Retinal vessel segmentation using textons

Segmenting vessels from retinal images, like segmentation in many other medical image domains, is a challenging task, as there is no unified way that can be adopted to extract the vessels accurately. However, it is the most critical stage in automatic assessment of various forms of diseases (e.g. Glaucoma, Age-related macular degeneration, diabetic retinopathy and cardiovascular diseases etc.). Our research aims to investigate retinal image segmentation approaches based on textons as they provide a compact description of texture that can be learnt from a training set. This thesis presents a brief review of those diseases and also includes their current situations, future trends and techniques used for their automatic diagnosis in routine clinical applications. The importance of retinal vessel segmentation is particularly emphasized in such applications. An extensive review of previous work on retinal vessel segmentation and salient texture analysis methods is presented. Five automatic retinal vessel segmentation methods are proposed in this thesis. The first method focuses on addressing the problem of removing pathological anomalies (Drusen, exudates) for retinal vessel segmentation, which have been identified by other researchers as a problem and a common source of error. The results show that the modified method shows some improvement compared to a previously published method. The second novel supervised segmentation method employs textons. We propose a new filter bank (MR11) that includes bar detectors for vascular feature extraction and other kernels to detect edges and photometric variations in the image. The k-means clustering algorithm is adopted for texton generation based on the vessel and non-vessel elements which are identified by ground truth. The third improved supervised method is developed based on the second one, in which textons are generated by k-means clustering and texton maps representing vessels are derived by back projecting pixel clusters onto hand labelled ground truth. A further step is implemented to ensure that the best combinations of textons are represented in the map and subsequently used to identify vessels in the test set. The experimental results on two benchmark datasets show that our proposed method performs well compared to other published work and the results of human experts. A further test of our system on an independent set of optical fundus images verified its consistent performance. The statistical analysis on experimental results also reveals that it is possible to train unified textons for retinal vessel segmentation. In the fourth method a novel scheme using Gabor filter bank for vessel feature extraction is proposed. The ii method is inspired by the human visual system. Machine learning is used to optimize the Gabor filter parameters. The experimental results demonstrate that our method significantly enhances the true positive rate while maintaining a level of specificity that is comparable with other approaches. Finally, we proposed a new unsupervised texton based retinal vessel segmentation method using derivative of SIFT and multi-scale Gabor filers. The lack of sufficient quantities of hand labelled ground truth and the high level of variability in ground truth labels amongst experts provides the motivation for this approach. The evaluation results reveal that our unsupervised segmentation method is comparable with the best other supervised methods and other best state of the art methods

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

Computational analysis of blood flow and oxygen transport in the retinal arterial network

Pathological changes in retinal microvasculature are known to be associated with systemic diseases such as hypertension and diabetes, and may result in potentially disadvantageous blood flow and impair oxygen distribution. Therefore, in order to improve our understanding of the link between systemic diseases and the retinal circulation, it is necessary to develop an approach to quantitatively determine the hemodynamic and oxygen transport parameters in the retinal vascular circulation. This thesis aims to provide more insights into the detailed hemodynamic features of the retinal arterial tree by means of non-invasive imaging and computational modelling. It covers the following two aspects: i) 3D reconstruction of the retinal arterial tree, and ii) development of an image-based computational model to predict blood flow and oxygen transport in realistic subject-specific retinal arterial trees. The latter forms the main body of the thesis. 3D reconstruction of the retinal arterial tree was performed based on retinal images acquired in vivo with a fundus camera and validated using a simple 3D object. The reproduction procedure was found to be feasible but with limited accuracy. In the proposed 2D computational model, the smaller peripheral vessels indistinguishable from the retinal images were represented by self-similar asymmetric structured trees. The non-Newtonian properties of blood, and nonlinear oxyhemoglobin dissociation in the red blood cells and plasma were considered. The simulation results of the computational model were found in good agreement with in vivo measurements reported in the literature. In order to understand the effect of retinal vascular structure on blood flow and oxygen transport, the computational model was applied to subject-specific geometries for a number of hypertensive and diabetic patients, and comparisons were made with results obtained from healthy retinal arterial networks. Moreover, energy analysis of normal and hypertensive subjects was performed using 3D hypothetical models. Finally, the influence of different viscosity models on flow and oxygen transport in a retinal tree and the advantage of low dimensional models were examined. This study has demonstrated the applicability of the image-based computational modelling to study the hemodynamics and oxygen distribution in the retinal arterial network

