Automated retinal analysis

Diabetes is a chronic disease affecting over 2% of the population in the UK [1]. Long-term complications of diabetes can affect many different systems of the body including the retina of the eye. In the retina, diabetes can lead to a disease called diabetic retinopathy, one of the leading causes of blindness in the working population of industrialised countries. The risk of visual loss from diabetic retinopathy can be reduced if treatment is given at the onset of sight-threatening retinopathy. To detect early indicators of the disease, the UK National Screening Committee have recommended that diabetic patients should receive annual screening by digital colour fundal photography [2]. Manually grading retinal images is a subjective and costly process requiring highly skilled staff. This thesis describes an automated diagnostic system based oil image processing and neural network techniques, which analyses digital fundus images so that early signs of sight threatening retinopathy can be identified. Within retinal analysis this research has concentrated on the development of four algorithms: optic nerve head segmentation, lesion segmentation, image quality assessment and vessel width measurements. This research amalgamated these four algorithms with two existing techniques to form an integrated diagnostic system. The diagnostic system when used as a 'pre-filtering' tool successfully reduced the number of images requiring human grading by 74.3%: this was achieved by identifying and excluding images without sight threatening maculopathy from manual screening

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

Deep Learning Techniques for Automated Analysis and Processing of High Resolution Medical Imaging

Programa Oficial de Doutoramento en Computación . 5009V01[Abstract] Medical imaging plays a prominent role in modern clinical practice for numerous medical specialties. For instance, in ophthalmology, different imaging techniques are commonly used to visualize and study the eye fundus. In this context, automated image analysis methods are key towards facilitating the early diagnosis and adequate treatment of several diseases. Nowadays, deep learning algorithms have already demonstrated a remarkable performance for different image analysis tasks. However, these approaches typically require large amounts of annotated data for the training of deep neural networks. This complicates the adoption of deep learning approaches, especially in areas where large scale annotated datasets are harder to obtain, such as in medical imaging. This thesis aims to explore novel approaches for the automated analysis of medical images, particularly in ophthalmology. In this regard, the main focus is on the development of novel deep learning-based approaches that do not require large amounts of annotated training data and can be applied to high resolution images. For that purpose, we have presented a novel paradigm that allows to take advantage of unlabeled complementary image modalities for the training of deep neural networks. Additionally, we have also developed novel approaches for the detailed analysis of eye fundus images. In that regard, this thesis explores the analysis of relevant retinal structures as well as the diagnosis of different retinal diseases. In general, the developed algorithms provide satisfactory results for the analysis of the eye fundus, even when limited annotated training data is available.[Resumen] Las técnicas de imagen tienen un papel destacado en la práctica clínica moderna de numerosas especialidades médicas. Por ejemplo, en oftalmología es común el uso de diferentes técnicas de imagen para visualizar y estudiar el fondo de ojo. En este contexto, los métodos automáticos de análisis de imagen son clave para facilitar el diagnóstico precoz y el tratamiento adecuado de diversas enfermedades. En la actualidad, los algoritmos de aprendizaje profundo ya han demostrado un notable rendimiento en diferentes tareas de análisis de imagen. Sin embargo, estos métodos suelen necesitar grandes cantidades de datos etiquetados para el entrenamiento de las redes neuronales profundas. Esto complica la adopción de los métodos de aprendizaje profundo, especialmente en áreas donde los conjuntos masivos de datos etiquetados son más difíciles de obtener, como es el caso de la imagen médica. Esta tesis tiene como objetivo explorar nuevos métodos para el análisis automático de imagen médica, concretamente en oftalmología. En este sentido, el foco principal es el desarrollo de nuevos métodos basados en aprendizaje profundo que no requieran grandes cantidades de datos etiquetados para el entrenamiento y puedan aplicarse a imágenes de alta resolución. Para ello, hemos presentado un nuevo paradigma que permite aprovechar modalidades de imagen complementarias no etiquetadas para el entrenamiento de redes neuronales profundas. Además, también hemos desarrollado nuevos métodos para el análisis en detalle de las imágenes del fondo de ojo. En este sentido, esta tesis explora el análisis de estructuras retinianas relevantes, así como el diagnóstico de diferentes enfermedades de la retina. En general, los algoritmos desarrollados proporcionan resultados satisfactorios para el análisis de las imágenes de fondo de ojo, incluso cuando la disponibilidad de datos de entrenamiento etiquetados es limitada.[Resumo] As técnicas de imaxe teñen un papel destacado na práctica clínica moderna de numerosas especialidades médicas. Por exemplo, en oftalmoloxía é común o uso de diferentes técnicas de imaxe para visualizar e estudar o fondo de ollo. Neste contexto, os métodos automáticos de análises de imaxe son clave para facilitar o diagn ostico precoz e o tratamento adecuado de diversas enfermidades. Na actualidade, os algoritmos de aprendizaxe profunda xa demostraron un notable rendemento en diferentes tarefas de análises de imaxe. Con todo, estes métodos adoitan necesitar grandes cantidades de datos etiquetos para o adestramento das redes neuronais profundas. Isto complica a adopción dos métodos de aprendizaxe profunda, especialmente en áreas onde os conxuntos masivos de datos etiquetados son máis difíciles de obter, como é o caso da imaxe médica. Esta tese ten como obxectivo explorar novos métodos para a análise automática de imaxe médica, concretamente en oftalmoloxía. Neste sentido, o foco principal é o desenvolvemento de novos métodos baseados en aprendizaxe profunda que non requiran grandes cantidades de datos etiquetados para o adestramento e poidan aplicarse a imaxes de alta resolución. Para iso, presentamos un novo paradigma que permite aproveitar modalidades de imaxe complementarias non etiquetadas para o adestramento de redes neuronais profundas. Ademais, tamén desenvolvemos novos métodos para a análise en detalle das imaxes do fondo de ollo. Neste sentido, esta tese explora a análise de estruturas retinianas relevantes, así como o diagnóstico de diferentes enfermidades da retina. En xeral, os algoritmos desenvolvidos proporcionan resultados satisfactorios para a análise das imaxes de fondo de ollo, mesmo cando a dispoñibilidade de datos de adestramento etiquetados é limitada

