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

Autosoom-retsessiivne Stargardti tõbi: fenotüübiline heterogeensus ja genotüübi-fenotüübi seosed

Väitekirja elektrooniline versioon ei sisalda publikatsiooneStargardti tõbi (STGD1) on kõige sagedasem pärilik võrkkesta kollatähni düstroofia põhjustades progresseeruvat nägemislangust sageli juba lapseeast. Haigust põhjustavad mutatsioonid ABCA4 geenis, mille tulemusena ladestuvad võrkkesta toksilised jääkproduktid põhjustades fotoretseptorite kadu. Praeguseks on teada üle 1000 haigustekkelise mutatsiooni ABCA4 geenis, mille tõttu on haiguse kliiniline pilt (fenotüüp) väga mitmekesine. Meie uuringu peamisteks eesmärkideks olid STGD1 puhuste võrkkesta struktuurimuutuste analüüs ja leida võimalikke seoseid haiguse fenotüüpide ja (ABCA4) mutatsioonide vahel. Doktoritöös leidsime, et vastupidiselt senise arusaama kohaselt esineb varases haiguse faasis noortel STGD1 haigetel lisaks fotoretseptorite kihi õhenemisele välise piirimembraani oluline paksenemine võrreldes kontrollgrupiga. Tõenäoliselt iseloomustab leid võrkkesta gliiarakkude mööduvat hüpertroofiat, olles STGD1 varajaseks kliiniliseks markeriks, mis omab kliinilist potentsiaali diagnoosimaks haigust varases faasis. Teiseks analüüsisime STGD1 korral harva esinevat fenotüüpi, mida nimetatakse kollatähni tsentri (fovea) tühimikuks (ingl.k. optical gap; foveal cavitation). Tegemist on foveas paiknevate fotoretseptorite kaoga, mille tulemusena moodustub võrkkesta väliskihtidesse struktuurne tühimik. Võrkkesta kuvamisuuringute analüüsil näitasime, kuidas fenotüüp dünaamiliselt kujuneb ja kaob ning lõime selle baasil arengustaadiumid. Vastupidiselt siiani juhtivale arusaamale, et STGD1 puhul esineb primaarselt võrkkesta pigmentepiteelirakkude (RPE) kõhetumine näitasime kuvamisuuringutel, et fovea tühimiku korral toimub esmaselt just fotoretseptorite kadu, millele järgneb RPE atroofia. Ühtlasi leidsime genotüübi-fenotüübi analüüsil, et STGD1 haigetel esineb statistiliselt tugev seos ABCA4 geenis paikneva p. G1961E mutatsiooni ja fovea tühimiku vahel. Kolmandaks näitasime, et STGD1 võib fenotüübilt sarnaneda hüdroksüklorokviin (Plaquenil) võrkkesta toksiliste muutusega. Moodustub nn „foveat säilitava“ fenotüübi variant, mille tulemusena on kollatähni teravanägemise punktis võrkkest suhteliselt hästi säilinud ning patsiendid on avastamise hetkel enamasti sümptomite vabad. Fenotüüpide detailne iseloomustamine, dünaamika hindamine ja genotüübi-fenotüübi seoste kirjeldamine võiks paremini aidata planeerida ja analüüsida ravimi, geeniteraapia ja tüvirakuteraapia uuringuid ja nende tulemusi STGD1 kontekstisStargardt disease (STGD1) is the most common form of inherited macular dystrophy causing progressive visual loss often from childhood. The disease is caused by mutations in the ABCA4 gene resulting in progressive accumulation of toxic visual cycle byproducts in the retina leading to photoreceptor loss. More than 1000 disease-causing ABCA4 mutations have been described resulting in remarkable diverse clinical (phenotypic) expression of the disease. The main aims of our study were to analyze STGD1-associated retinal structural changes as well as phenotypic expression via multimodal imaging and to detect possible genotype-phenotype associations. In the thesis studies we determined that in addition to thinning of photoreceptor-associated (ellipsoid zone) layer, there is a statistically significant thickening of external limiting membrane in young STGD1 patients compared to age-matched controls. The finding describes probably transient hypertrophy of retinal glial cells and could clinically be an important early stage disease marker, holding a diagnostic potential in early diagnosis of the STGD1. We also analyzed STGD1 patients with foveal cavitation (optical gap) phenotype. It is a rare manifestation of the STGD1 characterized by focal loss of photoreceptors in the fovea leaving a sub-foveal optically empty space detectable with spectral- domain optical coherent tomography (SD-OCT). With multimodal imaging we showed how the phenotype develops and dynamically behaves, based on which we described developmental stages of this phenotype. In contrast to the common understanding, that the first cells being affected in STGD1 are the retinal pigment epithelial (RPE) cells, we showed that, in foveal cavitation, the first cells which disappear are photoreceptors followed by atrophy of the RPE cells. We also detected a strong association between foveal cavitation phenotype and p.G1961E mutation in STGD1. We also showed that STGD1 could phenocopy hydroxychloroquine (Plaquenil) maculopathy. The specific foveal sparing phenotype has quite unique conformation on SD-OCT, where foveal area is relatively well spared preserving a very good visual acuity. Detailed phenotypic descriptions and dynamic assessment via multimodal imaging as well as detecting new genotype-phenotype associations potentially improves the planning of pharmacological, gene- and stem cell-based treatment studies of STGD1

