A Study of Hypertensive Retinopathy Changes for Stroke Prediction

Hypertensive retinopathy is an ailment that is highly connected to hypertension which help in stroke prediction. The presence of hypertensive retinopathy is diagnosed through image processing on the fundus image to identify the possible microvascular retinal abnormalities signs that lead to hypertension. Arterio-Venous Ratio (AVR) value is one of the main indicator of hypertensive retinopathy, useful for grading severity of hypertensive retinopathy and for prediction of risk of stroke. Image preprocessing were performed in this work to extract vessels in fundus image. In this thesis, fundus images were acquired from VICAVR and DRIVE databases

Retinal Vascular Measurement Tools for Diagnostic Feature Extraction

The contributions of this work are in the development of new and state of the art algorithms for retinal image analysis including optic disc detection, tortuosity estimation, and cross-over abnormality detection. The retina is one of the only areas of the human body that blood vessels can be visualized noninvasively. Retinal imaging has become a standard in the ophthalmologist’s office because it is an easy and inexpensive way to monitor not just eye health, but also systemic vascular diseases. Changes to the retinal vasculature can be the early signs of diseases such as diabetic and hypertensive retinopathy, of which early detection can save vision, money, and improve overall health for the patient. When looking at the retinal vasculature, ophthalmologists generally rely on a qualitative assessment which can make comparisons over time or between different ophthalmologists difficult. Computer aided systems are now able to quantify what the ophthalmologist is qualitatively measuring in what they consider to be the most important features of the vasculature. These include, but are not limited to, tortuosity, arteriolar narrowing, cross-over abnormalities, and artery-vein (AV) ratio. The University of Padova has created a semi-automatic system for detecting and quantifying retinal vessels starting from optic disc detection, vessel segmentation, width estimation, tortuosity calculation, AV classification, and AV ratio. We propose a new method for optic disc detection that converts the retinal image into a graph and exploits vessel enhancement methods to calculate edge weights in finding the shortest path between pairs of points on the periphery of the image. The line segment with the maximum number of shortest paths is considered the optic disc location. The method was tested on three publicly available datasets: DRIVE, DIARETDB1, and Messidor consisting of 40, 89, and 1200 images and achieved an accuracy of 100, 98.88, and 99.42% respectively. The second contribution is a new algorithm for calculating abnormalities at AV crossing points. In retinal images, Gunn’s sign appears as a tapering of the vein at a crossing point, while Salus’s sign presents as an S-shaped curving. This work presents a method for the automatic quantification of these two signs once a crossover has been detected; combining segmentation, artery vein classification, and morphological feature extraction techniques to calculate vein widths and angles entering and exiting the crossover. Results on two datasets show separation between the two classes and that we can reliably detect and quantify these signs under the right conditions. The last contribution in tortuosity consists of two parts. A comparative study was performed on several of the most popular methods for tortuosity estimation on a new vessel dataset. Results show that several methods have good Cohen’s kappa agreement with both graders, while the tortuosity density metric has the highest single metric average agreement across vessel type and grader. The second is a new way to enhance curvature in segmented vessels based on a difference of Gabor filters to create a curvature enhanced image. The proposed method was tested on the RET-TORT database using several methods to calculate tortuosity, and had best Pearson’s correlation of .94 for arteries and .882 for veins, outperforming single mathematical formulations on the data. This held true after testing the method on the propose dataset as well, having higher correlation values across grader and vessel type compared with other tortuosity metrics. Summary of Results: The optic disc detection method was tested on three publicly available datasets: DRIVE, DIARETDB1, and Messidor consisting of 40, 89, and 1200 images and achieved an accuracy of 100, 98.88, and 99.42% respectively. The AV nicking quantification method was tested on a small dataset of 10 crossing provided by doctors at Papageorgiou Hospital, Aristotle University of Thessaloniki, Thessaloniki, Greece. Results showed separation between the normal and abnormal classes for both the Gunn and Salus sign. The method was then tested on a larger, publicly available dataset which showed good separation for the Gunn sign. The proposed tortuosity method was tested on the RET-TORT database using several methods to calculate tortuosity, and had best Pearson’s correlation of .94 for arteries and .882 for veins, outperforming single mathematical formulations on the data. It was then tested on the dataset proposed in this thesis, further corroborating the effectiveness of the method