The Genotype-Phenotype Correlation of the key features of Non-Proliferative Diabetic Retinopathy

Diabetic Retinopathy (DR) is a leading cause of visual impairment but its pathophysiology is not well understood. Moderate/severe non-proliferative DR (NPDR) is characterised by the presence of three features: deep haemorrhages (DH), venous beading (VB) and intraretinal microvascular abnormalities (IRMA). They are grouped together as risk factors for progression to sight threatening DR. It remains unclear whether these individual features have similar pathophysiologies, and whether they respond equally to anti-VEGF, a new therapy for NPDR. Optomap images of 504 NPDR eyes were examined to evaluate the distribution and prevalence of these three features. DNA samples from 199 patients with NPDR and 397 diabetic patients with no DR were collected. The genotype of specific candidate genes were evaluated in patients with DR, VB or IRMA vs no DR. Optical coherence tomography angiography (OCTA) images of 30 patients were examined for focal ischemia adjacent to VB and IRMA. The responses of these three features to anti-VEGF treatment were also re-examined in the images from the CLARITY trial. DH were present in most cases of NPDR. VB and IRMA did not always co-exist in the same eye and when they do, were often in different locations. VEGF, TGFb-1 and ARHGAP22 polymorphisms (ischaemia-related genes) were more common in patients with DR and IRMA, but not VB. Areas of focal ischaemia were more frequently adjacent to IRMA than to VB. DH and IRMA responded to anti-VEGF therapy but VB did not. These findings suggest that VB and IRMA do not share the same pathophysiology, and that IRMA are more likely to be ischaemic driven. Nonetheless, some IRMA may not be driven by ischaemia as they have no adjacent ischaemia on OCTA, do not carry the specific genotype, and do not respond to anti-VEGF. Furthermore, patients with VB may not benefit from anti-VEGF therapy

BLOOD VESSELS SEGMENTATION METHOD FOR RETINAL FUNDUS IMAGES BASED ON ADAPTIVE PRINCIPAL CURVATURE AND IMAGE DERIVATIVE OPERATORS

