Automatic detection of fluorescein tear breakup sequence

Dry Eye Syndrome is a common disease in the western world, with effects from uncomfortable itchiness to permanent damage to the ocular surface. Almost 5 million Americans over 50 years old suffer from dry eye. A conservative estimate shows that approximately 17 million Americans have contact lens related dry eye -one of the main factors to contact lens discontinuation. In addition, the incidence of the disease is on the rise. Nevertheless, there is still no gold standard test that can reliably detect dry eye. One of the most commonly used tests by clinicians to detect dry eye is the Fluorescein Break Up Time (FBUT). However, results vary a lot between clinicians. Other tests such as observing the tear meniscus height are also performed regularly by the clinicians but not necessarily in conjunction with the FBUT test. Therefore there is a real need for a reliable, robust and operator-dependent method to evaluate dry eye. To our knowledge, no previous research has been conducted on automatic evaluation of dry eye in fluorescein images. In this thesis, we present new algorithms to automatically detect various dryness signs and make a number of original contributions. The first problem we address is how to detect the dry areas in fluorescein videos of the anterior of the eye, which are captured using a portable camera. We present a new multi-step algorithm which first locates the iris in each image in the video, then aligns the images according to the location of the iris and finally analyzes the aligned video to find the regions of dryness. We produce a novel segmentation result called dryness image, which depicts the various degrees of tear film thinning over the corneal surface. Then, we demonstrate through experiments that there is a large variation in the estimated Break Up Time (BUT) between clinicians and no ground-truth can be defined. To overcome that, we define a new value based on the clinical definitions of the BUT. These definitions are converted to image processing properties and an estimate of the BUT is computed using temporal analysis of the aligned video. We demonstrate that our new value is in the accepted range of the BUT values provided by the clinicians. We present an extension to the dryness algorithm, which is based on transforming the video to a volume by considering each video frame as a slice in a 3D volume. On a volume, a temporal monotonic constraint can be applied between pixels in consecutive slices. The constraint enforces the clinical definition of tear film thinning over time -the amount of fluid cannot increase while not blinking. The constraint is applied directly into the cost function and the whole volume is segmented simultaneously using graph-cuts. As a consequence, the approach is more robust and less sensitive to alignment errors. Finally, we generalize the idea and explain how monotonic constraints can be applied to other imaging modalities. In the last part of the thesis, we develop a new algorithm to evaluate the tear meniscus height and shape using graph-cuts. We formulate the segmentation problem using asymmetric cost functions and demonstrate its power and usefulness for the task. The asymmetry induces which directional moves are permitted in the minimization process and thus produces a result that adheres to the known shape properties of the tear meniscus. The iterative algorithm provides simultaneously the best segmentation result and shape prior of the meniscus

Predicting an unstable tear film through artificial intelligence

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Artificial intelligence in dry eye disease

Dry eye disease (DED) has a prevalence of between 5 and 50%, depending on the diagnostic criteria used and population under study. However, it remains one of the most underdiagnosed and undertreated conditions in ophthalmology. Many tests used in the diagnosis of DED rely on an experienced observer for image interpretation, which may be considered subjective and result in variation in diagnosis. Since artificial intelligence (AI) systems are capable of advanced problem solving, use of such techniques could lead to more objective diagnosis. Although the term ‘AI’ is commonly used, recent success in its applications to medicine is mainly due to advancements in the sub-field of machine learning, which has been used to automatically classify images and predict medical outcomes. Powerful machine learning techniques have been harnessed to understand nuances in patient data and medical images, aiming for consistent diagnosis and stratification of disease severity. This is the first literature review on the use of AI in DED. We provide a brief introduction to AI, report its current use in DED research and its potential for application in the clinic. Our review found that AI has been employed in a wide range of DED clinical tests and research applications, primarily for interpretation of interferometry, slit-lamp and meibography images. While initial results are promising, much work is still needed on model development, clinical testing and standardisation

An SPH study on viscoplastic surges overriding mobile beds: The many regimes of entrainment