Grading the Severity of Arteriolosclerosis from Retinal Arterio-venous Crossing Patterns

The status of retinal arteriovenous crossing is of great significance for clinical evaluation of arteriolosclerosis and systemic hypertension. As an ophthalmology diagnostic criteria, Scheie's classification has been used to grade the severity of arteriolosclerosis. In this paper, we propose a deep learning approach to support the diagnosis process, which, to the best of our knowledge, is one of the earliest attempts in medical imaging. The proposed pipeline is three-fold. First, we adopt segmentation and classification models to automatically obtain vessels in a retinal image with the corresponding artery/vein labels and find candidate arteriovenous crossing points. Second, we use a classification model to validate the true crossing point. At last, the grade of severity for the vessel crossings is classified. To better address the problem of label ambiguity and imbalanced label distribution, we propose a new model, named multi-diagnosis team network (MDTNet), in which the sub-models with different structures or different loss functions provide different decisions. MDTNet unifies these diverse theories to give the final decision with high accuracy. Our severity grading method was able to validate crossing points with precision and recall of 96.3% and 96.3%, respectively. Among correctly detected crossing points, the kappa value for the agreement between the grading by a retina specialist and the estimated score was 0.85, with an accuracy of 0.92. The numerical results demonstrate that our method can achieve a good performance in both arteriovenous crossing validation and severity grading tasks. By the proposed models, we could build a pipeline reproducing retina specialist's subjective grading without feature extractions. The code is available for reproducibility

Measurement of retinal vessel widths from fundus images based on 2-D modeling

Changes in retinal vessel diameter are an important sign of diseases such as hypertension, arteriosclerosis and diabetes mellitus. Obtaining precise measurements of vascular widths is a critical and demanding process in automated retinal image analysis as the typical vessel is only a few pixels wide. This paper presents an algorithm to measure the vessel diameter to subpixel accuracy. The diameter measurement is based on a two-dimensional difference of Gaussian model, which is optimized to fit a two-dimensional intensity vessel segment. The performance of the method is evaluated against Brinchmann-Hansen's half height, Gregson's rectangular profile and Zhou's Gaussian model. Results from 100 sample profiles show that the presented algorithm is over 30% more precise than the compared techniques and is accurate to a third of a pixel

Automated Systems for Calculating Arteriovenous Ratio in Retinographies : A Scoping Review

There is evidence of an association between hypertension and retinal arteriolar narrowing. Manual measurement of retinal vessels comes with additional variability, which can be eliminated using automated software. This scoping review aims to summarize research on automated retinal vessel analysis systems. Searches were performed on Medline, Scopus, and Cochrane to find studies examining automated systems for the diagnosis of retinal vascular alterations caused by hypertension using the following keywords: diagnosis; diagnostic screening programs; image processing, computer-assisted; artificial intelligence; electronic data processing; hypertensive retinopathy; hypertension; retinal vessels; arteriovenous ratio and retinal image analysis. The searches generated 433 articles. Of these, 25 articles published from 2010 to 2022 were included in the review. The retinographies analyzed were extracted from international databases and real scenarios. Automated systems to detect alterations in the retinal vasculature are being introduced into clinical practice for diagnosis in ophthalmology and other medical specialties due to the association of such changes with various diseases. These systems make the classification of hypertensive retinopathy and cardiovascular risk more reliable. They also make it possible for diagnosis to be performed in primary care, thus optimizing ophthalmological visits

Deep multi-instance heatmap regression for the detection of retinal vessel crossings and bifurcations in eye fundus images

