP >160 mmHg, n = 15). Colour fundus images (Optibrand ClearView) were assessed for hypertensive lesions. Retinal vascular diameters and bifurcation angles were annotated and calculated using the Vascular Assessment and Measurement Platform for Images of the Retina annotation tool (VAMPIRE-AT). When available, measurements were obtained at 3 and 6 months after amlodipine besylate treatment.

RESULTS

Ten hypertensive cats had retinal lesions, most commonly intraretinal haemorrhages and retinal exudates. Arteriole and venule diameters decreased significantly with increasing age (-0.17 ± 0.05 pixels/year [P = 0.0004]; -0.19 ± 0.05 pixels/year). Adjusted means ± SEM for arteriole and venule diameter (pixels) were 6.3 ± 0.2 and 8.9 ± 0.2 (group 1); 7.6 ± 0.3 and 10.1 ± 0.4 (group 2); 6.9 ± 0.2 and 9.5 ± 0.3 (group 3); 7.4 ± 0.3 and 10.0 ± 0.4 (group 4); and 7.0 ± 0.3 and 9.8 ± 0.4 (group 5). Group 1 arteriole and venule diameters were significantly lower than those of groups 2 and 4. Group 2 arteriole bifurcation angle was significantly narrower than those of groups 1 and 3. Post-treatment, vessel diameters decreased significantly at 3 and 6 months in seven hypertensive cats.

CONCLUSIONS AND RELEVANCE

Increased age was associated with reduced vascular diameters. Longitudinal studies are required to assess if vessel diameters are a risk indicator for hypertension in cats.

A Hybrid Method to Enhance Thick and Thin Vessels for Blood Vessel Segmentation

Retinal blood vessels have been presented to contribute confirmation with regard to tortuosity, branching angles, or change in diameter as a result of ophthalmic disease. Although many enhancement filters are extensively utilized, the Jerman filter responds quite effectively at vessels, edges, and bifurcations and improves the visualization of structures. In contrast, curvelet transform is specifically designed to associate scale with orientation and can be used to recover from noisy data by curvelet shrinkage. This paper describes a method to improve the performance of curvelet transform further. A distinctive fusion of curvelet transform and the Jerman filter is presented for retinal blood vessel segmentation. Mean-C thresholding is employed for the segmentation purpose. The suggested method achieves average accuracies of 0.9600 and 0.9559 for DRIVE and CHASE_DB1, respectively. Simulation results establish a better performance and faster implementation of the suggested scheme in comparison with similar approaches seen in the literature.

Imaging the eye and its relevance to diabetes care

Diabetes is a major cause of vision loss globally, yet this devastating complication is largely preventable. Early detection and treatment of diabetic retinopathy necessitates screening. Ocular imaging is widely used clinically, both for the screening and management of diabetic retinopathy. Common eye conditions, such as glaucoma, cataracts and retinal vessel thrombosis, and signs of systemic conditions, such as hypertension, are frequently revealed. As well as imaging by a skilled clinician during an eye examination, non-ophthalmic clinicians, such as general practitioners, endocrinologists, nurses and trained health workers, can also can carry out diabetic eye screening. This process usually comprises local imaging with remote grading, mostly human grading. However, grading incorporating artificial intelligence is emerging. In a clinical research context, retinal vasculature analyses using semi-automated software in many populations have identified associations between retinal vessel geometry, such as vessel caliber, and the risk of diabetic retinopathy and other chronic complications of type 1 and type 2 diabetes. Similarly, evaluation of corneal nerves by corneal confocal microscopy is revealing diabetes-related abnormalities, and associations with and predictive power for other chronic diabetes complications. As yet, the value of retinal vessel geometry and corneal confocal microscopy measures at an individual level is uncertain. In this article, targeting non-ocular clinicians and researchers, we review existent and emerging ocular imaging and grading tools, including artificial intelligence, and their associations between ocular imaging findings and diabetes and its chronic complications.

Retinal Vessel Phenotype in Patients with a History of Retinal Vein Occlusion

INTRODUCTION

The aim of the study was to estimate the phenotype of retinal vessels using central retinal artery equivalent (CRAE), central retinal vein equivalent (CRVE), tortuosity, and fractal analysis in the unaffected contralateral eye of patients with central or branch retinal vein occlusion (CRVO or BRVO).

METHODS

Thirty-four patients suffering from CRVO, 15 suffering from BRVO, and 49 controlled matched subjects had a fundus image analyzed using the VAMPIRE software. The intraclass correlation coefficient and a Bland-Altman plot were done for the reproducibility study.

RESULTS

There was a lack of evidence of difference between the control group and the CRVO group for CRAE (p = 0.06), CRVE (p = 0.3), and arterio-venule ratio (AVR, p = 0.6). Contralateral eyes of CRVO exhibited a significantly higher arterial and minimum arterial tortuosity values (p = 0.012), as compared with control eyes. Contralateral eyes of patients with a history of BRVO had a significantly higher CRAE (p = 0.02), AVR (p = 0.006), and minimal arterial tortuosity (p = 0.05). Fractal analysis showed that contralateral eyes of BRVO had higher values of fractal parameters (D0a, p = 0.005).

