Imaging of the human fundus in the clinical setting:past present and future

The human fundus is a complex structure that can be easily visualized and the world of ophthalmology is going through a golden era of new and exciting fundus imaging techniques; recent advances in technology have allowed a significant improvement in the imaging modalities clinicians have available to formulate a diagnostic and treatment plan for the patient, but there is constant on-going work to improve current technology and create new ideas in order to gather as much information as possible from the human fundus. In this article we shall summarize the imaging techniques available in the standard medical retina clinic (i.e. not limited to the research lab) and delineate the technologies that we believe will have a significant impact on the way clinicians will assess retinal and choroidal pathology in the not too distant future

Optic Nerve Head Image Analysis for Glaucoma Progression Detection

Glaucoma is a leading cause of visual disability across the world and when diagnosed the glaucoma patient will spend the rest of their life receiving treatment in managed clinical care. In the glaucoma clinic, retinal and optic nerve head (ONH) imaging can be used to help the clinician to manage patient treatment appropriately. By providing high resolution images of the optic nerve head structures and identifying changes therein related to disease onset and progression, an objective measure can be obtained as to how well or badly treatment is preventing further disease damage. This thesis contributes to the field of glaucoma progression detection by the analysis of clinical imaging data using confocal scanning laser tomography (CSLT). Primarily it is an investigation of how best to appraise and optimise current algorithms which aim to detect these glaucomatous structural changes in the optic nerve head. This is done by addressing how the performance of these methods can be best assessed in the absence of a gold standard for glaucomatous structural progression. Glaucoma expert assessment of photographs of the optic disc is the current clinical standard of assessing glaucomatous damage evident in the ONH. This is used in this thesis to act as a reference standard by which these algorithms can be compared. In addition, the statistical principles underpinning trend detection techniques are also investigated along with the performance of these techniques to detect trends in CSLT data in the presence of different types of measurement noise and image quality. A new computer model is developed and validated to simulate stable series of CSLT images, with realistic variability, which can be used to benchmark the false-positive rates of current and future progression algorithms. In conclusion, the main results reported in this thesis show that uncertainties involved in expert assessment of change in ONH photographs limits this as a reference standard for structural change in glaucoma. In addition, since stability in clinical datasets is uncertain, simulation using modelled series is shown to provide a new benchmark for comparing methods of progression detection

Optical coherence tomography: evaluation and clinical application

The ability to examine the appearance of the retina is of paramount importance for the diagnosis and monitoring of ophthalmic disease and for the evaluation of treatment outcomes. Direct cross-sectional imaging of retinal structure could be useful for early diagnosis and more sensitive monitoring of a variety of retinal conditions such as macular oedema and glaucoma. The view of the fundus given by ophthalmoscopy provides very limited depth information and clinicians will often have to resort to additional techniques such as flourescein angiography or visual field testing for information on structural abnormalities within the retina. Other currently available imaging techniques do not provide sufficient depth resolution to produce useful cross-sectional images of retinal structure. Optical coherence tomography (OCT) is a new imaging technique which is capable of producing cross-sectional images of the retina with a resolution that surpasses that of conventional imaging techniques. This new technique has axial resolution of around 1 O.tm and can resolve individual retinal layers, thus providing information on retinal structure. In principle, OCT is very similar to ultrasound however it makes use of a light source rather than an acoustic one. The technique is non-contact and non-invasive and is generally well tolerated by patients. This thesis describes the evaluation of this new imaging technique with regards to its potential within routine clinical practice. A number of investigations were performed to fuffil this evaluation. Tests were carried out to experimentally measure the system's resolution and the accuracy and precision of measurements made from the OCT scans. A number of factors that could affect the quality of the scans were identified and their effects were minimised wherever possible. The software provided with the system was rigorously tested and potential sources of error were identified. Various studies were undertaken to quantify the repeatability and reproducibility of measurements made from scans and normative values were established. These results were used to assess the ability of the technique to detect and quantify several retinal disorders. The potential of the technique for corneal imaging was investigated - a scanning protocol was established and customised software for processing cornea! scans was developed. The relationship between OCT bands and retinal morphology was investigated by correlating scans from canine retina with corresponding light microscopy images and by observing the position of retinal abnormalities on scans from patients with a variety of conditions that affected different parts of the retina. Finally the clinical potential of OCT was investigated by carrying out various studies on a number of retinal conditions. Further clinical studies which combine anatomical information from OCT with functional information from electrophysiology are currently underway. Current developments are aimed at improving the imaging processing features and user interface so as to provide a more robust, user-friendly system for routine clinical use

Novel methods for subcellular in vivo imaging of the cornea with the Rostock Cornea Module 2.0

The Rostock Cornea Module transforms a confocal laser scanning ophthalmoscope into a corneal confocal laser scanning microscope. In this thesis, an improved version, the Rostock Cornea Module 2.0, and its achieved results were demonstrated. These include a concave contact cap design to attenuate eye movements to improve 3D volume reconstruction, an oscillating focal plane to improve mosaicking of the subbasal nerve plexus, the integration of simultaneous optical coherence tomography, multiwavelength corneal imaging, the clinical usage, and the automated morphological characterization

Review on retrospective procedures to correct retinal motion artefacts in OCT imaging

Motion artefacts from involuntary changes in eye fixation remain a major imaging issue in optical coherence tomography (OCT). This paper reviews the state-of-the-art of retrospective procedures to correct retinal motion and axial eye motion artefacts in OCT imaging. Following an overview of motion induced artefacts and correction strategies, a chronological survey of retrospective approaches since the introduction of OCT until the current days is presented. Pre-processing, registration, and validation techniques are described. The review finishes by discussing the limitations of the current techniques and the challenges to be tackled in future developments

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

Correlation between perimetric indices and retinal nerve fibre layer thickness by OCT and GDX VCC in primary open angle glaucoma.