Flow-type landslides entrain mobile bed material, but the processes involved are diverse and require systematic study. We perform direct numerical simulations using the open-source SPH package DualSPHysics with a regularized Herschel–Bulkley rheology. We then compare model output with physical test data, and hence investigate the effects of varying the bed yield stress $_{,b}$ and bed depth ℎ$_b$, interpreting the results using a newly-identified set of dimensionless numbers. Results reveal diverse interaction regimes between surges and mobile beds, including ‘‘rigid bed’’, ‘‘lubrication’’, ‘‘shallow ploughing’’, ‘‘surfing’’, ‘‘plunging’’, and ‘‘deep ploughing’’. Shallow, borderline-stable beds ‘‘lubricate’’ the surge: once destabilized, these beds cause strong acceleration of the combined flow front. Deeper borderline-stable beds allow the surge material to ‘‘plunge’’ downward, massively displacing bed material upward and downstream. For stabler beds, ‘‘ploughing’’ and ‘‘surfing’’ are associated with intermediate and high values of $_{,b}$, respectively. In both cases, beds retard the surge, with mobile dams forming for ‘‘ploughing’’ regimes. Across all regimes identified, the influence of $_{,b}$ is non-monotonic, with intermediate values decelerating the combined flow fronts the most. Furthermore, the different interaction regimes exhibit unique velocity profiles. We develop phase diagrams based on three dimensionless numbers, demarcating these regimes

Modelling and analysis of ophthalmic fluid dynamics

Mathematical models and numerical methods are developed for analysing and simulating the spatio-temporal evolution of the tear film coating the anterior surface of the human eye during an interblink period. The novelty of the work is on two distinct fronts. • First, a systematic approach is taken to ensure that the (coupled) model evolution equations — one each for film thickness and lipid-surfactant concentration — arising from asymptotic thin-film approximations of the Navier-Stokes equations, are uniformly valid when realistic ophthalmic data are used in the parameterisation. In this way, the present model does not — as occurs in related literature — yield results that are in conflict with a priori approximation hypotheses. More specifically, novel results are obtained on the effects of substrate curvature by proposing a specific coordinate system in which: the influence of curvilinearity on the evolution of the tear film can be parameterised, and; the limiting case recovers the Cartesian models of related literature. Additionally, the evolution equations are developed using sophisticated bespoke computer-algebra (MAPLE) techniques that permit the correct a priori scalings — of the competing effects of gravity, inertia, evaporation and surface tension — that guarantee the above-mentioned uniform validity. A novel consideration of the physical viability of boundary conditions at three-phase contact line on the eyelid in the existing mathematical literature leads to the proposal, implementation and investigation of novel Neumann boundary conditions that are supported by the results of recent in vitro experimental work. • Second, bespoke spectral numerical methods are developed for solving the thinfilm approximations, yielding hitherto-unseen explicit formulæfor high-order Chebyshev differentiation matrices. Inherent errors are quantified, thereby yielding an explicit understanding of both the modelling limitations and the plausibility of results. A suite of post-processing tools is developed to negotiate the complexities of implementing the novel boundary conditions in a spectral environment. All numerical techniques are validated on test problems; a high degree of both accuracy and efficiency is demonstrated. An analysis is presented of the errors incurred in the numerical approximation of the (steep) film-profile gradients near the eyelids; the results of this error analysis prompt questions on the accuracy of many of the results of previously published models. Through the combination of new, uniformly valid, thin-film approximations and bespoke, fully validated numerical methods, the coupled evolution equations for the thin-film thickness and lipid surfactant concentration are solved with confidence that the results obtained are credible. The novel boundary conditions lead to results that predict behaviours of the tear film that, whilst unseen in all prior related mathematical literature, encouragingly align with in vivo experimental observations in the ophthalmic literature. As a result, a novel hypothesis is presented for the behaviour of the tear-film contact line, through which predictions are made regarding the development and treatment of dry-eye pathologies. Suggestions for future work conclude the thesis

Phase Transitions in Binary and Ternary Vanadium Oxides: Implications for Thermochromic and Intercalation Batteries