Modélisation statistique des structures anatomiques de la rétine à partir d'images de fond d'oeil

L’examen non-invasif du fond d’oeil permet d’identifier sur la rétine les signes de nombreuses pathologies oculaires qui développent de graves symptômes pour le patient pouvant entraîner la cécité. Le réseau vasculaire rétinien peut de surcroît présenter des signes précurseurs de pathologies cardiovasculaires et cérébro-vasculaires. La rétine, où apparaissent ces pathologies, est constituée de plusieurs structures anatomiques dont la variabilité est importante au sein d’une population saine. Pour autant, les évaluations cliniques actuelles ne prennent pas en compte cette variabilité ce qui ne permet pas de détecter précocement ces pathologies. Ces évaluations se basent sur un ensemble restreint de mesures prélevées à partir de structures dont la segmentation manuelle est réalisable par les experts. De plus, elles sont basées sur un seuillage empirique déterminé par les cliniciens et appliqué sur chacune des mesures afin d’établir un diagnostic. Ainsi, les évaluations cliniques actuelles sont affectées par la grande variabilité des structures anatomiques de la rétine au sein de la population et elles n’évaluent pas les anomalies trop difficiles à mesurer manuellement. Dans ce contexte, il convient de proposer de nouvelles mesures cliniques qui tiennent compte de la variabilité normale à l’aide d’une modélisation statistique des structures anatomiques de la rétine. Cette modélisation statistique permet de mieux comprendre et identifier ce qui est normal et comment l’anatomie et ses attributs varient au sein d’une population saine. Cela permet ainsi d’identifier la présence de pathologies à l’aide de nouvelles mesures cliniques construites en tenant compte de la variabilité des attributs de l’anatomie. La modélisation statistique des structures anatomiques de la rétine est cependant difficile étant donné les variations morphologiques et topologiques de ces structures. Les changements morphologiques et topologiques du réseau vasculaire rétinien compliquent son analyse statistique ainsi que les outils de recalage, de segmentation et de représentation sémantique s’y appliquant. Les questions de recherches adressées dans cette thèse sont la production d’outils capables d’analyser la variabilité des structures anatomiques de la rétine et l’élaboration de nouvelles mesures cliniques tenant compte de la variabilité normale de ces structures. Pour répondre à ces questions de recherche, trois objectifs de recherche sont formulés. ----------ABSTRACT: Non-invasive retinal fundus examination allows clinicians to identify signs of many ocular conditions that develop critical symptoms affecting the patient and even leading to blindness. In addition, the retinal vascular network may present early signs of cardiovascular and cerebrovascular diseases. The retina, where these pathologies appear, is composed of several anatomical structures whose variability is considerable within a healthy population. Yet, current clinical evaluations do not take into account this variability, and this does not allow early detection of these pathologies. These evaluations are based on a limited set of measurements taken from structures whose manual segmentation is achievable by the experts. In addition, they are based on empirical thresholding determined by the clinicians and applied to each of the measurements to establish a diagnosis. Thus, current clinical assessments are affected by the large variability of anatomical structures of the retina within a healthy population and do not evaluate abnormalities that are too difficult to measure manually. In this context, it is advisable to propose new clinical measurements that take into account the normal variability using statistical modeling of the anatomical structures of the retina. Such a statistical modeling approach helps us to better understand and identify what is normal and how the anatomy and its attributes vary across a healthy population. This makes it possible to identify the presence of pathologies using new clinical measurements constructed by taking into account the variability of the anatomy’s attributes. Statistical modeling of the anatomical structures of the retina is difficult, however, given the morphological and topological variations of these structures. Morphological and topological changes in the retinal vascular network complicate its statistical analysis as well as the registration methods, segmentation and semantic representation applied to it. The research questions proposed in this thesis pertain to creating tools capable of analyzing the variability of the anatomical structures of the retina and proposing new clinical measures that take into account the normal variability of those structures. To answer these research questions, three research objectives are formulated