Diabetes is a common disease in the modern life. According to WHO’s data, in 2018, there were 8.3% of adult population had diabetes. Many countries over the world have spent a lot of finance, force to treat this disease. One of the most dangerous complications that diabetes can cause is the blood vessel lesion. It can happen on organs, limbs, eyes, etc. In this paper, we propose an adaptive principal curvature and three blood vessels segmentation methods for retinal fundus images based on the adaptive principal curvature and images derivatives: the central difference, the Sobel operator and the Prewitt operator. These methods are useful to assess the lesion level of blood vessels of eyes to let doctors specify the suitable treatment regimen. It also can be extended to apply for the blood vessels segmentation of other organs, other parts of a human body. In experiments, we handle proposed methods and compare their segmentation results based on a dataset – DRIVE. Segmentation quality assessments are computed on the Sorensen-Dice similarity, the Jaccard similarity and the contour matching score with the given ground truth that were segmented manually by a human

Modélisation statistique des structures anatomiques de la rétine à partir d'images de fond d'oeil

L’examen non-invasif du fond d’oeil permet d’identifier sur la rétine les signes de nombreuses pathologies oculaires qui développent de graves symptômes pour le patient pouvant entraîner la cécité. Le réseau vasculaire rétinien peut de surcroît présenter des signes précurseurs de pathologies cardiovasculaires et cérébro-vasculaires. La rétine, où apparaissent ces pathologies, est constituée de plusieurs structures anatomiques dont la variabilité est importante au sein d’une population saine. Pour autant, les évaluations cliniques actuelles ne prennent pas en compte cette variabilité ce qui ne permet pas de détecter précocement ces pathologies. Ces évaluations se basent sur un ensemble restreint de mesures prélevées à partir de structures dont la segmentation manuelle est réalisable par les experts. De plus, elles sont basées sur un seuillage empirique déterminé par les cliniciens et appliqué sur chacune des mesures afin d’établir un diagnostic. Ainsi, les évaluations cliniques actuelles sont affectées par la grande variabilité des structures anatomiques de la rétine au sein de la population et elles n’évaluent pas les anomalies trop difficiles à mesurer manuellement. Dans ce contexte, il convient de proposer de nouvelles mesures cliniques qui tiennent compte de la variabilité normale à l’aide d’une modélisation statistique des structures anatomiques de la rétine. Cette modélisation statistique permet de mieux comprendre et identifier ce qui est normal et comment l’anatomie et ses attributs varient au sein d’une population saine. Cela permet ainsi d’identifier la présence de pathologies à l’aide de nouvelles mesures cliniques construites en tenant compte de la variabilité des attributs de l’anatomie. La modélisation statistique des structures anatomiques de la rétine est cependant difficile étant donné les variations morphologiques et topologiques de ces structures. Les changements morphologiques et topologiques du réseau vasculaire rétinien compliquent son analyse statistique ainsi que les outils de recalage, de segmentation et de représentation sémantique s’y appliquant. Les questions de recherches adressées dans cette thèse sont la production d’outils capables d’analyser la variabilité des structures anatomiques de la rétine et l’élaboration de nouvelles mesures cliniques tenant compte de la variabilité normale de ces structures. Pour répondre à ces questions de recherche, trois objectifs de recherche sont formulés. ----------ABSTRACT: Non-invasive retinal fundus examination allows clinicians to identify signs of many ocular conditions that develop critical symptoms affecting the patient and even leading to blindness. In addition, the retinal vascular network may present early signs of cardiovascular and cerebrovascular diseases. The retina, where these pathologies appear, is composed of several anatomical structures whose variability is considerable within a healthy population. Yet, current clinical evaluations do not take into account this variability, and this does not allow early detection of these pathologies. These evaluations are based on a limited set of measurements taken from structures whose manual segmentation is achievable by the experts. In addition, they are based on empirical thresholding determined by the clinicians and applied to each of the measurements to establish a diagnosis. Thus, current clinical assessments are affected by the large variability of anatomical structures of the retina within a healthy population and do not evaluate abnormalities that are too difficult to measure manually. In this context, it is advisable to propose new clinical measurements that take into account the normal variability using statistical modeling of the anatomical structures of the retina. Such a statistical modeling approach helps us to better understand and identify what is normal and how the anatomy and its attributes vary across a healthy population. This makes it possible to identify the presence of pathologies using new clinical measurements constructed by taking into account the variability of the anatomy’s attributes. Statistical modeling of the anatomical structures of the retina is difficult, however, given the morphological and topological variations of these structures. Morphological and topological changes in the retinal vascular network complicate its statistical analysis as well as the registration methods, segmentation and semantic representation applied to it. The research questions proposed in this thesis pertain to creating tools capable of analyzing the variability of the anatomical structures of the retina and proposing new clinical measures that take into account the normal variability of those structures. To answer these research questions, three research objectives are formulated

