CAD system for early diagnosis of diabetic retinopathy based on 3D extracted imaging markers.

This dissertation makes significant contributions to the field of ophthalmology, addressing the segmentation of retinal layers and the diagnosis of diabetic retinopathy (DR). The first contribution is a novel 3D segmentation approach that leverages the patientspecific anatomy of retinal layers. This approach demonstrates superior accuracy in segmenting all retinal layers from a 3D retinal image compared to current state-of-the-art methods. It also offers enhanced speed, enabling potential clinical applications. The proposed segmentation approach holds great potential for supporting surgical planning and guidance in retinal procedures such as retinal detachment repair or macular hole closure. Surgeons can benefit from the accurate delineation of retinal layers, enabling better understanding of the anatomical structure and more effective surgical interventions. Moreover, real-time guidance systems can be developed to assist surgeons during procedures, improving overall patient outcomes. The second contribution of this dissertation is the introduction of a novel computeraided diagnosis (CAD) system for precise identification of diabetic retinopathy. The CAD system utilizes 3D-OCT imaging and employs an innovative approach that extracts two distinct features: first-order reflectivity and 3D thickness. These features are then fused and used to train and test a neural network classifier. The proposed CAD system exhibits promising results, surpassing other machine learning and deep learning algorithms commonly employed in DR detection. This demonstrates the effectiveness of the comprehensive analysis approach employed by the CAD system, which considers both low-level and high-level data from the 3D retinal layers. The CAD system presents a groundbreaking contribution to the field, as it goes beyond conventional methods, optimizing backpropagated neural networks to integrate multiple levels of information effectively. By achieving superior performance, the proposed CAD system showcases its potential in accurately diagnosing DR and aiding in the prevention of vision loss. In conclusion, this dissertation presents novel approaches for the segmentation of retinal layers and the diagnosis of diabetic retinopathy. The proposed methods exhibit significant improvements in accuracy, speed, and performance compared to existing techniques, opening new avenues for clinical applications and advancements in the field of ophthalmology. By addressing future research directions, such as testing on larger datasets, exploring alternative algorithms, and incorporating user feedback, the proposed methods can be further refined and developed into robust, accurate, and clinically valuable tools for diagnosing and monitoring retinal diseases

Graph Theory and Dynamic Programming Framework for Automated Segmentation of Ophthalmic Imaging Biomarkers

<p>Accurate quantification of anatomical and pathological structures in the eye is crucial for the study and diagnosis of potentially blinding diseases. Earlier and faster detection of ophthalmic imaging biomarkers also leads to optimal treatment and improved vision recovery. While modern optical imaging technologies such as optical coherence tomography (OCT) and adaptive optics (AO) have facilitated in vivo visualization of the eye at the cellular scale, the massive influx of data generated by these systems is often too large to be fully analyzed by ophthalmic experts without extensive time or resources. Furthermore, manual evaluation of images is inherently subjective and prone to human error.</p><p>This dissertation describes the development and validation of a framework called graph theory and dynamic programming (GTDP) to automatically detect and quantify ophthalmic imaging biomarkers. The GTDP framework was validated as an accurate technique for segmenting retinal layers on OCT images. The framework was then extended through the development of the quasi-polar transform to segment closed-contour structures including photoreceptors on AO scanning laser ophthalmoscopy images and retinal pigment epithelial cells on confocal microscopy images. </p><p>The GTDP framework was next applied in a clinical setting with pathologic images that are often lower in quality. Algorithms were developed to delineate morphological structures on OCT indicative of diseases such as age-related macular degeneration (AMD) and diabetic macular edema (DME). The AMD algorithm was shown to be robust to poor image quality and was capable of segmenting both drusen and geographic atrophy. To account for the complex manifestations of DME, a novel kernel regression-based classification framework was developed to identify retinal layers and fluid-filled regions as a guide for GTDP segmentation.</p><p>The development of fast and accurate segmentation algorithms based on the GTDP framework has significantly reduced the time and resources necessary to conduct large-scale, multi-center clinical trials. This is one step closer towards the long-term goal of improving vision outcomes for ocular disease patients through personalized therapy.</p>Dissertatio

