An automated image processing system for the detection of photoreceptor cells in adaptive optics retinal images

The rapid progress in Adaptive Optics (AO) imaging, in the last decades, has had a transformative impact on the entire approach underpinning the investigations of retinal tissues. Capable of imaging the retina in vivo at the cellular level, AO systems have revealed new insights into retinal structures, function, and the origins of various retinal pathologies. This has expanded the field of clinical research and opened a wide range of applications for AO imaging. The advances in image processing techniques contribute to a better observation of retinal microstructures and therefore more accurate detection of pathological conditions. The development of automated tools for processing images obtained with AO allows for objective examination of a larger number of images with time and cost savings and thus facilitates the use of AO imaging as a practical and efficient tool, by making it widely accessible to the clinical ophthalmic community. In this work, an image processing framework is developed that allows for enhancement of AO high-resolution retinal images and accurate detection of photoreceptor cells. The proposed framework consists of several stages: image quality assessment, illumination compensation, noise suppression, image registration, image restoration, enhancement and detection of photoreceptor cells. The visibility of retinal features is improved by tackling specific components of the AO imaging system, affecting the quality of acquired retinal data. Therefore, we attempt to fully recover AO retinal images, free from any induced degradation effects. A comparative study of different methods and evaluation of their efficiency on retinal datasets is performed by assessing image quality. In order to verify the achieved results, the cone packing density distribution was calculated and correlated with statistical histological data. From the performed experiments, it can be concluded that the proposed image processing framework can effectively improve photoreceptor cell image quality and thus can serve as a platform for further investigation of retinal tissues. Quantitative analysis of the retinal images obtained with the proposed image processing framework can be used for comparison with data related to pathological retinas, as well as for understanding the effect of age and retinal pathology on cone packing density and other microstructures

Astigmatism and Pseudoaccommodation in Pseudophakic Eyes

noAdvanced IOLs with circumferential zones of different power provide pseudoaccommodation. We investigated the potential for power variation with meridian, namely astigmatism, to provide pseudo-accommodation. With appropriate power and axis orientations, acceptable pseudo-accommodation can be achieved

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

The assessment of visual behaviour and depth perception in surgery

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Improving the structure function relationship in the macula

The macula is the central part of the retina responsible for central vision and can suffer damage from many diseases, including diabetes, macular degeneration and glaucoma. Establishing a relationship between functional measurements, such as perimetry, and structural metrics, such as those obtained through imaging, has proven both clinically appealing and challenging, owing to specific features of this area of the retina. The programme of work presented in this thesis focuses on improving the accuracy of structure-function analyses of the macula as well as the mechanistic understanding of structure-function relationship in both healthy and diseased eyes. The first study revisits and improves previous models quantifying the length of Henle’s fibres. This directly relates to the radial displacement of Retinal Ganglion Cells (RGCs) from their photoreceptors and affects structure-function mapping. The study demonstrated the inaccuracy of previous methods used to displace perimetric stimuli, proposing a correct implementation of these calculations. These results were made available to other researchers in a user-friendly web application. The second study explored how natural positioning of observers in front of imaging and perimetry devices, as well as their fixation and eye movements, affected the precision of macular structure-function mapping. The study analysed data from an eye-tracking perimeter used to test both healthy eyes and patients with glaucoma. An optimal strategy for structure-function mapping was developed and the mapping error introduced by fixation was quantified. The third study used data from an eye-tracking perimeter and the framework of an established neural model of spatial summation to investigate the structure-function relationship in early neural loss in patients with diabetes without diabetic retinopathy, quantified with both imaging and functional tests, including Frequency Doubling Perimetry, standard visual acuity and contrast sensitivity. The fourth study involved the prospective collection of data from healthy observers with perimetric stimuli of different sizes and durations, using custom software. The data were used to develop a computational model of perimetric sensitivity able to reproduce the interaction between spatial and temporal summation in the context of cortical integration and their link to the number of retinal ganglion cells being stimulated. In the fifth study, the methodology and mechanistic framework developed in the previous studies were applied to test the computational model in glaucoma. The model was used to obtain functional estimates of retinal ganglion cell damage from standard automated perimetry data collected in glaucoma patients and healthy age-related controls. The results were correlated with imaging and histology data from previous literature

Adaptive Optics Progress

For over four decades there has been continuous progress in adaptive optics technology, theory, and systems development. Recently there also has been an explosion of applications of adaptive optics throughout the fields of communications and medicine in addition to its original uses in astronomy and beam propagation. This volume is a compilation of research and tutorials from a variety of international authors with expertise in theory, engineering, and technology. Eight chapters include discussion of retinal imaging, solar astronomy, wavefront-sensorless adaptive optics systems, liquid crystal wavefront correctors, membrane deformable mirrors, digital adaptive optics, optical vortices, and coupled anisoplanatism