INTRODUCTION: Glaucoma is defined as a disturbance of the structural and functional integrity of the optic nerve that can usually be arrested or diminished by adequate lowering of the intraocular pressure. It is among the leading causes of blindness in the developing world and a major health problem in the developed world. World Health Organization Statistics, published in 1995 indicates that glaucoma accounts for blindness in 5.1 million persons or 13.5% of global blindness. AIMS AND OBJECTIVES: To evaluate the correlation between the Retinal nerve fibre layer parameters(RNFL) analysed by OCT and GDx VCC and the global perimetric indices obtained with octopus perimetry. To establish whether structural parameters provided by optical coherence tomography (OCT ) and GDx VCC can be used to reflect functional damage in the visual field. To evaluate the relationship between the RNFL parameters measured using OCT and GDx VCC. MATERIALS AND METHODS: This was a cross sectional study, prospectively planned. 67 eyes of 34 glaucoma patients attending glaucoma clinic were included in this study. The study was carried out in Glaucoma clinic, Regional Institute of Ophthalmology and Government Eye Hospital, Chennai between March 2005 and July 2006. DISCUSSION: This study was designed with the major objective to evaluate the relationship between perimetric indices and structural changes brought out by optical coherence tomography and GDX VCC RNFL parameters and to compare the results obtained by these two methods for quantitatively assessing the RNFL(OCT and GDX VCC). CONCLUSION: Outcomes of the study: In established glaucoma patients a significant correlation exists between the global perimetric indices and the RNFL thickness . The RNFL thicknesses measured by two different investigatory modalities OCT and GDx are well correlated. Among the GDx parameters, the NFI was found to be a better indicator of visual field damage than the average thickness. In conclusion, though visual field testing is subjective, at present it cannot be replaced by imaging modalities. The newer instruments are valuable tools that have become available to provide quantitative reproducible and objective measurements of RNFL thickness. Thus, structural information provided by the OCT and GDx and functional information provided by the field analysis are both important and complementary to each other

Explainable AI for retinal OCT diagnosis

Artificial intelligence methods such as deep learning are leading to great progress in complex tasks that are usually associated with human intelligence and experience. Deep learning models have matched if not bettered human performance for medical diagnosis tasks including retinal diagnosis. Given a sufficient amount of data and computational resources, these models can perform classification and segmentation as well as related tasks such as image quality improvement. The adoption of these systems in actual healthcare centers has been limited due to the lack of reasoning behind their decisions. This black box nature along with upcoming regulations for transparency and privacy exacerbates the ethico-legal challenges faced by deep learning systems. The attribution methods are a way to explain the decisions of a deep learning model by generating a heatmap of the features which have the most contribution to the model's decision. These are generally compared in quantitative terms for standard machine learning datasets. However, the ability of these methods to generalize to specific data distributions such as retinal OCT has not been thoroughly evaluated. In this thesis, multiple attribution methods to explain the decisions of deep learning models for retinal diagnosis are compared. It is evaluated if the methods considered the best for explainability outperform the methods with a relatively simpler theoretical background. A review of current deep learning models for retinal diagnosis and the state-of-the-art explainability methods for medical diagnosis is provided. A commonly used deep learning model is trained on a large public dataset of OCT images and the attributions are generated using various methods. A quantitative and qualitative comparison of these approaches is done using several performance metrics and a large panel of experienced retina specialists. The initial quantitative metrics include the runtime of the method, RMSE, and Spearman's rank correlation for a single instance of the model. Later, two stronger metrics - robustness and sensitivity are presented. These evaluate the consistency amongst different instances of the same model and the ability to highlight the features with the most effect on the model output respectively. Similarly, the initial qualitative analysis involves the comparison between the heatmaps and a clinician's markings in terms of cosine similarity. Next, a panel of 14 clinicians rated the heatmaps of each method. Their subjective feedback, reasons for preference, and general feedback about using such a system are also documented. It is concluded that the explainability methods can make the decision process of deep learning models more transparent and the choice of the method should account for the preference of the domain experts. There is a high degree of acceptance from the clinicians surveyed for using such systems. The future directions regarding system improvements and enhancements are also discussed

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

Comparison of two different methods of stereoscopic viewing and the effect of teaching on the assessment of the cup to disc ratio for glaucoma detection

This study examines the effect of teaching on a group of naive observers asked to determine the cup-to-disc ratio of a series of stereo photographs presented by two different methods using custom software StereoDxT developed at Cardiff University. One method of presentation made use of Nvidia 3D software and compatible hardware, while the other was a ‘low tech’ approach using red-cyan anaglyphs. In order to further inform the results of this study, the members of the glaucoma team at North Devon District Hospital (NDDH), consisting of ophthalmologists, optometrists and other staff, undertook a similar study. An experiment to examine the magnification factor of several different binocular indirect lenses routinely used in the glaucoma clinics was also undertaken. The study showed observers, following a training session with the more expensive presentation system, improved their performance relative to an expert observer, while those using anaglyph images returned equivocal results. Control observers’ performance remained the same throughout the study. When compared against qualified staff at NDDH it was found that generally naive observers could improve their ability to determine cup-to-disc ratios to a similar level of experienced practitioners. It is believed that the anaglyph approach could be a viable alternative to higher cost training ‘set ups’ provided images are carefully selected and produced in a controlled manner. Measurements of a simulated optic disc taken with the binocular indirect lenses at differing simulated ametropias and working distances were found to be inconsistent with all the lenses tested. This raises concerns that a single manufacturers’ magnification factor may not be ideal where accurate measurements of fundal structures such as the optic disc is concerned