©2020. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/. This version of the article: Hervella, Á. S., Rouco, J., Novo, J., Penedo, M. G., & Ortega, M. (2020). “Deep multi-instance heatmap regression for the detection of retinal vessel crossings and bifurcations in eye fundus images” has been accepted for publication in Computer Methods and Programs in Biomedicine, 186(105201), 105201. The Version of Record is available online at: https://doi.org/10.1016/j.cmpb.2019.105201.[Abstract]: Background and objectives:The analysis of the retinal vasculature plays an important role in the diagnosis of many ocular and systemic diseases. In this context, the accurate detection of the vessel crossings and bifurcations is an important requirement for the automated extraction of relevant biomarkers. In that regard, we propose a novel approach that addresses the simultaneous detection of vessel crossings and bifurcations in eye fundus images. Method: We propose to formulate the detection of vessel crossings and bifurcations in eye fundus images as a multi-instance heatmap regression. In particular, a deep neural network is trained in the prediction of multi-instance heatmaps that model the likelihood of a pixel being a landmark location. This novel approach allows to make predictions using full images and integrates into a single step the detection and distinction of the vascular landmarks. Results: The proposed method is validated on two public datasets of reference that include detailed annotations for vessel crossings and bifurcations in eye fundus images. The conducted experiments evidence that the proposed method offers a satisfactory performance. In particular, the proposed method achieves 74.23% and 70.90% F-score for the detection of crossings and bifurcations, respectively, in color fundus images. Furthermore, the proposed method outperforms previous works by a significant margin. Conclusions: The proposed multi-instance heatmap regression allows to successfully exploit the potential of modern deep learning algorithms for the simultaneous detection of retinal vessel crossings and bifurcations. Consequently, this results in a significant improvement over previous methods, which will further facilitate the automated analysis of the retinal vasculature in many pathological conditions.This work is supported by Instituto de Salud Carlos III, Government of Spain, and the European Regional Development Fund (ERDF) of the European Union (EU) through the DTS18/00136 research project, and by Ministerio de Ciencia, Innovación y Universidades, Government of Spain, through the DPI2015-69948-R and RTI2018-095894-B-I00 research projects. The authors of this work also receive financial support from the ERDF and European Social Fund (ESF) of the EU, and Xunta de Galicia through Centro Singular de Investigación de Galicia, accreditation 2016–2019, ref. ED431G/01, Grupo de Referencia Competitiva, ref. ED431C 2016-047, and the predoctoral grant contract ref. ED481A-2017/328.Xunta de Galicia; ED431G/01Xunta de Galicia; ED431C 2016-047Xunta de Galicia; ED481A-2017/32

Novel methods in retinal vessel calibre feature extraction for systemic disease assessment

Retina and its vascular network have unique branching characteristics morphology of which will change as a result of some systemic diseases, including hypertension, stroke and diabetes. Therefore, retinal image has been used as non-invasive screening window for risk assessment and prediction of such disease condition especially at the baseline. The assessment is based on a number of features among which vessel diameter (both individual and summary) and fractal dimension (FD) are the ones mostly associated with risk of diabetes and stroke. The association is linked to the higher risk of diabetes and stroke in people with narrower retinal arteriole diameter or change in overall fractal dimension independent of any risk factor (i.e. blood pressure, cardiovascular risk factors). Diameter measurement requires vessel edges to be located and tracked however; accurate edge perception is subject to image contrast, shadows, lighting condition and even presence of retinopathy legions close to vessel boundaries. This will lead to imprecision and inconsistencies between different automatic measurement techniques and may affect the significance of its association with disease condition in risk-assessment studies. As accuracy and success of diameter measurement is subject to large variations due to image artifacts it may not be suitable for fully automatic applications. In order to compensate for such error, at first two novel automatic vessel diameter measurement techniques were proposed and validated which were more robust in the presence of such image artifacts compared to similar methods. However, sometimes the exact edge location and actual diameter value is not of interest. In most case-control studies, it is of importance to comparatively evaluate the variations in retinal vessel diameter as a sign of retinopathy such as arteriolar nicking as an example of hypertensive retinopathy. Vessel diameter is often required to be compared with a reference value in many analytical assessments for diagnostic purpose. This includes monitoring the diameter variations of a specific vessel segment within single subject overtime or across multiple subjects. This helps ophthalmologists to understand whether it has undergone any significant change and perhaps associate it with a disease abnormality. A technique that can effectively quantify that change without being impaired by image artifacts is of more importance and one of the rationales of this study. This research hypothesized an edge independent solution for quantifying diameter variations when the actual diameter value is not required and proposed a new feature based on fractal analysis of vessel cross-section profile as a time series signal. This feature provides a link between FD as a global measure of the complexity and diameter variation as local property of a specific vessel segment. The clinical application of this feature has been validated on two population studies which showed promising result for assessment of mild non-proliferative diabetic retinopathy and 10-year stroke. This research work has also investigated whether the FD of retinal microvasculature would be affected by cyclic pulsations of retinal vessels and whether ECG synchronization is required prior to taking fundus images to compensate for this potential source of variations

