Retinal Fundus Image Registration via Vascular Structure Graph Matching

Motivated by the observation that a retinal fundus image may contain some unique geometric structures within its vascular trees which can be utilized for feature matching, in this paper, we proposed a graph-based registration framework called GM-ICP to align pairwise retinal images. First, the retinal vessels are automatically detected and represented as vascular structure graphs. A graph matching is then performed to find global correspondences between vascular bifurcations. Finally, a revised ICP algorithm incorporating with quadratic transformation model is used at fine level to register vessel shape models. In order to eliminate the incorrect matches from global correspondence set obtained via graph matching, we proposed a structure-based sample consensus (STRUCT-SAC) algorithm. The advantages of our approach are threefold: (1) global optimum solution can be achieved with graph matching; (2) our method is invariant to linear geometric transformations; and (3) heavy local feature descriptors are not required. The effectiveness of our method is demonstrated by the experiments with 48 pairs retinal images collected from clinical patients

Registration of retinal images by a MAS-ICP approach - a preliminary study

Diabetic retinopathy has been revealed as the most common cause of blindness among people of working age. For monitoring the pathology image registration algorithms applied to retinal images is very useful. In this work, a novel vessel-based retinal image registration approach is proposed. The segmentation of the vasculature is performed by a multi-agent system model. All these information is then used in a Robust Point Matching Iterative Closest Point algorithm improved by a Region Bootstrap approach. With this preliminary study, the novelty of integrating all these algorithms for image registration preceded by a multi-agents system for image edges detection seems to be efficient for temporal retinal image registration. Consequently, a system developed on basis of this approach could help in screening programs for the diabetic retinopathy prevention.C. P. thanks the Fundacao para a Ciencia e Tecnologia (FCT), Portugal for the Ph.D. Grant SFRH/BD/ 61829/2009

Inferring Geodesic Cerebrovascular Graphs: Image Processing, Topological Alignment and Biomarkers Extraction

A vectorial representation of the vascular network that embodies quantitative features - location, direction, scale, and bifurcations - has many potential neuro-vascular applications. Patient-specific models support computer-assisted surgical procedures in neurovascular interventions, while analyses on multiple subjects are essential for group-level studies on which clinical prediction and therapeutic inference ultimately depend. This first motivated the development of a variety of methods to segment the cerebrovascular system. Nonetheless, a number of limitations, ranging from data-driven inhomogeneities, the anatomical intra- and inter-subject variability, the lack of exhaustive ground-truth, the need for operator-dependent processing pipelines, and the highly non-linear vascular domain, still make the automatic inference of the cerebrovascular topology an open problem. In this thesis, brain vessels’ topology is inferred by focusing on their connectedness. With a novel framework, the brain vasculature is recovered from 3D angiographies by solving a connectivity-optimised anisotropic level-set over a voxel-wise tensor field representing the orientation of the underlying vasculature. Assuming vessels joining by minimal paths, a connectivity paradigm is formulated to automatically determine the vascular topology as an over-connected geodesic graph. Ultimately, deep-brain vascular structures are extracted with geodesic minimum spanning trees. The inferred topologies are then aligned with similar ones for labelling and propagating information over a non-linear vectorial domain, where the branching pattern of a set of vessels transcends a subject-specific quantized grid. Using a multi-source embedding of a vascular graph, the pairwise registration of topologies is performed with the state-of-the-art graph matching techniques employed in computer vision. Functional biomarkers are determined over the neurovascular graphs with two complementary approaches. Efficient approximations of blood flow and pressure drop account for autoregulation and compensation mechanisms in the whole network in presence of perturbations, using lumped-parameters analog-equivalents from clinical angiographies. Also, a localised NURBS-based parametrisation of bifurcations is introduced to model fluid-solid interactions by means of hemodynamic simulations using an isogeometric analysis framework, where both geometry and solution profile at the interface share the same homogeneous domain. Experimental results on synthetic and clinical angiographies validated the proposed formulations. Perspectives and future works are discussed for the group-wise alignment of cerebrovascular topologies over a population, towards defining cerebrovascular atlases, and for further topological optimisation strategies and risk prediction models for therapeutic inference. Most of the algorithms presented in this work are available as part of the open-source package VTrails