Human Embryonic Stem Cell-Derived Retinal Pigment Epithelium Transplantation in Advanced Neovascular Age-Related Macular Degeneration

Age-related macular degeneration (AMD) remains one of the leading causes of permanent vision impairment worldwide. It is a disorder of the central retina that manifests with irreversible cell loss, primarily affecting the retinal pigment epithelium (RPE) and subsequently the retina and choroid, leading to blindness through atrophy or neovascularization and exudation. Current treatments are only able to suppress the progression of the early and moderate neovascular AMD, mainly by controlling leakage and haemorrhage, while there is no established therapy for the atrophic type or the advanced neovascular type. RPE transplantation strategies have been attempted with promising outcomes; however, their operational complexity combined with the large patients’ volume has underlined the need for more accessible cell sources and a more feasible surgical paradigm. This thesis aims to examine the feasibility, safety and efficacy of transplantation of a human Embryonic Stem Cell (hESC)-derived RPE sheet in patients with severe neovascular (n) AMD. A fully differentiated hESC-RPE monolayer on a coated synthetic basement membrane (BM) has been bioengineered ex vivo and, using a purpose-designed surgical tool, has been implanted in the subretinal space of two patients with nAMD and acute vision decline. Systemic immunosuppression was administered during the peri- operative periods, while only local, intra-ocular steroids were given for the longer term. The patients were followed-up in a prospective study to assess the safety, and the structural and functional outcomes of this strategy for two years post-operatively. Both subjects demonstrated good safety outcome with no signs of local or distal tumorigenicity or uncontrolled proliferation from the implanted cells. Both showed reconstruction of the RPE-BM complex sufficient to support the retinal structure and the rescue and preservation of the photoreceptors, during the study period. Furthermore, both patients showed significant gain in their visual function, in terms of fixation, retinal light sensitivity, visual acuity and reading speed, maintained for two years. Most importantly, in both cases there was a clear co-localisation of the structural support, provided by the transplant, with the areas of functional improvement. The work in this thesis provides proof that the reconstruction of the RPE using hESC on synthetic BM can rescue and preserve the retinal structure and function over the long term, in severe neovascular AMD

Evaluation of the potentials for optical coherence tomography (OCT) to detect early signs of retinal neurodegeneration

Among neuroretinal degenerations, glaucoma and age-related macular degeneration (AMD) have become the most frequent reasons for irreversible blindness globally. Among the causes of the elderly and senile dementia, Alzheimer’s disease (AD) has the leading position, the early ocular symptoms of which can potentially be a prognostic factor. The aim of this thesis was the early in vivo ligand-free detection of degenerative changes in the inner and outer retinal layers, which was possible using high-resolution optical coherence tomography (OCT) with the machine learning (ML) algorithms: support vector machine (SVM) and principal component analysis (PCA). Prior to the application of SVM and PCA for the classification of human OCT images, evaluation of the classifiers was performed in the classification of optical phantoms, the accuracy of which was in the range of 82-100%. This was the first attempt to measure the textural properties of various polystyrene and silica beads optical phantoms. To identify optical changes that characterise early apoptosis, OCT imaging of axotomised retinal ganglion cells (RGCs) in ex vivo retinal murine explants was performed. Substantial optical alterations in RGC dendrites in the early stages of apoptosis (up to 2 hours) were detected. ML algorithms correctly classified the retinal texture of the inner plexiform layer (IPL) of transgenic AD mice in all cases, indicating the potential for further investigation in in vivo animal and human studies. Not only the optical signature but also the transparency of the dissected murine retinal explants was investigated. Moreover, ML classification of 3xTg mice IPL layer was studied in terms of optical changes due to the RGD dendritic atrophy. ML classifiers’ accuracy in the detection of early and neovascular AMD was 93-100% for the texture of retinal pigment epithelium, 69-67% for the outer nuclear layer, 70% for the inner segment and 60-90% for the outer segment of photoreceptors. Classification of AMD stages and comparison with the age-matched healthy controls was carried out in the outer retina and RPE. Grey-level co-occurrence, run-length matrices, local binary patterns features were extracted from the IPL of the macula to classify glaucoma OCT images. The accuracy of linear and non-linear SVMs, linear and quadratic discriminant analyses, decision tree and logistic regression was between 55-70%. Based on the classifiers’ precision, recall and F1-score, Gaussian SVM outperformed other ML techniques. In this study, the observation of early glaucomatous subtle optical changes of human IPL was conducted. Also, the significance of various supervised ML algorithms was investigated. Understanding the optical signature of cumulative inherent speckle of OCT scans arising from apoptotic retinal ganglion cells and photoreceptors may provide vital information for the prevention of retinal neurodegeneration