A fully automated cell segmentation and morphometric parameter system for quantifying corneal endothelial cell morphology

YesBackground and Objective Corneal endothelial cell abnormalities may be associated with a number of corneal and systemic diseases. Damage to the endothelial cells can significantly affect corneal transparency by altering hydration of the corneal stroma, which can lead to irreversible endothelial cell pathology requiring corneal transplantation. To date, quantitative analysis of endothelial cell abnormalities has been manually performed by ophthalmologists using time consuming and highly subjective semi-automatic tools, which require an operator interaction. We developed and applied a fully-automated and real-time system, termed the Corneal Endothelium Analysis System (CEAS) for the segmentation and computation of endothelial cells in images of the human cornea obtained by in vivo corneal confocal microscopy. Methods First, a Fast Fourier Transform (FFT) Band-pass filter is applied to reduce noise and enhance the image quality to make the cells more visible. Secondly, endothelial cell boundaries are detected using watershed transformations and Voronoi tessellations to accurately quantify the morphological parameters of the human corneal endothelial cells. The performance of the automated segmentation system was tested against manually traced ground-truth images based on a database consisting of 40 corneal confocal endothelial cell images in terms of segmentation accuracy and obtained clinical features. In addition, the robustness and efficiency of the proposed CEAS system were compared with manually obtained cell densities using a separate database of 40 images from controls (n = 11), obese subjects (n = 16) and patients with diabetes (n = 13). Results The Pearson correlation coefficient between automated and manual endothelial cell densities is 0.9 (p < 0.0001) and a Bland–Altman plot shows that 95% of the data are between the 2SD agreement lines. Conclusions We demonstrate the effectiveness and robustness of the CEAS system, and the possibility of utilizing it in a real world clinical setting to enable rapid diagnosis and for patient follow-up, with an execution time of only 6 seconds per image

Novel medical imaging technologies for processing epithelium and endothelium layers in corneal confocal images. Developing automated segmentation and quantification algorithms for processing sub-basal epithelium nerves and endothelial cells for early diagnosis of diabetic neuropathy in corneal confocal microscope images

Diabetic Peripheral Neuropathy (DPN) is one of the most common types of diabetes that can affect the cornea. An accurate analysis of the corneal epithelium nerve structures and the corneal endothelial cell can assist early diagnosis of this disease and other corneal diseases, which can lead to visual impairment and then to blindness. In this thesis, fully-automated segmentation and quantification algorithms for processing and analysing sub-basal epithelium nerves and endothelial cells are proposed for early diagnosis of diabetic neuropathy in Corneal Confocal Microscopy (CCM) images. Firstly, a fully automatic nerve segmentation system for corneal confocal microscope images is proposed. The performance of the proposed system is evaluated against manually traced images with an execution time of the prototype is 13 seconds. Secondly, an automatic corneal nerve registration system is proposed. The main aim of this system is to produce a new informative corneal image that contains structural and functional information. Thirdly, an automated real-time system, termed the Corneal Endothelium Analysis System (CEAS) is developed and applied for the segmentation of endothelial cells in images of human cornea obtained by In Vivo CCM. The performance of the proposed CEAS system was tested against manually traced images with an execution time of only 6 seconds per image. Finally, the results obtained from all the proposed approaches have been evaluated and validated by an expert advisory board from two institutes, they are the Division of Medicine, Weill Cornell Medicine-Qatar, Doha, Qatar and the Manchester Royal Eye Hospital, Centre for Endocrinology and Diabetes, UK