Advanced retinal imaging: Feature extraction, 2-D registration, and 3-D reconstruction

In this dissertation, we have studied feature extraction and multiple view geometry in the context of retinal imaging. Specifically, this research involves three components, i.e., feature extraction, 2-D registration, and 3-D reconstruction. First, the problem of feature extraction is investigated. Features are significantly important in motion estimation techniques because they are the input to the algorithms. We have proposed a feature extraction algorithm for retinal images. Bifurcations/crossovers are used as features. A modified local entropy thresholding algorithm based on a new definition of co-occurrence matrix is proposed. Then, we consider 2-D retinal image registration which is the problem of the transformation of 2-D/2-D. Both linear and nonlinear models are incorporated to account for motions and distortions. A hybrid registration method has been introduced in order to take advantages of both feature-based and area-based methods have offered along with relevant decision-making criteria. Area-based binary mutual information is proposed or translation estimation. A feature-based hierarchical registration technique, which involves the affine and quadratic transformations, is developed. After that, a 3-D retinal surface reconstruction issue has been addressed. To generate a 3-D scene from 2-D images, a camera projection or transformations of 3-D/2-D techniques have been investigated. We choose an affine camera to characterize for 3-D retinal reconstruction. We introduce a constrained optimization procedure which incorporates a geometrically penalty function and lens distortion into the cost function. The procedure optimizes all of the parameters, camera's parameters, 3-D points, the physical shape of human retina, and lens distortion, simultaneously. Then, a point-based spherical fitting method is introduced. The proposed retinal imaging techniques will pave the path to a comprehensive visual 3-D retinal model for many medical applications

The ins and outs of the BCCAo model for chronic hypoperfusion: a multimodal and longitudinal MRI approach

Cerebral hypoperfusion induced by bilateral common carotid artery occlusion (BCCAo) in rodents has been proposed as an experimental model of white matter damage and vascular dementia. However, the histopathological and behavioral alterations reported in this model are variable and a full characterization of the dynamic alterations is not available. Here we implemented a longitudinal multimodal magnetic resonance imaging (MRI) design, including time- of-flight angiography, high resolution T1-weighted images, T2 relaxometry mapping, diffusion tensor imaging, and cerebral blood flow measurements up to 12 weeks after BCCAo or sham-operation in Wistar rats. Changes in MRI were related to behavioral performance in executive function tasks and histopathological alterations in the same animals. MRI frequently (70%) showed various degrees of acute ischemic lesions, ranging from very small to large subcortical infarctions. Independently, delayed MRI changes were also apparent. The patterns of MRI alterations were related to either ischemic necrosis or gliosis. Progressive microstructural changes revealed by diffusion tensor imaging in white matter were confirmed by observation of myelinated fiber degeneration, including severe optic tract degeneration. The latter interfered with the visually cued learning paradigms used to test executive functions. Independently of brain damage, BCCAo induced progressive arteriogenesis in the vertebrobasilar tree, a process that was associated with blood flow recovery after 12 weeks. The structural alterations found in the basilar artery were compatible with compensatory adaptive changes driven by shear stress. In summary, BCCAo in rats induces specific signatures in multimodal MRI that are compatible with various types of histological lesion and with marked adaptive arteriogenesis

Deep Learning Techniques for Automated Analysis and Processing of High Resolution Medical Imaging

