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

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

Real-Time Automatic Segmentation of Optical Coherence Tomography Volume Data of the Macular Region.

Optical coherence tomography (OCT) is a high speed, high resolution and non-invasive imaging modality that enables the capturing of the 3D structure of the retina. The fast and automatic analysis of 3D volume OCT data is crucial taking into account the increased amount of patient-specific 3D imaging data. In this work, we have developed an automatic algorithm, OCTRIMA 3D (OCT Retinal IMage Analysis 3D), that could segment OCT volume data in the macular region fast and accurately. The proposed method is implemented using the shortest-path based graph search, which detects the retinal boundaries by searching the shortest-path between two end nodes using Dijkstra's algorithm. Additional techniques, such as inter-frame flattening, inter-frame search region refinement, masking and biasing were introduced to exploit the spatial dependency between adjacent frames for the reduction of the processing time. Our segmentation algorithm was evaluated by comparing with the manual labelings and three state of the art graph-based segmentation methods. The processing time for the whole OCT volume of 496x644x51 voxels (captured by Spectralis SD-OCT) was 26.15 seconds which is at least a 2-8-fold increase in speed compared to other, similar reference algorithms used in the comparisons. The average unsigned error was about 1 pixel ( approximately 4 microns), which was also lower compared to the reference algorithms. We believe that OCTRIMA 3D is a leap forward towards achieving reliable, real-time analysis of 3D OCT retinal data

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

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

Quantitative traits related to primary open angle glaucoma in the Scottish population isolate of Orkney

The aetiology and pathogenesis of primary open angle glaucoma (POAG), the second most common cause of irreversible visual loss in the United Kingdom, remains a conundrum for contemporary ophthalmology. Evidence suggests that glaucoma is a complex disorder, where multiple genes interact with each other and with factors in the environment. However, the aetiological heterogeneity of glaucoma coupled with its varied clinical presentation and course has made the study of glaucoma genes problematic. We established the Orcades Eye Study, a cross sectional family based genetic study, to explore the inheritance of primary open angle glaucoma (POAG). As POAG is a disease of late onset and low prevalence, rather than study disease per se we chose to study quantitative traits (QTs) related to POAG, in an isolated population in the northern Scottish archipelago of Orkney. A number of factors in this population, including reduced genetic heterogeneity and more homogenous environmental effects, confer certain advantages over more admixed urban populations in complex disease gene mapping. Preliminary analysis of the procured quantitative trait data (n=256) has demonstrated that the values obtained for the POAG related QTs of intraocular pressure (IOP), central corneal thickness and a number of optic disc parameters including optic cup area, disc area, retinal nerve fiber thickness, vertical cup to disc ratio and peripapillary atrophy are not dissimilar to other published White Caucasian populations. We also found that intraocular pressure shows an increase with age and is influenced by central corneal thickness but found no relationship between IOP and gender or IOP and other ocular biometric variables including optic nerve head parameters and refractive components. Neither central corneal thickness nor optic nerve head parameters had a statistically significant relationship to age, gender or other tested ocular biometric parameters. These findings are clinically important as these factors should be taken into consideration when evaluating intraocular pressure and other ocular biometric traits in the investigation of glaucoma and other ocular diseases in the population of Orkney. Data collection is ongoing, and with time, an increased sample size and a meaningful genetic analysis, the Orcades Eye Study will hopefully identify genes and regions of the genome associated with primary open angle glaucoma susceptibility in the Scottish Population Isolate of Orkney. To our knowledge, the only other population based study which has investigated as large a number of glaucoma related QTs is the Beijing Eye Study

Ocular rigidity : a previously unexplored risk factor in the pathophysiology of open-angle glaucoma : assessment using a novel OCT-based measurement method

