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

Automatic Segmentation of the Retinal Nerve Fiber Layer by Means of Mathematical Morphology and Deformable Models in 2D Optical Coherence Tomography Imaging

[EN] Glaucoma is a neurodegenerative disease process that leads to progressive damage of the optic nerve to produce visual impairment and blindness. Spectral-domain OCT technology enables peripapillary circular scans of the retina and the measurement of the thickness of the retinal nerve fiber layer (RNFL) for the assessment of the disease status or progression in glaucoma patients. This paper describes a new approach to segment and measure the retinal nerve fiber layer in peripapillary OCT images. The proposed method consists of two stages. In the first one, morphological operators robustly detect the coarse location of the layer boundaries, despite the speckle noise and diverse artifacts in the OCT image. In the second stage, deformable models are initialized with the results of the previous stage to perform a fine segmentation of the boundaries, providing an accurate measurement of the entire RNFL. The results of the RNFL segmentation were qualitatively assessed by ophthalmologists, and the measurements of the thickness of the RNFL were quantitatively compared with those provided by the OCT inbuilt software as well as the state-of-the-art methods.This work was partially funded by Spanish National projects AES2017-PI17/00771 and AES2017-PI17/00821 (Instituto de Salud Carlos III), PID2019-105142RB-C21 (AI4SKIN) (Spanish Ministry of Economy and Competitiveness), PTA2017-14610-I (State Research Spanish Agency), regional project 20901/PI/18 (Fundacion Seneca) and Polytechnic University of Valencia (PAID-01-20).Berenguer-Vidal, R.; Verdú-Monedero, R.; Morales-Sánchez, J.; Sellés-Navarro, I.; Del Amor, R.; García-Pardo, JG.; Naranjo Ornedo, V. (2021). Automatic Segmentation of the Retinal Nerve Fiber Layer by Means of Mathematical Morphology and Deformable Models in 2D Optical Coherence Tomography Imaging. Sensors. 21(23):1-30. https://doi.org/10.3390/s21238027S130212

Clinical Applications of Artificial Intelligence in Glaucoma

Ophthalmology is one of the major imaging-intensive fields of medicine and thus has potential for extensive applications of artificial intelligence (AI) to advance diagnosis, drug efficacy, and other treatment-related aspects of ocular disease. AI has made impressive progress in ophthalmology within the past few years and two autonomous AIenabled systems have received US regulatory approvals for autonomously screening for mid-level or advanced diabetic retinopathy and macular edema. While no autonomous AI-enabled system for glaucoma screening has yet received US regulatory approval, numerous assistive AI-enabled software tools are already employed in commercialized instruments for quantifying retinal images and visual fields to augment glaucoma research and clinical practice. In this literature review (non-systematic), we provide an overview of AI applications in glaucoma, and highlight some limitations and considerations for AI integration and adoption into clinical practice

Surface Denoising based on The Variation of Normals and Retinal Shape Analysis

Through the development of this thesis, starting from the curvature tensor, we have been able to understand the variation of tangent vectors to define a shape analysis operator and also a relationship between the classical shape operator and the curvature tensor on a triangular surface. In continuation, the first part of the thesis analyzed the variation of surface normals and introduced a shape analysis operator, which is further used for mesh and point set denoising. In the second part of the thesis, mathematical modeling and shape quantification algorithms are introduced for retinal shape analysis. In the first half, this thesis followed the concept of the variation of surface normals, which is termed as the normal voting tensor and derived a relation between the shape operator and the normal voting tensor. The concept of the directional and the mean curvatures is extended on the dual representation of a triangulated surface. A normal voting tensor is defined on each triangle of a geometry and termed as the element-based normal voting tensor (ENVT). Later, a deformation tensor is extracted from the ENVT and it consists of the anisotropy of a surface and the mean curvature vector is defined based on the ENVT deformation tensor. The ENVT-based mesh denoising algorithm is introduced, where the ENVT is used as a shape operator. A binary optimization technique is applied on the spectral components of the ENVT that helps the algorithm to retain sharp features in the concerned geometry and improves the convergence rate of the algorithm. Later, a stochastic analysis of the effect of noise on the triangular mesh based on the minimum edge length of the elements in the geometry is explained. It gives an upper bound to the noise standard deviation to have minimum probability for flipped element normals. The ENVT-based mesh denoising concept is extended for a point set denoising, where noisy vertex normals are filtered using the vertex-based NVT and the binary optimization. For vertex update stage in point set denoising, we added different constraints to the quadratic error metric based on features (edges and corners) or non-feature (planar) points. This thesis also investigated a robust statistics framework for face normal bilateral filtering and proposed a robust and high fidelity two-stage mesh denoising method using Tukey’s bi-weight function as a robust estimator, which stops the diffusion at sharp features and produces smooth umbilical regions. This algorithm introduced a novel vertex update scheme, which uses a differential coordinate-based Laplace operator along with an edge-face normal orthogonality constraint to produce a high-quality mesh without face normal flips and it also makes the algorithm more robust against high-intensity noise. The second half of thesis focused on the application of the proposed geometric processing algorithms on the OCT (optical coherence tomography) scan data for quantification of the human retinal shape. The retina is a part of the central nervous system and comprises a similar cellular composition as the brain. Therefore, many neurological disorders affect the retinal shape and these neuroinflammatory conditions are known to cause modifications to two important regions of the retina: the fovea and the optical nerve head (ONH). This thesis consists of an accurate and robust shape modeling of these regions to diagnose several neurological disorders by detecting the shape changes. For the fovea, a parametric modeling algorithm is introduced using Cubic Bezier and this algorithm derives several 3D shape parameters, which quantify the foveal shape with high accuracy. For the ONH, a 3D shape analysis algorithm is introduced to measure the shape variation regarding different neurological disorders. The proposed algorithm uses triangulated manifold surfaces of two different layers of the retina to derive several 3D shape parameters. The experimental results of the fovea and the ONH morphometry confirmed that these algorithms can provide an aid to diagnose several neurological disorders