Corneal confocal microscopy for diagnosis of diabetic peripheral neuropathy: an analysis of patients with diabetes screened as part of the South Manchester Diabetic Retinopathy Screening Service

Background and Aims: Quantitative assessment of small nerve fibre damage is key to the early diagnosis of diabetic peripheral neuropathy (DPN) and assessment of its progression. Corneal confocal microscopy (CCM) is a non-invasive, in-vivo diagnostic technique that provides an accurate surrogate biomarker for small fibre neuropathy. Its diagnostic efficacy has been previously validated in several studies. This thesis uses CCM images obtained, for the first time, in a large cohort of patients whose CCM examinations were undertaken during retinopathy screening in primary care. The following were the primary aims of the study: 1. To determine the prevalence of diabetic peripheral neuropathy, as defined by CCM parameters in a cohort of people with diabetes 2.To assess whether abnormalities in corneal nerve fibre morphology are present during the first two years following diabetes diagnosis. 3. To assess whether abnormalities in corneal nerve morphology are present prior to any retinopathy, defined as grade 1 or more. 4. To assess whether abnormalities in corneal nerve morphology are present prior to clinical evidence of diabetic neuropathy, as defined by diabetic neuropathic symptom (DNS) scoring of 1 or more The hypotheses for these main aims were that firstly, the prevalence of diabetic peripheral neuropathy, defined using CCM parameters would be lower in this population in comparison to previous CCM studies using patients under the hospital eye service to determine prevalence of DPN. There will be evidence of abnormalities in corneal nerve fibre morphology in some, but not all, patients with diabetic disease duration of less than or equal to 2 years, patients with retinopathy and maculopathy grade 0 and patients with a DNS score of 0. Methods: In this retrospective, primary care, cross-sectional study, 427 patients with diabetes (18 T1DM, 407 T2DM, 2 unknown) and 40 healthy controls underwent quantification of corneal nerve parameters using both automated and semi-automated analysis software. Clinical levels of neuropathy were assessed via diabetic neuropathy symptom score (DNS). Diabetic Retinopathy (DR) was graded using the Early Treatment Diabetic Retinopathy Study (ETDRS) grading scale. Results: Patients with diabetes demonstrated significant differences in all nerve parameters in comparison to healthy control subjects (p0.05). There was no significant difference in any CCM parameters between white and black patients with diabetes (p>0.05). Automated software showed poor agreement with semi-automated results, with a general underestimation for CNFD, CNFL and CNBD. Conclusion: In patients attending primary care screening, CCM in a sensitive biomarker for DPN. Semi-automated CCM quantification reliably detected corneal nerve abnormalities soon after diagnosis of diabetes. Changes in corneal nerve morphology were present prior to any neuropathy symptoms or retinopathy. CCM measured using automatic software requires development to improve agreement with semi-automated analysis