CONCLUSION

This study suggests that CVRO or BRVO is not triggered by the same retinal vascular phenotypes in the contralateral eye. The morphology of retinal vasculature may be associated with the occurrence of RVO, independently of known risk factors.

Retinal arteriolar calibre and venular fractal dimension predict progression of proliferative diabetic retinopathy 6 months after panretinal photocoagulation: a prospective, clinical interventional study

Objective

We examined the hypothesis that baseline retinal vascular geometry in patients with proliferative diabetic retinopathy (PDR) predicts disease activity 6 months after panretinal photocoagulation (PRP).

Methods and analysis

We included 47 eyes from 40 patients with treatment-naïve PDR in a 6-month prospective study. Diagnosis of PDR and disease activity was evaluated by wide-field fluorescein angiography (Optomap, Optos, Dunfermline, Scotland, UK). At baseline and 6-month follow-up, the retinal vessel geometry was measured on optic disc centred images using semiautomated software Vessel Assessment and Measurement Platform for Images of the Retina (VAMPIRE, Dundee, Scotland).

Results

At baseline, mean age and duration of diabetes was 51.6 and 21.4 years, and 62.5% were men. Seventeen eyes (36.2%) had progression of PDR during follow-up. At baseline, we found higher retinal arteriolar calibre (31.3±0.8 vs 28.8±0.8 pixels, p=0.02) and venous fractal dimension (F_D) (1.257±0.011 vs 1.222±0.011, p=0.02) in eyes with progression of PDR as compared with eyes with non-progression. In a multiple logistic regression model, both higher retinal arteriolar calibre (OR 1.34, 95% CI, 1.09 to 1.64, p<0.01) and venular F_D (OR 1.15, 95% CI, 1.04 to 1.27, p<0.01) predicted progression of PDR. Venular calibre was seen to increase from baseline to month six regardless of disease progression (non-progression 45.0±0.7 vs 52.7±1.8 pixels, p<0.01; progression 46.2±0.8 vs 51.0±1.7 pixels, p<0.01).

Conclusion

Our prospective study showed that arteriolar calibre and venular F_D at baseline were predictive of disease activity 6 months after PRP treatment in patients with treatment-naïve PDR.

Retinal Vascular Fractal Dimensions and Their Association with Macrovascular Cardiac Disease

INTRODUCTION

As the only part of the human vasculature, the retina is available for direct, noninvasive inspection. Retinal vascular fractal dimension (DF) is a method to measure the structure of the retinal vascular tree, with higher noninteger values between 1 and 2 representing a more complex and dense retinal vasculature. Retinal vascular structure has been associated with a variety of systemic diseases, and this study examined the association of DF and macrovascular cardiac disease in a case-control design.

METHODS

Retinal fundus photos were captured with Topcon TRC-50X in 38 persons that had coronary artery bypass grafting (CABG, cases) and 37 cardiovascular healthy controls. The semiautomatic software VAMPIRE was used to measure retinal DF.

RESULTS

Patients with CABG had lower DF of the retinal main venular vessels compared to the control group (1.15 vs. 1.18, p = 0.01). In a multivariable regression model adjusted for gender and age, eyes in the fourth quartile with higher DF were less likely to have CABG compared to patients in the first (OR, 7.20; 95% confidence interval: 1.63-31.86; p = 0.009) and second (OR, 8.25; 95% confidence interval: 1.70-40.01; p = 0.009) quartiles.

CONCLUSIONS

This study demonstrates that lower complexity of main venular vessels associates with higher risk of having CABG. The research supports the hypothesis that the retinal vascular structure can be used to assess nonocular macrovascular disease.

Retinal vascular caliber changes after laser photocoagulation in diabetic retinopathy

BACKGROUND Diabetic retinopathy causes vascular dilatation caused by hypoxia, whereas oxygen tension improvement leads to retinal vessels narrowing. Given that laser photocoagulation aims to increase the oxygen tension in the retina, we hypothesized that the narrowing of vessel caliber after the treatment could be possibly demonstrated. This study aimed to assess the changes in the caliber of retinal vessels before and after laser photocoagulation in diabetic retinopathy. METHODS This research was a prospective cohort study on the treatment of diabetic retinopathy by laser photocoagulation, and it was conducted at Universitas Hasanuddin Hospital, Makassar, Indonesia between November 2017–April 2018. Retinal vascular caliber changes were analyzed before and 6–8 weeks after photocoagulation in 30 diabetic eyes. Central retinal arteriolar equivalent (CRAE) and central retinal venular equivalent (CRVE) were measured using the vessel assessment and measurement platform software for images of the retina (VAMPIRE) manual annotation tool. RESULTS A significant decrease of CRVE was observed after laser photocoagulation (p<0.001), but CRAE was not reduced significantly (p = 0.067). No difference was recorded between CRVE and CRAE post-laser photocoagulation (p = 0.14), implying a reduction in vein caliber toward normal in the treated eyes. CONCLUSIONS Laser photocoagulation decreases the CRVE in diabetic retinopathy despite the absence of changes in the grade of diabetic retinopathy.