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

Retinal Vessels Segmentation Techniques and Algorithms: A Survey

Retinal vessels identification and localization aim to separate the different retinal vasculature structure tissues, either wide or narrow ones, from the fundus image background and other retinal anatomical structures such as optic disc, macula, and abnormal lesions. Retinal vessels identification studies are attracting more and more attention in recent years due to non-invasive fundus imaging and the crucial information contained in vasculature structure which is helpful for the detection and diagnosis of a variety of retinal pathologies included but not limited to: Diabetic Retinopathy (DR), glaucoma, hypertension, and Age-related Macular Degeneration (AMD). With the development of almost two decades, the innovative approaches applying computer-aided techniques for segmenting retinal vessels are becoming more and more crucial and coming closer to routine clinical applications. The purpose of this paper is to provide a comprehensive overview for retinal vessels segmentation techniques. Firstly, a brief introduction to retinal fundus photography and imaging modalities of retinal images is given. Then, the preprocessing operations and the state of the art methods of retinal vessels identification are introduced. Moreover, the evaluation and validation of the results of retinal vessels segmentation are discussed. Finally, an objective assessment is presented and future developments and trends are addressed for retinal vessels identification techniques.https://doi.org/10.3390/app802015

A deep learning model to assess and enhance eye fundus image quality

Engineering aims to design, build, and implement solutions that will increase and/or improve the life quality of human beings. Likewise, from medicine, solutions are generated for the same purposes, enabling these two knowledge areas to converge for a common goal. With the thesis work “A Deep Learning Model to Assess and Enhance Eye Fundus Image Quality", a model was proposed and implement a model that allows us to evaluate and enhance the quality of fundus images, which contributes to improving the efficiency and effectiveness of a subsequent diagnosis based on these images. On the one hand, for the evaluation of these images, a model based on a lightweight convolutional neural network architecture was developed, termed as Mobile Fundus Quality Network (MFQ-Net). This model has approximately 90% fewer parameters than those of the latest generation. For its evaluation, the Kaggle public data set was used with two sets of quality annotations, binary (good and bad) and three classes (good, usable and bad) obtaining an accuracy of 0.911 and 0.856 in the binary mode and three classes respectively in the classification of the fundus image quality. On the other hand, a method was developed for eye fundus quality enhancement termed as Pix2Pix Fundus Oculi Quality Enhancement (P2P-FOQE). This method is based on three stages which are; pre-enhancement: for color adjustment, enhancement: with a Pix2Pix network (which is a Conditional Generative Adversarial Network) as the core of the method and post-enhancement: which is a CLAHE adjustment for contrast and detail enhancement. This method was evaluated on a subset of quality annotations for the Kaggle public database which was re-classified for three categories (good, usable, and poor) by a specialist from the Fundación Oftalmolóica Nacional. With this method, the quality of these images for the good class was improved by 72.33%. Likewise, the image quality improved from the bad class to the usable class, and from the bad class to the good class by 56.21% and 29.49% respectively.La ingeniería busca diseñar, construir e implementar soluciones que permitan aumentar y/o mejorar la calidad de vida de los seres humanos. Igualmente, desde la medicina son generadas soluciones con los mismos fines, posibilitando que estas dos áreas del conocimiento convergan por un bien común. Con el trabajo de tesis “A Deep Learning Model to Assess and Enhance Eye Fundus Image Quality”, se propuso e implementó un modelo que permite evaluar y mejorar la calidad de las imágenes de fondo de ojo, lo cual contribuye a mejorar la eficiencia y eficacia de un posterior diagnóstico basado en estas imágenes. Para la evaluación de estás imágenes, se desarrolló un modelo basado en una arquitectura de red neuronal convolucional ligera, la cual fue llamada Mobile Fundus Quality Network (MFQ-Net). Este modelo posee aproximadamente 90% menos parámetros que aquellos de última generación. Para su evaluación se utilizó el conjunto de datos públicos de Kaggle con dos sets de anotaciones de calidad, binario (buena y mala) y tres clases (buena, usable y mala) obteniendo en la tareas de clasificación de la calidad de la imagen de fondo de ojo una exactitud de 0.911 y 0.856 en la modalidad binaria y tres clases respectivamente. Por otra parte, se desarrolló un método el cual realiza una mejora de la calidad de imágenes de fondo de ojo llamado Pix2Pix Fundus Oculi Quality Enhacement (P2P-FOQE). Este método está basado en tres etapas las cuales son; premejora: para ajuste de color, mejora: con una red Pix2Pix (la cual es una Conditional Generative Adversarial Network) como núcleo del método y postmejora: la cual es un ajuste CLAHE para contraste y realce de detalles. Este método fue evaluado en un subconjunto de anotaciones de calidad para la base de datos pública de Kaggle el cual fue re clasificado por un especialista de la Fundación Oftalmológica Nacional para tres categorías (buena, usable y mala). Con este método fue mejorada la calidad de estas imágenes para la clase buena en un 72,33%. Así mismo, la calidad de imagen mejoró de la clase mala a la clase utilizable, y de la clase mala a clase buena en 56.21% y 29.49% respectivamente.Línea de investigación: Visión por computadora para análisis de imágenes médicasMaestrí