228 research outputs found

    3D Segmentation & Measurement of Macular Holes

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    Macular holes are blinding conditions where a hole develops in the central part of retina, resulting in reduced central vision. The prognosis and treatment options are related to a number of variables including the macular hole size and shape. In this work we introduce a method to segment and measure macular holes in three-dimensional (3D) data. High-resolution spectral domain optical coherence tomography (SD-OCT) allows precise imaging of the macular hole geometry in three dimensions, but the measurement of these by human observers is time consuming and prone to high inter- and intra-observer variability, being characteristically measured in 2D rather than 3D. This work introduces several novel techniques to automatically retrieve accurate 3D measurements of the macular hole, including surface area, base area, base diameter, top area, top diameter, height, and minimum diameter. Specifically, it is introducing a multi-scale 3D level set segmentation approach based on a state-of-the-art level set method, and introducing novel curvature-based cutting and 3D measurement procedures. The algorithm is fully automatic, and we validate the extracted measurements both qualitatively and quantitatively, where the results show the method to be robust across a variety of scenarios. A segmentation software package is presented for targeting medical and biological applications, with a high level of visual feedback and several usability enhancements over existing packages. Specifically, it is providing a substantially faster graphics processing unit (GPU) implementation of the local Gaussian distribution fitting (LGDF) energy model, which can segment inhomogeneous objects with poorly defined boundaries as often encountered in biomedical images. It also provides interactive brushes to guide the segmentation process in a semi-automated framework. The speed of implementation allows us to visualise the active surface in real-time with a built-in ray tracer, where users may halt evolution at any timestep to correct implausible segmentation by painting new blocking regions or new seeds. Quantitative and qualitative validation is presented, demonstrating the practical efficacy of the interactive elements for a variety of real-world datasets. The size of macular holes is known to be one of the strongest predictors of surgical success both anatomically and functionally. Furthermore, it is used to guide the choice of treatment, the optimum surgical approach and to predict outcome. Our automated 3D image segmentation algorithm has extracted 3D shape-based macular hole measurements and described the dimensions and morphology. Our approach is able to robustly and accurately measure macular hole dimensions. This thesis is considered as a significant contribution for clinical applications particularly in the field of macular hole segmentation and shape analysis

    Macular hole morphology and measurement using an automated three dimensional image segmentation algorithm

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    Objective: Full-thickness macular holes (MH) are classified principally by size, which is one of the strongest predictors of anatomical and visual success. Using a three-dimensional (3D) automated image processing algorithm, we analysed optical coherence tomography (OCT) images of 104 MH of patients, comparing MH dimensions and morphology with clinician-acquired two-dimensional measurements. Methods and Analysis: All patients underwent a high-density central horizontal scanning OCT protocol. Two independent clinicians measured the minimum linear diameter (MLD) and maximum base diameter. OCT images were also analysed using an automated 3D segmentation algorithm which produced key parameters including the respective maximum and minimum diameter of the minimum area (MA) of the MH, as well as volume and surface area. Results: Using the algorithm-derived values, MH were found to have significant asymmetry in all dimensions. The minima of the MA were typically approximately 90° to the horizontal, and differed from their maxima by 55 μm. The minima of the MA differed from the human-measured MLD by a mean of nearly 50 μm, with significant interobserver variability. The resultant differences led to reclassification using the International Vitreomacular Traction Study Group classification in a quarter of the patients (p=0.07). Conclusion: MH are complex shapes with significant asymmetry in all dimensions. We have shown how 3D automated analysis of MH describes their dimensions more accurately and repeatably than human assessment. This could be used in future studies investigating hole progression and outcome to help guide optimum treatments

    Robust 3D U-Net Segmentation of Macular Holes

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    Macular holes are a common eye condition which result in visual impairment. We look at the application of deep convolutional neural networks to the problem of macular hole segmentation. We use the 3D U-Net architecture as a basis and experiment with a number of design variants. Manually annotating and measuring macular holes is time consuming and error prone. Previous automated approaches to macular hole segmentation take minutes to segment a single 3D scan. Our proposed model generates significantly more accurate segmentations in less than a second. We found that an approach of architectural simplification, by greatly simplifying the network capacity and depth, exceeds both expert performance and state-of-the-art models such as residual 3D U-Nets