Retinal vascular fractal dimension and cerebral blood flow, a pilot study

PURPOSE

Ocular and brain microcirculation share embryological and histological similarities. The retinal vascular fractal dimension (FD) is a marker of retinal vascular complexity of the vascular tree. The purpose of this study was to explore the relationship between cerebral blood flow (CBF), retinal vascular FD and other retinal vascular markers.

METHODS

Cross-sectional analysis comprising 26 individuals ≥65 years old from the Cognitive REServe and Clinical ENDOphenotype (CRESCENDO) cohort of relative healthy older adults. Retinal vascular FD was measured from fundus photographs by using the semi-automated Singapore Eye Vessel Assessment (SIVA) software. CBF was estimated using a 2D pulsed ASL MRI sequence. Associations between blood flow and retinal parameters were analysed using linear regression models adjusted for age and sex.

RESULTS

Cerebral blood flow was positively associated with venular FD (R²  = 0.32, p = 0.03). This association was stronger in the anterior versus posterior brain territories (R²  = 0.35 [p = 0.001] versus R²  = 0.16 [p = 0.07], respectively). Global CBF was correlated with arteriolar branching angle (R²  = 0.23, p = 0.01) and tortuosity (R²  = 0.20, p = 0.02). Global CBF was not correlated with other SIVA parameters.

CONCLUSIONS

Retinal venular complexity summarized by the FD was associated with cerebral blood flow as well as retinal arteriolar tortuosity and branching angle. Larger prospective clinical studies are needed to confirm these results.

Multiloss Function Based Deep Convolutional Neural Network for Segmentation of Retinal Vasculature into Arterioles and Venules

The arterioles and venules (AV) classification of retinal vasculature is considered as the first step in the development of an automated system for analysing the vasculature biomarker association with disease prognosis. Most of the existing AV classification methods depend on the accurate segmentation of retinal blood vessels. Moreover, the unavailability of large-scale annotated data is a major hindrance in the application of deep learning techniques for AV classification. This paper presents an encoder-decoder based fully convolutional neural network for classification of retinal vasculature into arterioles and venules, without requiring the preliminary step of vessel segmentation. An optimized multiloss function is used to learn the pixel-wise and segment-wise retinal vessel labels. The proposed method is trained and evaluated on DRIVE, AVRDB, and a newly created AV classification dataset; and it attains 96%, 98%, and 97% accuracy, respectively. The new AV classification dataset is comprised of 700 annotated retinal images, which will offer the researchers a benchmark to compare their AV classification results.

Vascular changes in diabetic retinopathy-a longitudinal study in the Nile rat

Diabetic retinopathy is the most common microvascular complication of diabetes and is a major cause of blindness, but an understanding of the pathogenesis of the disease has been hampered by a lack of accurate animal models. Here, we explore the dynamics of retinal cellular changes in the Nile rat (Arvicanthis niloticus), a carbohydrate-sensitive model for type 2 diabetes. The early retinal changes in diabetic Nile rats included increased acellular capillaries and loss of pericytes that correlated linearly with the duration of diabetes. These vascular changes occurred in the presence of microglial infiltration but in the absence of retinal ganglion cell loss. After a prolonged duration of diabetes, the Nile rat also exhibits a spectrum of retinal lesions commonly seen in the human condition including vascular leakage, capillary non-perfusion, and neovascularization. Our longitudinal study documents a range and progression of retinal lesions in the diabetic Nile rat remarkably similar to those observed in human diabetic retinopathy, and suggests that this model will be valuable in identifying new therapeutic strategies.

Towards Accurate Segmentation of Retinal Vessels and the Optic Disc in Fundoscopic Images with Generative Adversarial Networks

Automatic segmentation of the retinal vasculature and the optic disc is a crucial task for accurate geometric analysis and reliable automated diagnosis. In recent years, Convolutional Neural Networks (CNN) have shown outstanding performance compared to the conventional approaches in the segmentation tasks. In this paper, we experimentally measure the performance gain for Generative Adversarial Networks (GAN) framework when applied to the segmentation tasks. We show that GAN achieves statistically significant improvement in area under the receiver operating characteristic (AU-ROC) and area under the precision and recall curve (AU-PR) on two public datasets (DRIVE, STARE) by segmenting fine vessels. Also, we found a model that surpassed the current state-of-the-art method by 0.2 - 1.0% in AU-ROC and 0.8 - 1.2% in AU-PR and 0.5 - 0.7% in dice coefficient. In contrast, significant improvements were not observed in the optic disc segmentation task on DRIONS-DB, RIM-ONE (r3) and Drishti-GS datasets in AU-ROC and AU-PR.