Visual Impairment and Blindness

Blindness and vision impairment affect at least 2.2 billion people worldwide with most individuals having a preventable vision impairment. The majority of people with vision impairment are older than 50 years, however, vision loss can affect people of all ages. Reduced eyesight can have major and long-lasting effects on all aspects of life, including daily personal activities, interacting with the community, school and work opportunities, and the ability to access public services. This book provides an overview of the effects of blindness and visual impairment in the context of the most common causes of blindness in older adults as well as children, including retinal disorders, cataracts, glaucoma, and macular or corneal degeneration

Automatic detection of drusen associated with age-related macular degeneration in optical coherence tomography: a graph-based approach

Tese de Doutoramento em Líderes para Indústrias TecnológicasThe age-related macular degeneration (AMD) starts to manifest itself with the appearance of drusen. Progressively, the drusen increase in size and in number without causing alterations to vision. Nonetheless, their quantification is important because it correlates with the evolution of the disease to an advanced stage, which could lead to the loss of central vision. Manual quantification of drusen is impractical, since it is time-consuming and it requires specialized knowledge. Therefore, this work proposes a method for quantifying drusen automatically In this work, it is proposed a method for segmenting boundaries limiting drusen and another method for locating them through classification. The segmentation method is based on a multiple surface framework that is adapted for segmenting the limiting boundaries of drusen: the inner boundary of the retinal pigment epithelium + drusen complex (IRPEDC) and the Bruch’s membrane (BM). Several segmentation methods have been considerably successful in segmenting layers of healthy retinas in optical coherence tomography (OCT) images. These methods were successful because they incorporate prior information and regularization. However, these factors have the side-effect of hindering the segmentation in regions of altered morphology that often occur in diseased retinas. The proposed segmentation method takes into account the presence of lesion related with AMD, i.e., drusen and geographic atrophies (GAs). For that, it is proposed a segmentation scheme that excludes prior information and regularization that is only valid for healthy regions. Even with this segmentation scheme, the prior information and regularization can still cause the oversmoothing of some drusen. To address this problem, it is also proposed the integration of local shape priors in the form of a sparse high order potentials (SHOPs) into the multiple surface framework. Drusen are commonly detected by thresholding the distance among the boundaries that limit drusen. This approach misses drusen or portions of drusen with a height below the threshold. To improve the detection of drusen, Dufour et al. [1] proposed a classification method that detects drusen using textural information. In this work, the method of Dufour et al. [1] is extended by adding new features and performing multi-label classification, which allow the individual detection of drusen when these occur in clusters. Furthermore, local information is incorporated into the classification by combining the classifier with a hidden Markov model (HMM). Both the segmentation and detections methods were evaluated in a database of patients with intermediate AMD. The results suggest that both methods frequently perform better than some methods present in the literature. Furthermore, the results of these two methods form drusen delimitations that are closer to expert delimitations than two methods of the literature.A degenerescência macular relacionada com a idade (DMRI) começa a manifestar-se com o aparecimento de drusas. Progressivamente, as drusas aumentam em tamanho e em número sem causar alterações à visão. Porém, a sua quantificação é importante porque está correlacionada com a evolução da doença para um estado avançado, levar à perda de visão central. A quantificação manual de drusas é impraticável, já que é demorada e requer conhecimento especializado. Por isso, neste trabalho é proposto um método para segmentar drusas automaticamente. Neste trabalho, é proposto um método para segmentar as fronteiras que limitam as drusas e outro método para as localizar através de classificação. O método de segmentação é baseado numa ”framework” de múltiplas superfícies que é adaptada para segmentar as fronteiras que limitam as drusas: a fronteira interior do epitélio pigmentar + complexo de drusas e a membrana de Bruch. Vários métodos de segmentação foram consideravelmente bem-sucedidos a segmentar camadas de retinas saudáveis em imagens de tomografia de coerência ótica. Estes métodos foram bem-sucedidos porque incorporaram informação prévia e regularização. Contudo, estes fatores têm como efeito secundário dificultar a segmentação em regiões onde a morfologia da retina está alterada devido a doenças. O método de segmentação proposto toma em consideração a presença de lesões relacionadas com DMRI, .i.e., drusas e atrofia geográficas. Para isso, é proposto um esquema de segmentação que exclui informação prévia e regularização que são válidas apenas em regiões saudáveis da retina. Mesmo com este esquema de segmentação, a informação prévia e a regularização podem causar a suavização excessiva de algumas drusas. Para tentar resolver este problema, também é proposta a integração de informação prévia local sob a forma de potenciais esparsos de ordem elevada na ”framework” multi-superfície. As drusas são usalmente detetadas por ”thresholding” da distância entre as fronteiras que limitam as drusas. Esta abordagem falha drusas ou porções de drusas abaixo do ”threshold”. Para melhorar a deteção de drusas, Dufour et al. [1] propuseram um método de classificação que deteta drusas usando informação de texturas. Neste trabalho, o método de Dufour et al. [1] é estendido, adicionando novas características e realizando uma classificação com múltiplas classes, o que permite a deteção individual de drusas em aglomerados. Além disso, é incorporada informação local na classificação, combinando o classificador com um modelo oculto de Markov. Ambos os métodos de segmentação e deteção foram avaliados numa base de dados de pacientes com DMRI intermédia. Os resultados sugerem que ambos os métodos obtêm frequentemente melhores resultados que alguns métodos descritos na literatura. Para além disso, os resultados destes dois métodos formam delimitações de drusas que estão mais próximas das delimitações dos especialistas que dois métodos da literatura.This work was supported by FCT with the reference project UID/EEA/04436/2013, by FEDER funds through the COMPETE 2020 – Programa Operacional Competitividade e Internacionalização (POCI) with the reference project POCI-01-0145-FEDER-006941. Furthermore, the Portuguese funding institution Fundação Calouste Gulbenkian has conceded me a Ph.D. grant for this work. For that, I wish to acknowledge this institution. Additionally, I want to thank one of its members, Teresa Burnay, for all her assistance with issues related with the grant, for believing that my work was worth supporting and for encouraging me to apply for the grant