Advances in Ophthalmology

This book focuses on the different aspects of ophthalmology - the medical science of diagnosis and treatment of eye disorders. Ophthalmology is divided into various clinical subspecialties, such as cornea, cataract, glaucoma, uveitis, retina, neuro-ophthalmology, pediatric ophthalmology, oncology, pathology, and oculoplastics. This book incorporates new developments as well as future perspectives in ophthalmology and is a balanced product between covering a wide range of diseases and expedited publication. It is intended to be the appetizer for other books to follow. Ophthalmologists, researchers, specialists, trainees, and general practitioners with an interest in ophthalmology will find this book interesting and useful

Retinal Diseases: multifocal pupillographic objective perimetry, and epidemiology

My thesis entitled Retinal diseases: multifocal pupillographic objective perimtery, and epidemiology comprises two parts. First, retinal function measured with multifocal pupillographic objective perimetry (mfPOP) in neovascular AMD (nAMD) and diabetic macular oedema (DMO), and second, epidemiology of vitreoretinal diseases (VRD) in Bhutan and Nepal. The epidemiological chapters cover 1905 new VRD patients in Nepal, and 2913 in Bhutan. Those studies identified the common, preventable VRDs including: diabetic retinopathy and macular oedema, nAMD, high myopia, hypertensive retinopathy, retinal vein occlusion, retinal breaks, retinal vasculitis, retinopathy of prematurity, etc. In Nepal, females presented later than males by 9 years (p<0.0001), age or disease duration for diabetic retinopathy did not correlate with severity, presenting visual acuity (VA) was asymmetric between eyes (p<0.0001), and patients often only reported once their right (dominant) eyes had VA of 6/18 or worse. In Bhutan, females presented earlier: (p=0.003). Myopia constituted 92.1% of refractive error with myopia prevalence (MP) of 12.3%, more common among females (p=0.01) and urbanites (p=0.02). MP was highest among urban females (20.9%), followed by urban males (11.9%), rural females (6.8%), and rural males (5.2%). Logistic regression revealed that the odds of having myopia were increased by being a student (4.96 x) or professional (1.96 x), and decreased by rural living (1.75 x), all p =/< 0.038. Those diseases are treatable with complete recovery if managed early. To improve management, we address the knowledge gap in developing countries by advancing a reliable objective diagnostic method: an FDA-cleared prototype of mfPOP, the ObjectiveFIELD Analyser (OFA) (Konan Medical USA, Irvine, CA). In nAMD, the per-region central OFA sensitivities decreased and delays increased with disease severity in patients tested monthly for up to 2 years. Central sensitivity of eyes needing anti-VEGF treatment was less sensitive than in the untreated eyes by -2.23+/-0.051 dB (p<0.0002). Based on the peripheral responses we have noticed 2 types of patients: positive with peripheral hypersensitivity and longer delays than normal, and negative with peripheral hyposensitivity and shorter delays. Among the positive eyes the peripheral sensitivity increased on average by 9.88+/-4.41 dB/month (p=0.042), and the delay increased at 3.49+/-1.75 ms/month (p=0.049) before the clinical decision to treat. For the negative eyes, the peripheral sensitivity dropped, and the delay was shorter a month before the treatment by 9.38+/-3.59 (p=0.013), and a month following treatment it improved (shorter by 8.50+/-2.71 ms, p=0.004). Treatment drove subsequent responses towards normality either from the positive or negative patient condition. Importantly, changes occurred 1-3 months before treatment was indicated clinically or by other diagnostic methods. We followed Type 2 diabetes patients for up to 2 years. Reduced mean sensitivities and longer delays were seen compared to normal. Peripheral hypersensitivity and shorter delays were associated with shorter diabetes duration. Principal curve analysis showed correlation between OFA and Matrix values. Outer macular thickness correlated significantly with inner and outer OFA sensitivity and delay, all p<0.0012 with DMO, and median p=0.001 without DMO, while the inner thickness was not correlated. A mixed-effects logistic regression determined that outer thickness and OFA sensitivity contributed independently to a clinical diagnosis of DMO. Outer thickness, number of visits and male gender increased the odds of having DMO. From a practical standpoint dilation with tropicamide 1% affected per-region sensitivities for more hours than either delays or signal-to-noise ratios (SNRs). So sensitivity measurements should be considered only after 48 hours, but delays or SNRs can be measured after 8-12 hours following mydriasis with tropicamide