Retinal microvasculature and cerebral small vessel disease in the Lothian Birth Cohort 1936 and Mild Stroke Study

Research has suggested that the retinal vasculature may act as a surrogate marker for diseased cerebral vessels. Retinal vascular parameters were measured using Vessel Assessment and Measurement Platform for Images of the Retina (VAMPIRE) software in two cohorts: (i) community-dwelling older subjects of the Lothian Birth Cohort 1936 (n = 603); and (ii) patients with recent minor ischaemic stroke of the Mild Stroke Study (n = 155). Imaging markers of small vessel disease (SVD) (white matter hyperintensities [WMH] on structural MRI, visual scores and volume; perivascular spaces; lacunes and microbleeds), and vascular risk measures were assessed in both cohorts. We assessed associations between retinal and brain measurements using structural equation modelling and regression analysis. In the Lothian Birth Cohort 1936 arteriolar fractal dimension accounted for 4% of the variance in WMH load. In the Mild Stroke Study lower arteriolar fractal dimension was associated with deep WMH scores (odds ratio [OR] 0.53; 95% CI, 0.32–0.87). No other retinal measure was associated with SVD. Reduced fractal dimension, a measure of vascular complexity, is related to SVD imaging features in older people. The results provide some support for the use of the retinal vasculature in the study of brain microvascular disease.

Towards Standardization of Quantitative Retinal Vascular Parameters: Comparison of SIVA and VAMPIRE Measurements in the Lothian Birth Cohort 1936

Purpose

Semiautomated software applications derive quantitative retinal vascular parameters from fundus camera images. However, the extent of agreement between measurements from different applications is unclear. We evaluate the agreement between retinal measures from two software applications, the Singapore "I" Vessel Assessment (SIVA) and the Vessel Assessment and Measurement Platform for Images of the Retina (VAMPIRE), and examine respective associations between retinal and systemic outcomes.

Method

Fundus camera images from 665 Lothian Birth Cohort 1936 participants were analyzed with SIVA and VAMPIRE. Intraclass correlation coefficients (ICC) and Bland-Altman plots assessed agreement between retinal parameters: measurements of vessel width, fractal dimension, and tortuosity. Retinal-systemic variable associations were assessed with Pearson's correlation, and intersoftware correlation magnitude differences were examined with Williams's test.

Results

ICC values indicated poor to limited agreement for all retinal parameters (0.159-0.410). Bland-Altman plots revealed proportional bias in the majority, and systematic bias in all measurements. SIVA and VAMPIRE measurements were associated most consistently with systemic variables relating to blood pressure (SIVA r's from -0.122 to -0.183; VAMPIRE r's from -0.078 to -0.177). Williams's tests indicated significant differences in the magnitude of association between retinal and systemic variables for 7 of 77 comparisons (P < 0.05).

Conclusions

Agreement between two common software applications was poor. Further studies are required to determine whether associations with systemic variables are software-dependent.

Translational Relevance

Standardization of the measurement of retinal vascular parameters is warranted to ensure that they are reliable and application-independent. This would be an important step towards realizing the potential of the retina as a source of imaging-derived biomarkers that are clinically useful.

Determinants of retinal microvascular features and their relationships in two European populations

Supplemental Digital Content is available in the text

Objectives:

To examine factors influencing retinal vasculature in two environmentally contrasted, cross-sectional studies of adult participants of European descent and to estimate the extent and specificity of genetic contributions to each retinal vasculature feature.

Methods:

Retinal images from 1088 participants in the Orkney Complex Disease Study and 387 in the CROATIA-Korčula study, taken using the same nonmydriatic camera system and graded by the same person, were evaluated. Using general linear models, we estimated the influence of an extensive range of systemic risk factors, calculated retinal traits heritabilities and genetic correlations.

Main results:

Systemic covariates explained little (<4%) of the variation in vessel tortuosity, substantially more (>10%, up to 31.7%) of the variation in vessel width and monofractal dimension. Suggestive not well trodden associations of biological interest included that of urate, tissue plasminogen activator and cardiac PR interval with arteriolar narrowing, that of carotid intima–media thickness with less-tortuous arterioles and of cardiac QT interval with more tortuous venules. The genetic underpinning of tortuosity is largely distinct from that of the other retinal vascular features, whereas that of fractal dimension and vessel width greatly overlaps. The previously recognized influence of ocular axial length on vessel widths was high and can be expected to lead to artefactual genetic associations [genetic correlation with central retinal arteriolar equivalent: −0.53 (standard error 0.11)]. The significant genetic correlation between SBP and central retinal arteriolar equivalent, −0.53 (standard error 0.22) (after adjusting for age, sex and axial length of the eye), augurs more favourably for the discovery of genetic variants relevant to vascular physiology.