    A Parametric Model for the Analysis and Quantification of Foveal Shapes

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    Recently, the advance of OCT enables a detailed examination of the human retina in-vivo for clinical routine and experimental eye research. One of the structures inside the retina of immense scientific interest is the fovea, a small retinal pit located in the central region with extraordinary visual resolution. Today, only a few investigations captured foveal morphology based on a large subject group by a detailed analysis employing mathematical models. In this work, we develop a parametric model function to describe the shape of the human fovea. Starting with a detailed discussion on the history and present of fovea research, we define the requirements for a suitable model and derive a function which can represent a broad range of foveal shapes. The model is one-dimensional in its basic form and can only account for the shape of one particular section through a fovea. Therefore, we apply a radial fitting scheme in different directions which can capture a fovea in its full three-dimensional appearance. Highly relevant foveal characteristics, derived from the model, provide valuable descriptions to quantify the fovea and allow for a detailed analysis of different foveal shapes. To put the theoretical model into practice, we develop a numerical scheme to compute model parameters from retinal \ac{oct} scans and to reconstruct the shape of an entire fovea. For the sake of scientific reproducibility, this section includes implementation details, examples and a discussion of performance considerations. Finally, we present several studies which employed the fovea model successfully. A first feasibility study verifies that the parametric model is suitable for foveal shapes occurring in a large set of healthy human eyes. In a follow-up investigation, we analyse foveal characteristics occurring in healthy humans in detail. This analysis will concern with different aspects including, e.g. an investigation of the fovea's asymmetry, a gender comparison, a left versus right eye correlation and the computation of subjects with extreme foveal shapes. Furthermore, we will show how the model was used to support investigations unrelated to the direct quantification of the fovea itself. In these investigations we employed the model to compute anatomically correct regions of interest in an analysis of the OCB and the calculation of an average fovea for an optical simulation of light rays. We will conclude with currently unpublished data that shows the fovea modelling of hunting birds which have unusual, funnel-like foveal shapes

    NON-INVASIVE IMAGE ENHANCEMENT OF COLOUR RETINAL FUNDUS IMAGES FOR A COMPUTERISED DIABETIC RETINOPATHY MONITORING AND GRADING SYSTEM

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    Diabetic Retinopathy (DR) is a sight threatening complication due to diabetes mellitus affecting the retina. The pathologies of DR can be monitored by analysing colour fundus images. However, the low and varied contrast between retinal vessels and the background in colour fundus images remains an impediment to visual analysis in particular in analysing tiny retinal vessels and capillary networks. To circumvent this problem, fundus fluorescein angiography (FF A) that improves the image contrast is used. Unfortunately, it is an invasive procedure (injection of contrast dyes) that leads to other physiological problems and in the worst case may cause death. The objective of this research is to develop a non-invasive digital Image enhancement scheme that can overcome the problem of the varied and low contrast colour fundus images in order that the contrast produced is comparable to the invasive fluorescein method, and without introducing noise or artefacts. The developed image enhancement algorithm (called RETICA) is incorporated into a newly developed computerised DR system (called RETINO) that is capable to monitor and grade DR severity using colour fundus images. RETINO grades DR severity into five stages, namely No DR, Mild Non Proliferative DR (NPDR), Moderate NPDR, Severe NPDR and Proliferative DR (PDR) by enhancing the quality of digital colour fundus image using RETICA in the macular region and analysing the enlargement of the foveal avascular zone (F AZ), a region devoid of retinal vessels in the macular region. The importance of this research is to improve image quality in order to increase the accuracy, sensitivity and specificity of DR diagnosis, and to enable DR grading through either direct observation or computer assisted diagnosis system