The implications of climate change and resource scarcity stand to impose great strain on society, and require the development of novel ways to conserve and store energy. Due to the large portion of energy use accounted for the heating and cooling of buildings worldwide, thermochromic coatings offer promise for reducing energy footprints. Vanadium dioxide has long been a material of interest because of the intrinsitc metalinsulator transition wherein the material switches between a low-temperature, insulating, monoclinic phase (EB = 0.6 eV, infrared transparent) and a high temperature, metallic, tetragonal phase (infrared reflective). In this work we present the development of a scalable synthesis of extremely high quality vanadium dioxide nanoparticles, exhibiting superior a superior metal-insulator transition comparable to the highest quality films produced via molecular beam epitaxy. In the field of energy storage, high energy and power density batteries are imperative to help drive the electrification of the transportation industry as well as for grid-level storage. However, there are many issues with current state-of-the-art Li-ion battery technologies including high cost, poor high rate performance, and safety concerns that hinder large scale adoption. However, the mechanisms governing cation transport and phase formation in battery host materials are not well understood. The study of these mechanisms is further complicated by their non-equilibrium nature, requiring multiple modes of characterization that combine theoretical calculations with geometric and electronic structure determination across multiple length scales. In this work, we have used chemical lithiation as a Li-ion insertion tool to study the mechanism of lithium insertion and diffusion through substrate free nanowires and nanoplatelets of vanadium pentoxide. Specifically, we have examined the dependence of these properties on particle size. Systematic studies utilizing X-ray diffraction, Raman spectroscopy, X-ray absorption spectroscopy, and scanning transmission X-ray microscopy have allowed us to identify the formation of two-phase mixtures upon Li-ion insertion. Combining these measurements with first principles calculations has allowed us to determine the mechanistic origins of this phase separation, and suggest that the barrier to diffusion of Li-ions through layered vanadium pentoxide arises from the formation of a small polaron upon Li-ion insertion. Understanding the mechanisms by which lithium mobility through the layered vanadium pentoxide structure is impeded has allowed us to develop a novel polymorph of vanadium pentoxide that minimizes the strength of the polaronic confinement. By replacing the layered framework with one-dimensional tunnels that provide a more rigid framework and spread the localized electron, we have achieved facile, fast, single-phase lithium insertion and removal

Electrophysiologic assessment of (central) auditory processing disorder in children with non-syndromic cleft lip and/or palate

Session 5aPP - Psychological and Physiological Acoustics: Auditory Function, Mechanisms, and Models (Poster Session)Cleft of the lip and/or palate is a common congenital craniofacial malformation worldwide, particularly non-syndromic cleft lip and/or palate (NSCL/P). Though middle ear deficits in this population have been universally noted in numerous studies, other auditory problems including inner ear deficits or cortical dysfunction are rarely reported. A higher prevalence of educational problems has been noted in children with NSCL/P compared to craniofacially normal children. These high level cognitive difficulties cannot be entirely attributed to peripheral hearing loss. Recently it has been suggested that children with NSCLP may be more prone to abnormalities in the auditory cortex. The aim of the present study was to investigate whether school age children with (NSCL/P) have a higher prevalence of indications of (central) auditory processing disorder [(C)APD] compared to normal age matched controls when assessed using auditory event-related potential (ERP) techniques. School children (6 to 15 years) with NSCL/P and normal controls with matched age and gender were recruited. Auditory ERP recordings included auditory brainstem response and late event-related potentials, including the P1-N1-P2 complex and P300 waveforms. Initial findings from the present study are presented and their implications for further research in this area —and clinical intervention—are outlined. © 2012 Acoustical Society of Americapublished_or_final_versio

Remote Sensing of Savannas and Woodlands

Savannas and woodlands are one of the most challenging targets for remote sensing. This book provides a current snapshot of the geographical focus and application of the latest sensors and sensor combinations in savannas and woodlands. It includes feature articles on terrestrial laser scanning and on the application of remote sensing to characterization of vegetation dynamics in the Mato Grosso, Cerrado and Caatinga of Brazil. It also contains studies focussed on savannas in Europe, North America, Africa and Australia. It should be important reading for environmental practitioners and scientists globally who are concerned with the sustainability of the global savanna and woodland biome

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