Retinal vasculature in glaucoma: a review

Despite the critical impact of glaucoma on global blindness, its aetiology is not fully characterised. Elevated intraocular pressure is highly associated with glaucomatous optic neuropathy. However, visual field loss still progresses in some patients with normal or even low intraocular pressure. Vascular factors have been suggested to play a role in glaucoma development, based on numerous studies showing associations of glaucoma with blood pressure, ocular perfusion pressure, vasospasm, cardiovascular disease and ocular blood flow. As the retinal vasculature is the only part of the human circulation that readily allows non-invasive visualisation of the microcirculation, a number of quantitative retinal vascular parameters measured from retinal photographs using computer software (eg, calibre, fractal dimension, tortuosity and branching angle) are currently being explored for any association with glaucoma and its progression. Several population-based and clinical studies have reported that changes in retinal vasculature (eg, retinal arteriolar narrowing and decreased fractal dimension) are associated with optic nerve damage and glaucoma, supporting the vascular theory of glaucoma pathogenesis. This review summarises recent findings on the relationships between quantitatively measured structural retinal vascular changes with glaucoma and other markers of optic nerve head damage, including retinal nerve fibre layer thickness. Clinical implications, recent new advances in retinal vascular imaging (eg, optical coherence tomography angiography) and future research directions are also discussed.

The application of retinal fundus camera imaging in dementia: A systematic review

Introduction

The ease of imaging the retinal vasculature, and the evolving evidence suggesting this microvascular bed might reflect the cerebral microvasculature, presents an opportunity to investigate cerebrovascular disease and the contribution of microvascular disease to dementia with fundus camera imaging.

Methods

A systematic review and meta-analysis was carried out to assess the measurement of retinal properties in dementia using fundus imaging.

Results

Ten studies assessing retinal properties in dementia were included. Quantitative measurement revealed significant yet inconsistent pathologic changes in vessel caliber, tortuosity, and fractal dimension. Retinopathy was more prevalent in dementia. No association of age-related macular degeneration with dementia was reported.

Discussion

Inconsistent findings across studies provide tentative support for the application of fundus camera imaging as a means of identifying changes associated with dementia. The potential of fundus image analysis in differentiating between dementia subtypes should be investigated using larger well-characterized samples. Future work should focus on refining and standardizing methods and measurements.

A review on automatic analysis techniques for color fundus photographs

In this paper, we give a review on automatic image processing tools to recognize diseases causing specific distortions in the human retina. After a brief summary of the biology of the retina, we give an overview of the types of lesions that may appear as biomarkers of both eye and non-eye diseases. We present several state-of-the-art procedures to extract the anatomic components and lesions in color fundus photographs and decision support methods to help clinical diagnosis. We list publicly available databases and appropriate measurement techniques to compare quantitatively the performance of these approaches. Furthermore, we discuss on how the performance of image processing-based systems can be improved by fusing the output of individual detector algorithms. Retinal image analysis using mobile phones is also addressed as an expected future trend in this field.

Computer-aided diagnosis of retinopathy in retinal fundus images of preterm infants via quantification of vascular tortuosity

Retinopathy of prematurity (ROP), a disorder of the retina occurring in preterm infants, is the leading cause of preventable childhood blindness. An active phase of ROP that requires treatment is associated with the presence of plus disease, which is diagnosed clinically in a qualitative manner by visual assessment of the existence of a certain level of increase in the thickness and tortuosity of retinal vessels. The present study performs computer-aided diagnosis (CAD) of plus disease via quantitative measurement of tortuosity in retinal fundus images of preterm infants. Digital image processing techniques were developed for the detection of retinal vessels and measurement of their tortuosity. The total lengths of abnormally tortuous vessels in each quadrant and the entire image were then computed. A minimum-length diagnostic-decision-making criterion was developed to assess the diagnostic sensitivity and specificity of the values obtained. The area ([Formula: see text]) under the receiver operating characteristic curve was used to assess the overall diagnostic accuracy of the methods. Using a set of 19 retinal fundus images of preterm infants with plus disease and 91 without plus disease, the proposed methods provided an overall diagnostic accuracy of [Formula: see text]. Using the total length of all abnormally tortuous vessel segments in an image, our techniques are capable of CAD of plus disease with high accuracy without the need for manual selection of vessels to analyze. The proposed methods may be used in a clinical or teleophthalmological setting.

Multiquadric Spline-Based Interactive Segmentation of Vascular Networks

Commonly used drawing tools for interactive image segmentation and labeling include active contours or boundaries, scribbles, rectangles and other shapes. Thin vessel shapes in images of vascular networks are difficult to segment using automatic or interactive methods. This paper introduces the novel use of a sparse set of user-defined seed points (supervised labels) for precisely, quickly and robustly segmenting complex biomedical images. A multiquadric spline-based binary classifier is proposed as a unique approach for interactive segmentation using as features color values and the location of seed points. Epifluorescence imagery of the dura mater microvasculature are difficult to segment for quantitative applications due to challenging tissue preparation, imaging conditions, and thin, faint structures. Experimental results based on twenty epifluorescence images is used to illustrate the benefits of using a set of seed points to obtain fast and accurate interactive segmentation compared to four interactive and automatic segmentation approaches.