Le glaucome est la première cause de cécité irréversible dans le monde. Bien que sa pathogenèse demeure encore nébuleuse, les propriétés biomécaniques de l’oeil sembleraient jouer un rôle important dans le développement et la progression de cette maladie. Il est stipulé que la rigidité oculaire (RO) est altérée au travers les divers stades de la maladie et qu’elle serait le facteur le plus influent sur la réponse du nerf optique aux variations de la pression intraoculaire (PIO) au sein du glaucome. Pour permettre l’investigation du rôle de la RO dans le glaucome primaire à angle ouvert (GPAO), la capacité de quantifier la RO in vivo par l’entremise d’une méthode fiable et non-invasive est essentielle. Une telle méthode n’est disponible que depuis 2015. Basée sur l'équation de Friedenwald, cette approche combine l'imagerie par tomographie par cohérence optique (TCO) et la segmentation choroïdienne automatisée afin de mesurer le changement de volume choroïdien pulsatile (ΔV), ainsi que la tonométrie dynamique de contour Pascal pour mesurer le changement de pression pulsatile correspondant. L’objectif de cette thèse est d’évaluer la validité de cette méthode, et d’en faire usage afin d’investiguer le rôle de la RO dans les maladies oculaires, particulièrement le GPAO. Plus spécifiquement, cette thèse vise à : 1) améliorer la méthode proposée et évaluer sa validité ainsi que sa répétabilité, 2) investiguer l’association entre la RO et le dommage neuro-rétinien chez les patients glaucomateux, et ceux atteints d’un syndrome de vasospasticité, 3) évaluer l’association entre la RO et les paramètres biomécaniques de la cornée, 4) évaluer l’association entre la RO et les pics de PIO survenant suite aux thérapies par injections intravitréennes (IIV), afin de les prédire et de les prévenir chez les patients à haut risque, et 5) confirmer que la RO est réduite dans les yeux myopes. D’abord, nous avons amélioré le modèle mathématique de l’oeil utilisé pour dériver ΔV en le rendant plus précis anatomiquement et en tenant compte de la choroïde périphérique. Nous avons démontré la validité et la bonne répétabilité de cette méthodologie. Puis, nous avons effectué la mesure des coefficients de RO sur un large éventail de sujets sains et glaucomateux en utilisant notre méthode non-invasive, et avons démontré, pour la première fois, qu'une RO basse est corrélée aux dommages glaucomateux. Les corrélations observées étaient comparables à celles obtenues avec des facteurs de risque reconnus tels que la PIO maximale. Une forte corrélation entre la RO et les dommages neuro-rétiniens a été observée chez les patients vasospastiques, mais pas chez ceux atteints d'une maladie vasculaire ischémique. Cela pourrait potentiellement indiquer une plus grande susceptibilité au glaucome due à la biomécanique oculaire chez les patients vasospastiques. Bien que les paramètres biomécaniques cornéens aient été largement adoptés dans la pratique clinique en tant que substitut pour la RO, propriété biomécanique globale de l'oeil, nous avons démontré une association limitée entre la RO et ces paramètres, offrant une nouvelle perspective sur la relation entre les propriétés biomécaniques cornéennes et globales de l’oeil. Seule une faible corrélation entre le facteur de résistance cornéenne et la RO demeure après ajustement pour les facteurs de confusion dans le groupe des patients glaucomateux. Ensuite, nous avons présenté un modèle pour prédire l'amplitude des pics de PIO après IIV à partir de la mesure non-invasive de la RO. Ceci est particulièrement utile pour les patients à haut risque atteints de maladies rétiniennes exsudatives et de glaucome qui nécessiteraient des IIV thérapeutiques, et pourrait permettre aux cliniciens d'ajuster ou de personnaliser le traitement pour éviter toute perte de vision additionnelle. Enfin, nous avons étudié les différences de RO entre les yeux myopes et les non-myopes en utilisant cette technique, et avons démontré une RO inférieure dans la myopie axiale, facteur de risque du GPAO. Dans l'ensemble, ces résultats contribuent à l’avancement des connaissances sur la physiopathologie du GPAO. Le développement de notre méthode permettra non seulement de mieux explorer le rôle de la RO dans les maladies oculaires, mais contribuera également à élucider les mécanismes et développer de nouveaux traitements ciblant la RO pour contrer la déficience visuelle liée à ces maladies.Glaucoma is the leading cause of irreversible blindness worldwide. While its pathogenesis is yet to be fully understood, the biomechanical properties of the eye are thought to be involved in the development and progression of this disease. Ocular rigidity (OR) is thought to be altered through disease processes and has been suggested to be the most influential factor on the optic nerve head’s response to variations in intraocular pressure (IOP) in glaucoma. To further investigate the role of OR in open-angle glaucoma (OAG) and other ocular diseases such as myopia, the ability to quantify OR in living human eyes using a reliable and non-invasive method is essential. Such a method has only become available in 2015. Based on the Friedenwald equation, the method uses time-lapse optical coherence tomography (OCT) imaging and automated choroidal segmentation to measure the pulsatile choroidal volume change (ΔV), and Pascal dynamic contour tonometry to measure the corresponding pulsatile pressure change. The purpose of this thesis work was to assess the validity of the methodology, then use it to investigate the role of OR in ocular diseases, particularly in OAG. More specifically, the objectives were: 1) To improve the extrapolation of ΔV and evaluate the method’s validity and repeatability, 2) To investigate the association between OR and neuro-retinal damage in glaucomatous patients, as well as those with concomitant vasospasticity, 3) To evaluate the association between OR and corneal biomechanical parameters, 4) To assess the association between OR and IOP spikes following therapeutic intravitreal injections (IVIs), to predict and prevent them in high-risk patients, and 5) To confirm that OR is lower in myopia. First, we improved the mathematical model of the eye used to derive ΔV by rendering it more anatomically accurate and accounting for the peripheral choroid. We also confirmed the validity and good repeatability of the method. We carried out the measurement of OR coefficients on a wide range of healthy and glaucomatous subjects using this non-invasive method, and were able to show, for the first time, that lower OR is correlated with more glaucomatous damage. The correlations observed were comparable to those obtained with recognized risk factors such as maximum IOP. A strong correlation between OR and neuro-retinal damage was found in patients with concurrent vasospastic syndrome, but not in those with ischemic vascular disease. This could perhaps indicate a greater susceptibility to glaucoma due to ocular biomechanics in vasospastic patients. While corneal biomechanical parameters have been widely adopted in clinical practice as surrogate measurements for the eye’s overall biomechanical properties represented by OR, we have shown a limited association between these parameters, bringing new insight unto the relationship between corneal and global biomechanical properties. Only a weak correlation between the corneal resistance factor and OR remained in glaucomatous eyes after adjusting for confounding factors. In addition, we presented a model to predict the magnitude of IOP spikes following IVIs from the non-invasive measurement of OR. This is particularly useful for high-risk patients with exudative retinal diseases and glaucoma that require therapeutic IVIs, and could provide the clinician an opportunity to adjust or customize treatment to prevent further vision loss. Finally, we investigated OR differences between non-myopic and myopic eyes using this technique, and demonstrated lower OR in axial myopia, a risk factor for OAG. Overall, these findings provide new insights unto the pathophysiology of glaucomatous optic neuropathy. The development of our method will permit further investigation of the role of OR in ocular diseases, contributing to elucidate mechanisms and provide novel management options to counter vision impairment caused by these diseases