Retinal structure and function in age-related maculopathy

Age-related macular degeneration (AMD) is the principle cause of visual loss and blindness in the developed world. As new treatments and therapies are developed the need to better diagnose and then monitor outcomes of treatment has become more important. This thesis evaluates both structural and functional changes that occur in the early stage of AMD, known as age-related maculopathy (ARM), with the aim of determining their diagnostic potential. This thesis also explores the relationship between structural and functional parameters. Twenty four participants with ARM and 26 control participants were recruited. Retinal function was probed using four focal electroretinography (ERG) techniques: the focal cone ERG, focal flicker ERG, ERG photostress test and focal rod ERG. Long wavelength optical coherence tomography (OCT) was used to assess retinal structure, specifically retinal, choroidal and four intra-retinal layer thicknesses at 21 macular locations. These techniques were initially developed and optimised for the detection of AMD related changes. The ability of each parameter to diagnose ARM was assessed. Correlation and linear regression analyses were carried out to identify any relationships between retinal structure and function in healthy controls. Retinal thickness was reduced in participants with ARM at parafoveal locations (~2° eccentricity), but choroid thickness was unaffected. Diagnostically, focal ERG parameters provided better sensitivity and specificity to ARM than OCT measures, with the ERG photostress test providing the best diagnostic potential. No strong relationships were shown between any ERG parameter and any retinal or choroidal layer volume in control participants. Three ERG parameters were shown to be related to specific retinal features of ARM, but the strongest associations were between ERG photostress test recovery and focal cone ERG b-wave implicit time and a diagnosis of wet AMD in the contralateral eye. In conclusion the structural and functional parameters assessed appeared to provide independent information regarding disease status and severity. ERG parameters showed better diagnostic potential than OCT measures. The single most diagnostic parameter was the recovery time constant of the ERG photostress test.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