Automatic computation of the arteriovenous ratio and assessment of its effectiveness as a prognostic indicator in hypertension

[Resumen] La retina es la única parte del cuerpo humano en donde se pueden observar los vasos sanguíneos directamente de una forma no invasiva mediante un examen de fondo de ojo. De esta manera, la imagen de la retina mediante las técnicas de procesamiento de imágenes se convirtió en un campo de clave para el diagnóstico precoz de varias enfermedades sistémicas que provocan alteraciones visibles en dicha imagen. Así, alteraciones en el ancho de los vasos retinianos se asocian con patologías tales como diabetes o hipertensión. De hecho, el estrechamiento de las arterias constituye un indicio precoz de la hipertensión arterial sistémica, siendo una característica del grado I de la retinopatía hipertensiva de acuerdo con la clasificación de Keith-Wagener-Barker. En este sentido, se han realizado esfuerzos para desarrollar programas asistidos por ordenador para medir con precisión los cambios en el ancho de los vasos a través del índice arteriovenoso (IAV), es decir, la relación entre los calibres de las arterias y las venas. Sin embargo, aunque estos sistemas se han usado en muchos estudios con fines de investigación, su aplicabilidad en la práctica clínica diaria es todavía discutida. En este trabajo, se propone una nueva metodología para el cálculo del IAV con el fin de estratificar el riesgo cardiovascular de los hipertensos. Por un lado, se ha desarrollado un método completamente automático para estimar el IAV en una imagen de fondo de ojo de un paciente. Por otro lado, se propone un sistema para monitorizar el IAV del paciente a lo largo del tiempo. Para este fin, las mediciones del IAV en las diferentes imágenes adquiridas sobre el mismo ojo del paciente en diferentes fechas se estiman usando el mismo conjunto de vasos medidos en las mismas áreas. Por lo tanto, la mediciones obtenidos de esta manera son comparables y precisas, debido a que son independientes en el conjunto de vasos seleccionados para el cálculo. Las dos técnicas se han integrado en SIRIUS, un sistema web destinado a incluir diferentes servicios en el campo del análisis de la imagen retiniana. El sistema incluye también gestión de pacientes y revisiones, lo que facilita el análisis de las lesiones retinianas causadas por diferentes patologías y su evolución después de un determinado tratamiento. Además al ser una aplicación distribuída a través de la web, proporciona un entorno de colaboración entre diferentes médicos, investigadores y centros.[Resumo] A retina é a única parte do corpo humano onde se poden observar os vasos sanguíneos directamente dunha maneira non invasiva mediante un examen do fondo do ollo. Desta maneira, a imaxe da retina mediante as técnicas de procesamento de imáxenes converteuse nun campo chave para o diagnóstico precoz de varias enfermidades sistémicas que provocan alteracións visibles en dita imaxe. Así, cambios no ancho dos vasos retinianos asócianse con patoloxías tales como a diabetes ou a hipertensión. De feito, o estreitamento das arterias constitúe un indicio prematuro da hipertensión arterial sistémica, sendo unha característica do grado I da retinopatía hipertensiva dacordo coa clasificación de Keith- Wagener-Barker. Neste sentido, fixerónse moitos esforzos para desenvolver programas asistidos por ordenador para medir con precisión os cambios no ancho dos vasos a través do índice arteriovenoso (IAV), é dicir, a relación entre os calibres das