Modulation of retinal image vasculature analysis to extend utility and provide secondary value from optical coherence tomography imaging

Retinal image analysis is emerging as a key source of biomarkers of chronic systemic conditions affecting the cardiovascular system and brain. The rapid development and increasing diversity of commercial retinal imaging systems present a challenge to image analysis software providers. In addition, clinicians are looking to extract maximum value from the clinical imaging taking place. We describe how existing and well-established retinal vasculature segmentation and measurement software for fundus camera images has been modulated to analyze scanning laser ophthalmoscope retinal images generated by the dual-modality Heidelberg SPECTRALIS(®) instrument, which also features optical coherence tomography.

Automated retinal vessel recognition and measurements on large datasets

The characteristics of the retinal vascular network have been prospectively associated with many systemic and vascular diseases. QUARTZ is a fully automated software that has been developed to localize and quantify the morphological characteristics of blood vessels in retinal images for use in epidemiological studies. This software was used to analyse a dataset containing 16,000 retinal images from the EPIC-Norfolk cohort study. The objective of this paper is to both assess the suitability of this dataset for computational analysis and to further evaluate the QUARTZ software.

Computer-Based Image Analysis for Plus Disease Diagnosis in Retinopathy of Prematurity: Performance of the "i-ROP" System and Image Features Associated With Expert Diagnosis

PURPOSE

We developed and evaluated the performance of a novel computer-based image analysis system for grading plus disease in retinopathy of prematurity (ROP), and identified the image features, shapes, and sizes that best correlate with expert diagnosis.

METHODS

A dataset of 77 wide-angle retinal images from infants screened for ROP was collected. A reference standard diagnosis was determined for each image by combining image grading from 3 experts with the clinical diagnosis from ophthalmoscopic examination. Manually segmented images were cropped into a range of shapes and sizes, and a computer algorithm was developed to extract tortuosity and dilation features from arteries and veins. Each feature was fed into our system to identify the set of characteristics that yielded the highest-performing system compared to the reference standard, which we refer to as the "i-ROP" system.

RESULTS

Among the tested crop shapes, sizes, and measured features, point-based measurements of arterial and venous tortuosity (combined), and a large circular cropped image (with radius 6 times the disc diameter), provided the highest diagnostic accuracy. The i-ROP system achieved 95% accuracy for classifying preplus and plus disease compared to the reference standard. This was comparable to the performance of the 3 individual experts (96%, 94%, 92%), and significantly higher than the mean performance of 31 nonexperts (81%).

CONCLUSIONS

This comprehensive analysis of computer-based plus disease suggests that it may be feasible to develop a fully-automated system based on wide-angle retinal images that performs comparably to expert graders at three-level plus disease discrimination.

TRANSLATIONAL RELEVANCE

Computer-based image analysis, using objective and quantitative retinal vascular features, has potential to complement clinical ROP diagnosis by ophthalmologists.

Automatic retinal vessel classification using a Least Square-Support Vector Machine in VAMPIRE

It is important to classify retinal blood vessels into arterioles and venules for computerised analysis of the vasculature and to aid discovery of disease biomarkers. For instance, zone B is the standardised region of a retinal image utilised for the measurement of the arteriole to venule width ratio (AVR), a parameter indicative of microvascular health and systemic disease. We introduce a Least Square-Support Vector Machine (LS-SVM) classifier for the first time (to the best of our knowledge) to label automatically arterioles and venules. We use only 4 image features and consider vessels inside zone B (802 vessels from 70 fundus camera images) and in an extended zone (1,207 vessels, 70 fundus camera images). We achieve an accuracy of 94.88% and 93.96% in zone B and the extended zone, respectively, with a training set of 10 images and a testing set of 60 images. With a smaller training set of only 5 images and the same testing set we achieve an accuracy of 94.16% and 93.95%, respectively. This experiment was repeated five times by randomly choosing 10 and 5 images for the training set. Mean classification accuracy are close to the above mentioned result. We conclude that the performance of our system is very promising and outperforms most recently reported systems. Our approach requires smaller training data sets compared to others but still results in a similar or higher classification rate.