Deep learning-based improvement for the outcomes of glaucoma clinical trials

Glaucoma is the leading cause of irreversible blindness worldwide. It is a progressive optic neuropathy in which retinal ganglion cell (RGC) axon loss, probably as a consequence of damage at the optic disc, causes a loss of vision, predominantly affecting the mid-peripheral visual field (VF). Glaucoma results in a decrease in vision-related quality of life and, therefore, early detection and evaluation of disease progression rates is crucial in order to assess the risk of functional impairment and to establish sound treatment strategies. The aim of my research is to improve glaucoma diagnosis by enhancing state of the art analyses of glaucoma clinical trial outcomes using advanced analytical methods. This knowledge would also help better design and analyse clinical trials, providing evidence for re-evaluating existing medications, facilitating diagnosis and suggesting novel disease management. To facilitate my objective methodology, this thesis provides the following contributions: (i) I developed deep learning-based super-resolution (SR) techniques for optical coherence tomography (OCT) image enhancement and demonstrated that using super-resolved images improves the statistical power of clinical trials, (ii) I developed a deep learning algorithm for segmentation of retinal OCT images, showing that the methodology consistently produces more accurate segmentations than state-of-the-art networks, (iii) I developed a deep learning framework for refining the relationship between structural and functional measurements and demonstrated that the mapping is significantly improved over previous techniques, iv) I developed a probabilistic method and demonstrated that glaucomatous disc haemorrhages are influenced by a possible systemic factor that makes both eyes bleed simultaneously. v) I recalculated VF slopes, using the retinal never fiber layer thickness (RNFLT) from the super-resolved OCT as a Bayesian prior and demonstrated that use of VF rates with the Bayesian prior as the outcome measure leads to a reduction in the sample size required to distinguish treatment arms in a clinical trial

Digital Images Assessment of Posterior Capsule Opacification

A digital imaging system has been developed for the objective assessment of severity and morphology of posterior capsule opacification (PCO). The system is based on a Nikon FS-2 photo slit-lamp and a Kodak DCS 100 digital camera system. The slit-lamp was used with conventional (forward) illumination and studies were carried out to determine the best combination of slit-lamp and camera parameters to optimize the images. These were found to be a slit beam width of 3.5mm, slit angle of 45°, film speed (ISO) of 1600 for the camera and 800 for the digital storage unit (DSU), exposure compensation 0, flash intensity 2 and a magnification of 25x. A fixation stimulus was developed since it was shown to decrease the variability of the images and reduce the possibility of unwanted reflexes affecting the area of interest. In all cases a central 3mm x 3mm area of the posterior capsule, centred on the pupil, was analysed. The image processing software used throughout the work was Image Pro+ v4.51 (Media Cybernetics, Silver Spring, MD). For validating the system, subjects who had previously had uncomplicated cataract surgery were recruited from the Nd:YAG capsulotomy clinics of the Royal Eye Unit at Kingston. Intra-observer repeatability was assessed by taking 6 repeat images for each of 10 subjects. The coefficient of variation for total spectral power in the image, a measure of the severity of opacification, was 1.5%. Inter-observer repeatability was tested for 3 observers, an ophthalmologist, an optometrist and a non-clinician, by recording 2 images for each observer for each of 9 subjects. There was a 6 fold increase in the limits of agreement when the non-clinician was involved indicating that suitable training is required to use the system in a repeatable fashion. Severity of opacification was measured by calculating the log of the total spectral power in the Fourier Transform image of the area of interest. Results on 45 eyes correlated well with subjective grading of PCO by an experienced ophthalmologist: Within the 3 morphological groups, fibrotic, mixed and pearl, correlation coefficients of 0.909, 0.864 and 0.713 were found, which were all statistically significant. Morphological classification was carried out by developing a simple expert system. The rules in the knowledge base were derived from known histological features of PCO and clinically observed changes. The weighted kappa score for agreement between the objective classifier and an experienced ophthalmologist was 0.61 indicating a good level of agreement. However 14 out of 45 cases did not exhibit agreement. It may be possible with further work to improve the classification rules to reduce this number. New methods have been presented for objectively measuring the severity and morphologically classifying PCO. The system developed in this work shows distinct potential for use within primary care environments since it uses techniques and instruments whose operation is familiar to all practitioners involved. The validation studies presented here demonstrate variability in the measurements that is largely in agreement with previously published systems