arterias e das veas. Nembargantes, aínda que estes sistemas foron usados en moitos estudios con fins investigadores, a sua aplicabilidade na práctica clínica diaria aínda é discutida. Neste traballo, proponse unha nova metodoloxía para o cálculo do IAV co fin de estratificar o risco cardiovascular dos hipertensos. Por un lado, desenvolveuse un método completamente automático para estimar o IAV nunha imaxe de fondo de ollo dun doente. Por outra banda, proponse un sistema para monitorizar o IAV dun doente a lo longo do tempo. Para isto, as medicións do IAV nas diferentes imaxes adquiridas sobre o mesmo ollo do doente en diferentes datas fanse usando o mesmo conxunto de vasos medidos nas mesmas áreas. Polo tanto, as medicións obtidas desta maneira son comparables e precisas, debido a que son independentes do conxunto de vasos seleccionados para o cálculo. As dúas técnicas foron integradas no SIRIUS, un sistema web destinado a incluir diferentes servicios no campo da análise da imaxe retiniana. O sistema inclúe tamén xestión de doentes e revisións, facilitando a análise e estudo das lesións retinianas causadas por diferentes patoloxías e a súa evolución despois dun determinado tratamento. Ademais ao ser unha aplicación distribuída a través da web, proporciona un entorno de colaboración entre diferentes médicos, investigadores e centros.[Abstract] Retina is the only part in the human body where blood vessels can be directly observed in a non-invasive way through an eye fundus examination. In this manner, the retinal imaging assisted by image processing techniques became a key field for the early diagnosis of several systemic diseases which cause visible alterations in the fundus image. Thus, changes in the retinal vessel widths are associated with pathologies such as diabetes or hypertension. In fact, arteriolar narrowing constitutes an early sign of systemic hypertension, being a feature for the grade I of hypertension retinopathy according to Keith-Wagener-Barker classification. In this sense, some efforts have been made to develop computer-assisted programs to measure accurately abnormalities in the vessel widths through the arteriovenous ratio (AVR), that is, the relation between arteriolar and venular vessel widths. However, although these systems have been used in many studies for research purposes, their applicability to daily clinical practice is yet discussed. In this work, a new methodology for the AVR computation is proposed in order to stratify the cardiovascular risk of hypertension. On one hand, a fully automatic method to estimate the AVR in a sample patient's image is developed. On the other hand, an AVR monitoring system to compute the patient's AVR over time was implemented. To this end, the AVR measurements computed in the different patient's images acquired from the same eye at different dates, uses the same set of vessels measured at the same areas. Thus, the measurements achieved in this manner are comparable and precise due to they are independent on the set of vessels selected for the calculus. The two approaches have been integrated in SIRIUS, a web-based system aimed to include different services in the field of retinal image analysis. It includes patient and checkup management, making easier to analyze the retinal lesions caused by different pathologies and their evolution after a specific treatment. Moreover, being a application distributed via the web, it provides a collaborative environment among different physicians, researchers and medical centers