    Adaptive optics: principles and applications in ophthalmology

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    This is a comprehensive review of the principles and applications of adaptive optics (AO) in ophthalmology. It has been combined with flood illumination ophthalmoscopy, scanning laser ophthalmoscopy, as well as optical coherence tomography to image photoreceptors, retinal pigment epithelium (RPE), retinal ganglion cells, lamina cribrosa and the retinal vasculature. In this review, we highlight the clinical studies that have utilised AO to understand disease mechanisms. However, there are some limitations to using AO in a clinical setting including the cost of running an AO imaging service, the time needed to scan patients, the lack of normative databases and the very small size of area imaged. However, it is undoubtedly an exceptional research tool that enables visualisation of the retina at a cellular level

    Automatic detection of drusen associated with age-related macular degeneration in optical coherence tomography: a graph-based approach

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    Tese de Doutoramento em Líderes para Indústrias TecnológicasThe age-related macular degeneration (AMD) starts to manifest itself with the appearance of drusen. Progressively, the drusen increase in size and in number without causing alterations to vision. Nonetheless, their quantification is important because it correlates with the evolution of the disease to an advanced stage, which could lead to the loss of central vision. Manual quantification of drusen is impractical, since it is time-consuming and it requires specialized knowledge. Therefore, this work proposes a method for quantifying drusen automatically In this work, it is proposed a method for segmenting boundaries limiting drusen and another method for locating them through classification. The segmentation method is based on a multiple surface framework that is adapted for segmenting the limiting boundaries of drusen: the inner boundary of the retinal pigment epithelium + drusen complex (IRPEDC) and the Bruch’s membrane (BM). Several segmentation methods have been considerably successful in segmenting layers of healthy retinas in optical coherence tomography (OCT) images. These methods were successful because they incorporate prior information and regularization. However, these factors have the side-effect of hindering the segmentation in regions of altered morphology that often occur in diseased retinas. The proposed segmentation method takes into account the presence of lesion related with AMD, i.e., drusen and geographic atrophies (GAs). For that, it is proposed a segmentation scheme that excludes prior information and regularization that is only valid for healthy regions. Even with this segmentation scheme, the prior information and regularization can still cause the oversmoothing of some drusen. To address this problem, it is also proposed the integration of local shape priors in the form of a sparse high order potentials (SHOPs) into the multiple surface framework. Drusen are commonly detected by thresholding the distance among the boundaries that limit drusen. This approach misses drusen or portions of drusen with a height below the threshold. To improve the detection of drusen, Dufour et al. [1] proposed a classification method that detects drusen using textural information. In this work, the method of Dufour et al. [1] is extended by adding new features and performing multi-label classification, which allow the individual detection of drusen when these occur in clusters. Furthermore, local information is incorporated into the classification by combining the classifier with a hidden Markov model (HMM). Both the segmentation and detections methods were evaluated in a database of patients with intermediate AMD. The results suggest that both methods frequently perform better than some methods present in the literature. Furthermore, the results of these two methods form drusen delimitations that are closer to expert delimitations than two methods of the literature.A degenerescência macular relacionada com a idade (DMRI) começa a manifestar-se com o aparecimento de drusas. Progressivamente, as drusas aumentam em tamanho e em número sem causar alterações à visão. Porém, a sua quantificação é importante porque está correlacionada com a evolução da doença para um estado avançado, levar à perda de visão central. A quantificação manual de drusas é impraticável, já que é demorada e requer conhecimento especializado. Por isso, neste trabalho é proposto um método para segmentar drusas automaticamente. Neste trabalho, é proposto um método para segmentar as fronteiras que limitam as drusas e outro método para as localizar através de classificação. O método de segmentação é baseado numa ”framework” de múltiplas superfícies que é adaptada para segmentar as fronteiras que limitam as drusas: a fronteira interior do epitélio pigmentar + complexo de drusas e a membrana de Bruch. Vários métodos de segmentação foram consideravelmente bem-sucedidos a segmentar camadas de retinas saudáveis em imagens de tomografia de coerência ótica. Estes métodos foram bem-sucedidos porque incorporaram informação prévia e regularização. Contudo, estes fatores têm como efeito secundário dificultar a segmentação em regiões onde a morfologia da retina está alterada devido a doenças. O método de segmentação proposto toma em consideração a presença de lesões relacionadas com DMRI, .i.e., drusas e atrofia geográficas. Para isso, é proposto um esquema de segmentação que exclui informação prévia e regularização que são válidas apenas em regiões saudáveis da retina. Mesmo com este esquema de segmentação, a informação prévia e a regularização podem causar a suavização excessiva de algumas drusas. Para tentar resolver este problema, também é proposta a integração de informação prévia local sob a forma de potenciais esparsos de ordem elevada na ”framework” multi-superfície. As drusas são usalmente detetadas por ”thresholding” da distância entre as fronteiras que limitam as drusas. Esta abordagem falha drusas ou porções de drusas abaixo do ”threshold”. Para melhorar a deteção de drusas, Dufour et al. [1] propuseram um método de classificação que deteta drusas usando informação de texturas. Neste trabalho, o método de Dufour et al. [1] é estendido, adicionando novas características e realizando uma classificação com múltiplas classes, o que permite a deteção individual de drusas em aglomerados. Além disso, é incorporada informação local na classificação, combinando o classificador com um modelo oculto de Markov. Ambos os métodos de segmentação e deteção foram avaliados numa base de dados de pacientes com DMRI intermédia. Os resultados sugerem que ambos os métodos obtêm frequentemente melhores resultados que alguns métodos descritos na literatura. Para além disso, os resultados destes dois métodos formam delimitações de drusas que estão mais próximas das delimitações dos especialistas que dois métodos da literatura.This work was supported by FCT with the reference project UID/EEA/04436/2013, by FEDER funds through the COMPETE 2020 – Programa Operacional Competitividade e Internacionalização (POCI) with the reference project POCI-01-0145-FEDER-006941. Furthermore, the Portuguese funding institution Fundação Calouste Gulbenkian has conceded me a Ph.D. grant for this work. For that, I wish to acknowledge this institution. Additionally, I want to thank one of its members, Teresa Burnay, for all her assistance with issues related with the grant, for believing that my work was worth supporting and for encouraging me to apply for the grant