Diabetic Retinopathy Classification and Interpretation using Deep Learning Techniques

La retinopatia diabètica és una malaltia crònica i una de les principals causes de ceguesa i discapacitat visual en els pacients diabètics. L'examen ocular a través d'imatges de la retina és utilitzat pels metges per detectar les lesions relacionades amb aquesta malaltia. En aquesta tesi, explorem diferents mètodes innovadors per a la classificació automàtica del grau de malaltia utilitzant imatges del fons d'ull. Per a aquest propòsit, explorem mètodes basats en l'extracció i classificació automàtica, basades en xarxes neuronals profundes. A més, dissenyem un nou mètode per a la interpretació dels resultats. El model està concebut de manera modular per a que pugui ser utilitzat en d'altres xarxes i dominis de classificació. Demostrem experimentalment que el nostre model d'interpretació és capaç de detectar lesions de retina a la imatge únicament a partir de la informació de classificació. A més, proposem un mètode per comprimir la representació interna de la informació de la xarxa. El mètode es basa en una anàlisi de components independents sobre la informació del vector d'atributs intern de la xarxa generat pel model per a cada imatge. Usant el nostre mètode d'interpretació esmentat anteriorment també és possible visualitzar aquests components en la imatge. Finalment, presentem una aplicació experimental del nostre millor model per classificar imatges de retina d'una població diferent, concretament de l'Hospital de Reus. Els mètodes proposats arriben al nivell de rendiment de l'oftalmòleg i són capaços d'identificar amb gran detall les lesions presents en les imatges, que es dedueixen només de la informació de classificació de la imatge.La retinopatía diabética es una enfermedad crónica y una de las principales causas de ceguera y discapacidad visual en los pacientes diabéticos. El examen ocular a través de imágenes de la retina es utilizado por los médicos para detectar las lesiones relacionadas con esta enfermedad. En esta tesis, exploramos diferentes métodos novedosos para la clasificación automática del grado de enfermedad utilizando imágenes del fondo de la retina. Para este propósito, exploramos métodos basados en la extracción y clasificación automática, basadas en redes neuronales profundas. Además, diseñamos un nuevo método para la interpretación de los resultados. El modelo está concebido de manera modular para que pueda ser utilizado utilizando otras redes y dominios de clasificación. Demostramos experimentalmente que nuestro modelo de interpretación es capaz de detectar lesiones de retina en la imagen únicamente a partir de la información de clasificación. Además, proponemos un método para comprimir la representación interna de la información de la red. El método se basa en un análisis de componentes independientes sobre la información del vector de atributos interno de la red generado por el modelo para cada imagen. Usando nuestro método de interpretación mencionado anteriormente también es posible visualizar dichos componentes en la imagen. Finalmente, presentamos una aplicación experimental de nuestro mejor modelo para clasificar imágenes de retina de una población diferente, concretamente del Hospital de Reus. Los métodos propuestos alcanzan el nivel de rendimiento del oftalmólogo y son capaces de identificar con gran detalle las lesiones presentes en las imágenes, que se deducen solo de la información de clasificación de la imagen.Diabetic Retinopathy is a chronic disease and one of the main causes of blindness and visual impairment for diabetic patients. Eye screening through retinal images is used by physicians to detect the lesions related with this disease. In this thesis, we explore different novel methods for the automatic diabetic retinopathy disease grade classification using retina fundus images. For this purpose, we explore methods based in automatic feature extraction and classification, based on deep neural networks. Furthermore, as results reported by these models are difficult to interpret, we design a new method for results interpretation. The model is designed in a modular manner in order to generalize its possible application to other networks and classification domains. We experimentally demonstrate that our interpretation model is able to detect retina lesions in the image solely from the classification information. Additionally, we propose a method for compressing model feature-space information. The method is based on a independent component analysis over the disentangled feature space information generated by the model for each image and serves also for identifying the mathematically independent elements causing the disease. Using our previously mentioned interpretation method is also possible to visualize such components on the image. Finally, we present an experimental application of our best model for classifying retina images of a different population, concretely from the Hospital de Reus. The methods proposed, achieve ophthalmologist performance level and are able to identify with great detail lesions present on images, inferred only from image classification information