Novel Method for Automated Analysis of Retinal Images: Results in Subjects with Hypertensive Retinopathy and CADASIL

Morphological analysis of the retinal vessels by fundoscopy provides noninvasive means for detecting and staging systemic microvascular damage. However, full exploitation of fundoscopy in clinical settings is limited by paucity of quantitative, objective information obtainable through the observer-driven evaluations currently employed in routine practice. Here, we report on the development of a semiautomated, computer-based method to assess retinal vessel morphology. The method allows simultaneous and operator-independent quantitative assessment of arteriole-to-venule ratio, tortuosity index, and mean fractal dimension. The method was implemented in two conditions known for being associated with retinal vessel changes: hypertensive retinopathy and Cerebral Autosomal Dominant Arteriopathy with Subcortical Infarcts and Leukoencephalopathy (CADASIL). The results showed that our approach is effective in detecting and quantifying the retinal vessel abnormalities. Arteriole-to-venule ratio, tortuosity index, and mean fractal dimension were altered in the subjects with hypertensive retinopathy or CADASIL with respect to age- and gender-matched controls. The interrater reliability was excellent for all the three indices (intraclass correlation coefficient ≥ 85%). The method represents simple and highly reproducible means for discriminating pathological conditions characterized by morphological changes of retinal vessels. The advantages of our method include simultaneous and operator-independent assessment of different parameters and improved reliability of the measurements.

Suitability of UK Biobank Retinal Images for Automatic Analysis of Morphometric Properties of the Vasculature

Purpose

To assess the suitability of retinal images held in the UK Biobank - the largest retinal data repository in a prospective population-based cohort - for computer assisted vascular morphometry, generating measures that are commonly investigated as candidate biomarkers of systemic disease.

Methods

Non-mydriatic fundus images from both eyes of 2,690 participants - people with a self-reported history of myocardial infarction (n=1,345) and a matched control group (n=1,345) - were analysed using VAMPIRE software. These images were drawn from those of 68,554 UK Biobank participants who underwent retinal imaging at recruitment. Four operators were trained in the use of the software to measure retinal vascular tortuosity and bifurcation geometry.

Results

Total operator time was approximately 360 hours (4 minutes per image). 2,252 (84%) of participants had at least one image of sufficient quality for the software to process, i.e. there was sufficient detection of retinal vessels in the image by the software to attempt the measurement of the target parameters. 1,604 (60%) of participants had an image of at least one eye that was adequately analysed by the software, i.e. the measurement protocol was successfully completed. Increasing age was associated with a reduced proportion of images that could be processed (p=0.0004) and analysed (p<0.0001). Cases exhibited more acute arteriolar branching angles (p=0.02) as well as lower arteriolar and venular tortuosity (p<0.0001).

Conclusions

A proportion of the retinal images in UK Biobank are of insufficient quality for automated analysis. However, the large size of the UK Biobank means that tens of thousands of images are available and suitable for computational analysis. Parametric information measured from the retinas of participants with suspected cardiovascular disease was significantly different to that measured from a matched control group.

Retinal changes in visceral leishmaniasis by retinal photography

Background

In visceral leishmaniasis (VL), retinal changes have previously been noted but not described in detail and their clinical and pathological significance are unknown. A prospective observational study was undertaken in Mymensingh, Bangladesh aiming to describe in detail visible changes in the retina in unselected patients with VL.

Methods

Patients underwent assessment of visual function, indirect and direct ophthalmoscopy and portable retinal photography. The photographs were assessed by masked observers including assessment for vessel tortuosity using a semi-automated system.

Results

30 patients with VL were enrolled, of whom 6 (20%) had abnormalities. These included 5 with focal retinal whitening, 2 with cotton wool spots, 2 with haemorrhages, as well as increased vessel tortuosity. Visual function was preserved.

Conclusions

These changes suggest a previously unrecognized retinal vasculopathy. An inflammatory aetiology is plausible such as a subclinical retinal vasculitis, possibly with altered local microvascular autoregulation, and warrants further investigation.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2334-14-527) contains supplementary material, which is available to authorized users.

Accurate and reliable segmentation of the optic disc in digital fundus images

We describe a complete pipeline for the detection and accurate automatic segmentation of the optic disc in digital fundus images. This procedure provides separation of vascular information and accurate inpainting of vessel-removed images, symmetry-based optic disc localization, and fitting of incrementally complex contour models at increasing resolutions using information related to inpainted images and vessel masks. Validation experiments, performed on a large dataset of images of healthy and pathological eyes, annotated by experts and partially graded with a quality label, demonstrate the good performances of the proposed approach. The method is able to detect the optic disc and trace its contours better than the other systems presented in the literature and tested on the same data. The average error in the obtained contour masks is reasonably close to the interoperator errors and suitable for practical applications. The optic disc segmentation pipeline is currently integrated in a complete software suite for the semiautomatic quantification of retinal vessel properties from fundus camera images (VAMPIRE).

Multi-focus image fusion using epifluorescence microscopy for robust vascular segmentation

Automatic segmentation of three-dimensional mi-crovascular structures is needed for quantifying morphological changes to blood vessels during development, disease and treatment processes. Single focus two-dimensional epifluorescent imagery lead to unsatisfactory segmentations due to multiple out of focus vessel regions that have blurred edge structures and lack of detail. Additional segmentation challenges include varying contrast levels due to diffusivity of the lectin stain, leakage out of vessels and fine morphological vessel structure. We propose an approach for vessel segmentation that combines multi-focus image fusion with robust adaptive filtering. The robust adaptive filtering scheme handles noise without destroying small structures, while multi-focus image fusion considerably improves segmentation quality by deblurring out-of-focus regions through incorporating 3D structure information from multiple focus steps. Experiments using epifluorescence images of mice dura mater show an average of 30.4% improvement compared to single focus microvasculature segmentation.