    Automated retinal layer segmentation and pre-apoptotic monitoring for three-dimensional optical coherence tomography

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    The aim of this PhD thesis was to develop segmentation algorithm adapted and optimized to retinal OCT data that will provide objective 3D layer thickness which might be used to improve diagnosis and monitoring of retinal pathologies. Additionally, a 3D stack registration method was produced by modifying an existing algorithm. A related project was to develop a pre-apoptotic retinal monitoring based on the changes in texture parameters of the OCT scans in order to enable treatment before the changes become irreversible; apoptosis refers to the programmed cell death that can occur in retinal tissue and lead to blindness. These issues can be critical for the examination of tissues within the central nervous system. A novel statistical model for segmentation has been created and successfully applied to a large data set. A broad range of future research possibilities into advanced pathologies has been created by the results obtained. A separate model has been created for choroid segmentation located deep in retina, as the appearance of choroid is very different from the top retinal layers. Choroid thickness and structure is an important index of various pathologies (diabetes etc.). As part of the pre-apoptotic monitoring project it was shown that an increase in proportion of apoptotic cells in vitro can be accurately quantified. Moreover, the data obtained indicates a similar increase in neuronal scatter in retinal explants following axotomy (removal of retinas from the eye), suggesting that UHR-OCT can be a novel non-invasive technique for the in vivo assessment of neuronal health. Additionally, an independent project within the computer science department in collaboration with the school of psychology has been successfully carried out, improving analysis of facial dynamics and behaviour transfer between individuals. Also, important improvements to a general signal processing algorithm, dynamic time warping (DTW), have been made, allowing potential application in a broad signal processing field.EThOS - Electronic Theses Online ServiceGBUnited Kingdo
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