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

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

Retinal status analysis method based on feature extraction and quantitative grading in OCT images

Background: Optical coherence tomography (OCT) is widely used in ophthalmology for viewing the morphology of the retina, which is important for disease detection and assessing therapeutic effect. The diagnosis of retinal diseases is based primarily on the subjective analysis of OCT images by trained ophthalmologists. This paper describes an OCT images automatic analysis method for computer-aided disease diagnosis and it is a critical part of the eye fundus diagnosis. Methods: This study analyzed 300 OCT images acquired by Optovue Avanti RTVue XR (Optovue Corp., Fremont, CA). Firstly, the normal retinal reference model based on retinal boundaries was presented. Subsequently, two kinds of quantitative methods based on geometric features and morphological features were proposed. This paper put forward a retinal abnormal grading decision-making method which was used in actual analysis and evaluation of multiple OCT images. Results: This paper showed detailed analysis process by four retinal OCT images with different abnormal degrees. The final grading results verified that the analysis method can distinguish abnormal severity and lesion regions. This paper presented the simulation of the 150 test images, where the results of analysis of retinal status showed that the sensitivity was 0.94 and specificity was 0.92.The proposed method can speed up diagnostic process and objectively evaluate the retinal status. Conclusions: This paper aims on studies of retinal status automatic analysis method based on feature extraction and quantitative grading in OCT images. The proposed method can obtain the parameters and the features that are associated with retinal morphology. Quantitative analysis and evaluation of these features are combined with reference model which can realize the target image abnormal judgment and provide a reference for disease diagnosi

Study and Analysis of Fluid Filled Abnormalities in Retina Using OCT Images

Visual impairment is one of the most regularly happening infections in human. The reason being variation from the normal in the different layers of retina because of strange measure of liquid either abundance aggregation or shortage. This paper targets recognizing and assessing the different abnormalities that could be earlier stages to visual deficiency. The proposed target is achieved by means of implementation using Digital Image Processing Technique, starting from preprocessing to classification at various stages. Not restricting to binary classification as normal or abnormal, the proposed system also extends its capacity to classify the input image as Cystoid Macular Edema (CME), Choroidal Neo Vascular Membrane (CNVM), Macular Hole (MH) and normal images. The preprocessing methodology implemented filters to remove the speckle noises which are most common in ultrasound-based imaging system. Random forest classifier was utilized for classifying the input features and also seems to be promising on par with the various existing methodologies

Methods for automated analysis of macular OCT data

Optical coherence tomography (OCT) is fast becoming one of the most important modalities for imaging the eye. It provides high resolution, cross-sectional images of the retina in three dimensions, distinctly showing its many layers. These layers are critical for normal eye function, and vision loss may occur when they are altered by disease. Specifically, the thickness of individual layers can change over time, thereby making the ability to accurately measure these thicknesses an important part of learning about how different diseases affect the eye. Since manual segmentation of the layers in OCT data is time consuming and tedious, automated methods are necessary to extract layer thicknesses. While a standard set of tools exist on the scanners to automatically segment the retina, the output is often limited, providing measurements restricted to only a few layers. Analysis of longitudinal data is also limited, with scans from the same subject often processed independently and registered using only a single landmark at the fovea. Quantification of other changes in the retina, including the accumulation of fluid, are also generally unavailable using the built-in software. In this thesis, we present four contributions for automatically processing OCT data, specifically for data acquired from the macular region of the retina. First, we present a layer segmentation algorithm to robustly segment the eight visible layers of the retina. Our approach combines the use of a random forest (RF) classifier, which produces boundary probabilities, with a boundary refinement algorithm to find surfaces maximizing the RF probabilities. Second, we present a pair of methods for processing longitudinal data from individual subjects: one combining registration and motion correction, and one for simultaneously segmenting the layers across all scans. Third, we develop a method for segmentation of microcystic macular edema, which appear as small, fluid-filled, cystoid spaces within the retina. Our approach again uses an RF classifier to produce a robust segmentation. Finally, we present the development of macular flatspace (MFS), a computational domain used to put data from different subjects in a common coordinate system where each layer appears flat, thereby simplifying any automated processing. We present two applications of MFS: inhomogeneity correction to normalize the intensities within each layer, and layer segmentation by adapting and simplifying a graph formulation used previously

Retinal Fundus Image Analysis for Diagnosis of Glaucoma: A Comprehensive Survey

© 2016 IEEE. The rapid development of digital imaging and computer vision has increased the potential of using the image processing technologies in ophthalmology. Image processing systems are used in standard clinical practices with the development of medical diagnostic systems. The retinal images provide vital information about the health of the sensory part of the visual system. Retinal diseases, such as glaucoma, diabetic retinopathy, age-related macular degeneration, Stargardt's disease, and retinopathy of prematurity, can lead to blindness manifest as artifacts in the retinal image. An automated system can be used for offering standardized large-scale screening at a lower cost, which may reduce human errors, provide services to remote areas, as well as free from observer bias and fatigue. Treatment for retinal diseases is available; the challenge lies in finding a cost-effective approach with high sensitivity and specificity that can be applied to large populations in a timely manner to identify those who are at risk at the early stages of the disease. The progress of the glaucoma disease is very often quiet in the early stages. The number of people affected has been increasing and patients are seldom aware of the disease, which can cause delay in the treatment. A review of how computer-aided approaches may be applied in the diagnosis and staging of glaucoma is discussed here. The current status of the computer technology is reviewed, covering localization and segmentation of the optic nerve head, pixel level glaucomatic changes, diagonosis using 3-D data sets, and artificial neural networks for detecting the progression of the glaucoma disease

3D Segmentation & Measurement of Macular Holes

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