The Impact of Fundus Autofluorescence on the Management of Age-related Macular Degeneration

Background: Fundus autofluorescence (FAF) has been described as a topographical map of fluorophores that accumulate within the retinal pigment epithelium as a result of disease. Study aims: To evaluate whether FAF offers information relevant to age-related macular degeneration over that gathered via colour fundus photography (CFP) and optical coherence tomography (OCT). Methods: Ninety-three patients were imaged via CFP, OCT and FAF and the results analysed using Orange Data Mining artificial intelligence and SPSS software. Results: Pupillary dilation makes a significant improvement to FAF image quality. Nuclear sclerotic cataract of > 1.5 on the World Health Organisation scale indicates that there is ≃85% probability that the FAF image will not be of high quality. At > 1.9 there is ≃50% probability of the image not being clinically useful as defined by a novel grading scale. Age was negatively associated with FAF comfort. There is ≥ 90% probability of an abnormal FAF result for an eye with any of the following: > 50 small, > 40 intermediate, > 20 large drusen. Age > 92 years. > 30 packet years of smoking. Any pigmentary abnormalities. ≃80% for any reticular pseudodrusen (RPD). FAF results can be predicted via CFP and OCT data using machine learning with informedness of up to 70.2% and area under the curve (AUC) of 0.903. For transfer learning to be useful within primary care, image pre-processing is likely to be required. Geographic atrophy and pigment epithelial detachments appear to be linked to a patchy FAF pattern. RPD are linked to a reticular FAF pattern. Principle component analysis indicates that drusen were responsible for the greatest percentage of variability in this study’s data (38.6%). Conclusions: Clinical impact: FAF results can be predicted from CFP/OCT via machine learning with 70.2% informedness and AUC of 0.903. Drusen number/size were the most informative variables

Retinal structure and function in age-related maculopathy

Age-related macular degeneration (AMD) is the principle cause of visual loss and blindness in the developed world. As new treatments and therapies are developed the need to better diagnose and then monitor outcomes of treatment has become more important. This thesis evaluates both structural and functional changes that occur in the early stage of AMD, known as age-related maculopathy (ARM), with the aim of determining their diagnostic potential. This thesis also explores the relationship between structural and functional parameters. Twenty four participants with ARM and 26 control participants were recruited. Retinal function was probed using four focal electroretinography (ERG) techniques: the focal cone ERG, focal flicker ERG, ERG photostress test and focal rod ERG. Long wavelength optical coherence tomography (OCT) was used to assess retinal structure, specifically retinal, choroidal and four intra-retinal layer thicknesses at 21 macular locations. These techniques were initially developed and optimised for the detection of AMD related changes. The ability of each parameter to diagnose ARM was assessed. Correlation and linear regression analyses were carried out to identify any relationships between retinal structure and function in healthy controls. Retinal thickness was reduced in participants with ARM at parafoveal locations (~2° eccentricity), but choroid thickness was unaffected. Diagnostically, focal ERG parameters provided better sensitivity and specificity to ARM than OCT measures, with the ERG photostress test providing the best diagnostic potential. No strong relationships were shown between any ERG parameter and any retinal or choroidal layer volume in control participants. Three ERG parameters were shown to be related to specific retinal features of ARM, but the strongest associations were between ERG photostress test recovery and focal cone ERG b-wave implicit time and a diagnosis of wet AMD in the contralateral eye. In conclusion the structural and functional parameters assessed appeared to provide independent information regarding disease status and severity. ERG parameters showed better diagnostic potential than OCT measures. The single most diagnostic parameter was the recovery time constant of the ERG photostress test