Towards automatic detection of abnormal retinal capillaries in ultra-widefield-of-view retinal angiographic exams

Retinal capillary abnormalities include small, leaky, severely tortuous blood vessels that are associated with a variety of retinal pathologies. We present a prototype image-processing system for detecting abnormal retinal capillary regions in ultra-widefield-of-view (UWFOV) fluorescein angiography exams of the human retina. The algorithm takes as input an UWFOV FA frame and returns the candidate regions identified. An SVM classifier is trained on regions traced by expert ophthalmologists. Tests with a variety of feature sets indicate that edge features and allied properties differentiate best between normal and abnormal retinal capillary regions. Experiments with an initial set of images from patients showing branch retinal vein occlusion (BRVO) indicate promising area under the ROC curve of 0.950 and a weighted Cohen's Kappa value of 0.822.

Towards a multi-site international public dataset for the validation of retinal image analysis software

This paper discusses concisely the main issues and challenges posed by the validation of retinal image analysis algorithms. It is designed to set the discussion for the IEEE EBMC 2013 invited session "From laboratory to clinic: the validation of retinal image processing tools ". The session carries forward an international initiative started at EMBC 2011, Boston, which resulted in the first large-consensus paper (14 international sites) on the validation of retinal image processing software, appearing in IOVS. This paper is meant as a focus for the session discussion, but the ubiquity and importance of validation makes its contents, arguably, of interest for the wider medical image processing community.

Accurate estimation of retinal vessel width using bagged decision trees and an extended multiresolution Hermite model

We present an algorithm estimating the width of retinal vessels in fundus camera images. The algorithm uses a novel parametric surface model of the cross-sectional intensities of vessels, and ensembles of bagged decision trees to estimate the local width from the parameters of the best-fit surface. We report comparative tests with REVIEW, currently the public database of reference for retinal width estimation, containing 16 images with 193 annotated vessel segments and 5066 profile points annotated manually by three independent experts. Comparative tests are reported also with our own set of 378 vessel widths selected sparsely in 38 images from the Tayside Scotland diabetic retinopathy screening programme and annotated manually by two clinicians. We obtain considerably better accuracies compared to leading methods in REVIEW tests and in Tayside tests. An important advantage of our method is its stability (success rate, i.e., meaningful measurement returned, of 100% on all REVIEW data sets and on the Tayside data set) compared to a variety of methods from the literature. We also find that results depend crucially on testing data and conditions, and discuss criteria for selecting a training set yielding optimal accuracy.

Retinal vessel classification: sorting arteries and veins

For the discovery of biomarkers in the retinal vasculature it is essential to classify vessels into arteries and veins. We automatically classify retinal vessels as arteries or veins based on colour features using a Gaussian Mixture Model, an Expectation-Maximization (GMM-EM) unsupervised classifier, and a quadrant-pairwise approach. Classification is performed on illumination-corrected images. 406 vessels from 35 images were processed resulting in 92% correct classification (when unlabelled vessels are not taken into account) as compared to 87.6%, 90.08%, and 88.28% reported in [12] [14] and [15]. The classifier results were compared against two trained human graders to establish performance parameters to validate the success of classification method. The proposed system results in specificity of (0.8978, 0.9591) and precision (positive predicted value) of (0.9045, 0.9408) as compared to specificity of (0.8920, 0.7918) and precision of (0.8802, 0.8118) for (arteries, veins) respectively as reported in [13]. The classification accuracy was found to be 0.8719 and 0.8547 for veins and arteries, respectively.

In vivo imaging of human retinal microvasculature using adaptive optics scanning light ophthalmoscope fluorescein angiography

The adaptive optics scanning light ophthalmoscope (AOSLO) allows visualization of microscopic structures of the human retina in vivo. In this work, we demonstrate its application in combination with oral and intravenous (IV) fluorescein angiography (FA) to the in vivo visualization of the human retinal microvasculature. Ten healthy subjects ages 20 to 38 years were imaged using oral (7 and/or 20 mg/kg) and/or IV (500 mg) fluorescein. In agreement with current literature, there were no adverse effects among the patients receiving oral fluorescein while one patient receiving IV fluorescein experienced some nausea and heaving. We determined that all retinal capillary beds can be imaged using clinically accepted fluorescein dosages and safe light levels according to the ANSI Z136.1-2000 maximum permissible exposure. As expected, the 20 mg/kg oral dose showed higher image intensity for a longer period of time than did the 7 mg/kg oral and the 500 mg IV doses. The increased resolution of AOSLO FA, compared to conventional FA, offers great opportunity for studying physiological and pathological vascular processes.