RETINAL IMAGING IN CEREBROVASCULAR DISEASE AND DEMENTIA

Ph.DDOCTOR OF PHILOSOPH

Quantitative retinal traits and their association with cardiovascular disease and cardio-metabolic genetic variants in people with type 2 diabetes

Introduction: Type 2 diabetes (T2D) is one of the most prevalent noncommunicable diseases in the world and its cardiovascular complications present a huge socio-economic burden. In 2015, in the UK alone, 3.8 million people have been diagnosed with T2D and cardiovascular disease accounts for almost 1.7 million episodes throughout the country. Early diagnosis of cardiovascular disease in people with T2D thus becomes critical. The retina gives a unique opportunity to study the human microcirculation, which can then offer insights into the pathophysiology of cardiovascular disease. By using semi-automatic software, retinal images can provide quantitative traits derived from the microvasculature. Previous research has found that arteriolar and venular calibres are associated with cardiovascular outcomes such as hypertension and stroke. Moreover, retinal vascular tortuosity, a novel quantitative biomarker which measures the degree to which blood vessels visible in the retina twist and turn, has been associated with traditional cardiovascular risk factors in the general population. However, this area needs to be further explored, especially in the population with T2D and in prospective analyses. Aims: To determine whether quantitative retinal traits such as vessel widths, vessel tortuosity and multifractal dimensions are associated with the subsequent development of major cardiovascular events such as ischaemic heart disease and stroke in people with T2D. Also, to use a genome-wide association approach to investigate if these quantitative retinal traits are associated with cardio-metabolic genetic variants, which could help identify novel biomarkers of cardiovascular disease for future research. Methods: Analyses used the Edinburgh Type 2 Diabetes Study, a prospective cohort of 1066 men and women with T2D aged 60-75 years at baseline with eight years of follow-up for cardiovascular events. A total of 1028 retinal images from baseline were analysed using the semi-automatic retinal software VAMPIRE (Vascular Assessment and Measurement Platform for Images of the Retina). Cross-sectional analyses including ANOVA and Chi-square test were performed along with prospective analysis using Cox regression. Additionally, a genome-wide association study was performed to explore the association of 12 quantitative retinal traits with cardio-metabolic genetic variants. Imputation of variants included in the MetaboChip array was used. Results: In an unadjusted model, there was a significant association between arteriolar tortuosity and incident stroke (Hazard Ratio (HR) 1.26; 95% CI 1.02, 1.57; p=0.03). This association remained significant after full adjustment for age, sex, cardiovascular risk factors (body mass index, HbA1c, total cholesterol, duration of diabetes, renal dysfunction) and previous cardiovascular events (HR 1.26; 95% CI 1.01, 1.58; p=0.04). Multifractal dimensions, a novel retinal biomarker which provides an insight into vascular geometry, was inversely associated with incident stroke (unadjusted HR 0.73; 95% CI 0.57, 0.94; p=0.01). This association also remained significant after adjustment for age, sex, cardiovascular risk factors and previous cardiovascular event (HR 0.73; 95% CI 0.56, 0.94 p=0.02). Associations between other retinal traits and stroke, and between traits and ischaemic heart disease, tended not to be statistically significant, especially after multivariable adjustment. The genome-wide association analysis of arteriovenous ratio (ratio of arteriolar to venular vessel width) revealed a genome wide significant locus, rs73198094 (p = 5.27 x 10-8), an intergenic variant located between ASAH1 and LOC101929066 genes in chromosome 8. The ASAH1 gene has been associated with atrial fibrillation. Although no further single nucleotide polymorphisms reached genome-wide significance, some additional promising findings emerged. Analysis for retinal arteriolar width revealed a genome-wide suggestive intronic locus, rs4944903 (p= 8.5x10-7), of the gene POLD3 in chromosome 11. Identified loci for minimum arteriolar tortuosity, rs7991332 (p=1.54 x 10-6) and rs2172724 (p=2.46 x10-6), are located in the COL4A2 gene in chromosome 13 and another identified variant, rs7319323 (p=3.53 x 10-6), is located in an intron of the neighbouring COL4A1 gene. Previous studies showed the relevance of these genes including an association with stroke and intracerebral haemorrhage. These two genes encode collagen protein chains, which are major components of the vascular basement membrane. Another promising variant identified was the rs34013641 locus, associated with minimum venular tortuosity (p=2.81 x 10- 6), which is located in the MYH11 gene in chromosome 2. This gene encodes smooth muscle myosin heavy chain protein, which is highly expressed in human arteries. Finally, identified loci for the multifractal dimension D0, rs10963694 (p=8.53 x 10-7) and rs4977506 (p=4.95 x 10-6), located on the ADAMTSL-1 gene in chromosome 9, are also strong candidates in the pathophysiology of vascular disorders. Conclusions: In older people with T2D, arteriolar tortuosity and multifractal dimensions were significantly and independently associated with incident stroke. GWAS findings for these and other quantitative retinal traits offer insight into pathophysiological changes of the vasculature, which may result in cardiovascular disease. These findings, in the context of further research, could potentially be used to reveal biological mechanisms related to major cardiovascular complications of T2D and to guide efforts on prevention and early interventions