Advanced image processing techniques for detection and quantification of drusen

Dissertation presented to obtain the degree of Doctor of Philosophy in Electrical Engineering, speciality on Perceptional Systems, by the Universidade Nova de Lisboa, Faculty of Sciences and TechnologyDrusen are common features in the ageing macula, caused by accumulation of extracellular materials beneath the retinal surface, visible in retinal fundus images as yellow spots. In the ophthalmologists’ opinion, the evaluation of the total drusen area, in a sequence of images taken during a treatment, will help to understand the disease progression and effectiveness. However, this evaluation is fastidious and difficult to reproduce when performed manually. A literature review on automated drusen detection showed that the works already published were limited to techniques of either adaptive or global thresholds which showed a tendency to produce a significant number of false positives. The purpose for this work was to propose an alternative method to automatically quantify drusen using advanced digital image processing techniques. This methodology is based on a detection and modelling algorithm to automatically quantify drusen. It includes an image pre-processing step to correct the uneven illumination by using smoothing splines fitting and to normalize the contrast. To quantify drusen a detection and modelling algorithm is adopted. The detection uses a new gradient based segmentation algorithm that isolates drusen and provides basic drusen characterization to the modelling stage. These are then fitted by Gaussian functions, to produce a model of the image, which is used to compute the affected areas. To validate the methodology, two software applications, one for semi-automated (MD3RI) and other for automated detection of drusen (AD3RI), were implemented. The first was developed for Ophthalmologists to manually analyse and mark drusen deposits, while the other implemented algorithms for automatic drusen quantification.Four studies to assess the methodology accuracy involving twelve specialists have taken place. These compared the automated method to the specialists and evaluated its repeatability. The studies were analysed regarding several indicators, which were based on the total affected area and on a pixel-to-pixel analysis. Due to the high variability among the graders involved in the first study, a new evaluation method, the Weighed Matching Analysis, was developed to improve the pixel-to-pixel analysis by using the statistical significance of the observations to differentiate positive and negative pixels. From the results of these studies it was concluded that the methodology proposed is capable to automatically measure drusen in an accurate and reproducible process. Also, the thesis proposes new image processing algorithms, for image pre-processing, image segmentation,image modelling and images comparison, which are also applicable to other image processing fields

Retinal Image Registration and Comparison for Clinical Decision Support

Background For eye diseases, such as glaucoma and age-related macular degeneration (ARMD), involved in long-term degeneration procedure, longitudinal comparison of retinal images is a common step for reliable diagnosis of these kinds of diseases. Aims To provide a retinal image registration approach for longitudinal retinal image alignment and comparison. Method Two image registration solutions were proposed for facing different image qualities of retinal images to make the registration methods more robust and feasible in a clinical application system. Results Thirty pairs of longitudinal retinal images were used for the registration test. The experiments showed both solutions provided good performance for the accurate image registrations with efficiency. Conclusion We proposed a set of retinal image registration solutions for longitudinal retinal image observation and comparison targeting a clinical application environment

Visual field and structural alterations in age-related macular degeneration

The thesis investigated progression of the central 10° visual field with structural changes at the macula in a cross-section of patients with varying degrees of agerelated macular degeneration (AMD). The relationships between structure and function were investigated for both standard and short-wavelength automated perimetry (SWAP). Factors known to influence the measure of visual field progression were considered, including the accuracy of the refractive correction on SWAP thresholds and the learning effect. Techniques of assessing the structure to function relationships between fundus images and the visual field were developed with computer programming and evaluated for repeatability. Drusen quantification of fundus photographs and retro-mode scanning laser ophthalmoscopic images was performed. Visual field progression was related to structural changes derived from both manual and automated methods. Principal Findings: • Visual field sensitivity declined with advancing stage of AMD. SWAP showed greater sensitivity to progressive changes than standard perimetry. • Defects were confined to the central 5°. SWAP defects occurred at similar locations but were deeper and wider than corresponding standard perimetry defects. • The central field became less uniform as severity of AMD increased. SWAP visual field indices of focal loss were of more importance when detecting early change in AMD, than indices of diffuse loss. • The decline in visual field sensitivity over stage of severity of AMD was not uniform, whereas a linear relationship was found between the automated measure of drusen area and visual field parameters. • Perimetry exhibited a stronger relationship with drusen area than other measures of visual function. • Overcorrection of the refraction for the working distance in SWAP should be avoided in subjects with insufficient accommodative facility. • The perimetric learning effect in the 10° field did not differ significantly between normal subjects and AMD patients. • Subretinal deposits appeared more numerous in retro-mode imaging than in fundus photography

Automatic screening and grading of age-related macular degeneration from texture analysis of fundus images

Age-related macular degeneration (AMD) is a disease which causes visual deficiency and irreversible blindness to the elderly. In this paper, an automatic classification method for AMD is proposed to perform robust and reproducible assessments in a telemedicine context. First, a study was carried out to highlight the most relevant features for AMD characterization based on texture, color, and visual context in fundus images. A support vector machine and a random forest were used to classify images according to the different AMD stages following the AREDS protocol and to evaluate the features' relevance. Experiments were conducted on a database of 279 fundus images coming from a telemedicine platform. The results demonstrate that local binary patterns in multiresolution are the most relevant for AMD classification, regardless of the classifier used. Depending on the classification task, our method achieves promising performances with areas under the ROC curve between 0.739 and 0.874 for screening and between 0.469 and 0.685 for grading. Moreover, the proposed automatic AMD classification system is robust with respect to image quality