On the Indeterminates of Glaucoma:the Controversy of Arterial Blood Pressure and Retinal Perfusion

Glaucoma is a chronic eye disease characterized by thinning of the retina, death of ganglion cells, and progressive loss of vision, eventually leading to blindness. The prevalence of glaucoma is estimated at 1-3% of those over 40 years old. With a constantly aging population, this number is expected to increase significantly over the next 10 years. Even with treatment, about 15% of people with glaucoma currently develop residual vision or tunnel vision and eventually become blind or partially sighted. The mechanisms behind ganglion cell death are poorly understood. Elevated eye pressure is the main risk factor for glaucoma, but treatment in the form of medication, laser, or surgery can only slow the decline, not stop it. In addition, high intraocular pressure is neither necessary nor sufficient for the development of glaucoma, indicating the existence of other unknown risk factors. It has been established that the death of ganglion cells results in a decreased oxygen demand and a concomitant decrease in blood flow. However, there is also a hypothesis that reduced or unstable blood supply is not only a consequence, but also a cause of glaucoma. This is known as the ‘chicken-egg’ dilemma in glaucoma. It is supported by the observation that the risk of developing glaucoma is higher in people with very low blood pressure (sometimes even as a result of overtreatment of high blood pressure).This dissertation is an attempt to methodically examine whether blood pressure can be linked to changes in the retina that could suggest susceptibility to glaucoma. For this purpose, we analyze epidemiological data from the Groningen Longitudinal Glaucoma Study, we use advanced imaging techniques to model the microcirculation, and we describe its relationship with the neural structure and oxygen consumption of the retina. We provide evidence leaning towards the existence of a vascular component, likely pertinent to glaucoma

A modular, open-source information extraction framework for identifying clinical concepts and processes of care in clinical narratives

In this thesis, a synthesis is presented of the knowledge models required by clinical informa- tion systems that provide decision support for longitudinal processes of care. Qualitative research techniques and thematic analysis are novelly applied to a systematic review of the literature on the challenges in implementing such systems, leading to the development of an original conceptual framework. The thesis demonstrates how these process-oriented systems make use of a knowledge base derived from workflow models and clinical guidelines, and argues that one of the major barriers to implementation is the need to extract explicit and implicit information from diverse resources in order to construct the knowledge base. Moreover, concepts in both the knowledge base and in the electronic health record (EHR) must be mapped to a common ontological model. However, the majority of clinical guideline information remains in text form, and much of the useful clinical information residing in the EHR resides in the free text fields of progress notes and laboratory reports. In this thesis, it is shown how natural language processing and information extraction techniques provide a means to identify and formalise the knowledge components required by the knowledge base. Original contributions are made in the development of lexico-syntactic patterns and the use of external domain knowledge resources to tackle a variety of information extraction tasks in the clinical domain, such as recognition of clinical concepts, events, temporal relations, term disambiguation and abbreviation expansion. Methods are developed for adapting existing tools and resources in the biomedical domain to the processing of clinical texts, and approaches to improving the scalability of these tools are proposed and evalu- ated. These tools and techniques are then combined in the creation of a novel approach to identifying processes of care in the clinical narrative. It is demonstrated that resolution of coreferential and anaphoric relations as narratively and temporally ordered chains provides a means to extract linked narrative events and processes of care from clinical notes. Coreference performance in discharge summaries and progress notes is largely dependent on correct identification of protagonist chains (patient, clinician, family relation), pronominal resolution, and string matching that takes account of experiencer, temporal, spatial, and anatomical context; whereas for laboratory reports additional, external domain knowledge is required. The types of external knowledge and their effects on system performance are identified and evaluated. Results are compared against existing systems for solving these tasks and are found to improve on them, or to approach the performance of recently reported, state-of-the- art systems. Software artefacts developed in this research have been made available as open-source components within the General Architecture for Text Engineering framework