Programa Oficial de Doutoramento en Computación . 5009V01[Abstract] Medical imaging plays a prominent role in modern clinical practice for numerous medical specialties. For instance, in ophthalmology, different imaging techniques are commonly used to visualize and study the eye fundus. In this context, automated image analysis methods are key towards facilitating the early diagnosis and adequate treatment of several diseases. Nowadays, deep learning algorithms have already demonstrated a remarkable performance for different image analysis tasks. However, these approaches typically require large amounts of annotated data for the training of deep neural networks. This complicates the adoption of deep learning approaches, especially in areas where large scale annotated datasets are harder to obtain, such as in medical imaging. This thesis aims to explore novel approaches for the automated analysis of medical images, particularly in ophthalmology. In this regard, the main focus is on the development of novel deep learning-based approaches that do not require large amounts of annotated training data and can be applied to high resolution images. For that purpose, we have presented a novel paradigm that allows to take advantage of unlabeled complementary image modalities for the training of deep neural networks. Additionally, we have also developed novel approaches for the detailed analysis of eye fundus images. In that regard, this thesis explores the analysis of relevant retinal structures as well as the diagnosis of different retinal diseases. In general, the developed algorithms provide satisfactory results for the analysis of the eye fundus, even when limited annotated training data is available.[Resumen] Las técnicas de imagen tienen un papel destacado en la práctica clínica moderna de numerosas especialidades médicas. Por ejemplo, en oftalmología es común el uso de diferentes técnicas de imagen para visualizar y estudiar el fondo de ojo. En este contexto, los métodos automáticos de análisis de imagen son clave para facilitar el diagnóstico precoz y el tratamiento adecuado de diversas enfermedades. En la actualidad, los algoritmos de aprendizaje profundo ya han demostrado un notable rendimiento en diferentes tareas de análisis de imagen. Sin embargo, estos métodos suelen necesitar grandes cantidades de datos etiquetados para el entrenamiento de las redes neuronales profundas. Esto complica la adopción de los métodos de aprendizaje profundo, especialmente en áreas donde los conjuntos masivos de datos etiquetados son más difíciles de obtener, como es el caso de la imagen médica. Esta tesis tiene como objetivo explorar nuevos métodos para el análisis automático de imagen médica, concretamente en oftalmología. En este sentido, el foco principal es el desarrollo de nuevos métodos basados en aprendizaje profundo que no requieran grandes cantidades de datos etiquetados para el entrenamiento y puedan aplicarse a imágenes de alta resolución. Para ello, hemos presentado un nuevo paradigma que permite aprovechar modalidades de imagen complementarias no etiquetadas para el entrenamiento de redes neuronales profundas. Además, también hemos desarrollado nuevos métodos para el análisis en detalle de las imágenes del fondo de ojo. En este sentido, esta tesis explora el análisis de estructuras retinianas relevantes, así como el diagnóstico de diferentes enfermedades de la retina. En general, los algoritmos desarrollados proporcionan resultados satisfactorios para el análisis de las imágenes de fondo de ojo, incluso cuando la disponibilidad de datos de entrenamiento etiquetados es limitada.[Resumo] As técnicas de imaxe teñen un papel destacado na práctica clínica moderna de numerosas especialidades médicas. Por exemplo, en oftalmoloxía é común o uso de diferentes técnicas de imaxe para visualizar e estudar o fondo de ollo. Neste contexto, os métodos automáticos de análises de imaxe son clave para facilitar o diagn ostico precoz e o tratamento adecuado de diversas enfermidades. Na actualidade, os algoritmos de aprendizaxe profunda xa demostraron un notable rendemento en diferentes tarefas de análises de imaxe. Con todo, estes métodos adoitan necesitar grandes cantidades de datos etiquetos para o adestramento das redes neuronais profundas. Isto complica a adopción dos métodos de aprendizaxe profunda, especialmente en áreas onde os conxuntos masivos de datos etiquetados son máis difíciles de obter, como é o caso da imaxe médica. Esta tese ten como obxectivo explorar novos métodos para a análise automática de imaxe médica, concretamente en oftalmoloxía. Neste sentido, o foco principal é o desenvolvemento de novos métodos baseados en aprendizaxe profunda que non requiran grandes cantidades de datos etiquetados para o adestramento e poidan aplicarse a imaxes de alta resolución. Para iso, presentamos un novo paradigma que permite aproveitar modalidades de imaxe complementarias non etiquetadas para o adestramento de redes neuronais profundas. Ademais, tamén desenvolvemos novos métodos para a análise en detalle das imaxes do fondo de ollo. Neste sentido, esta tese explora a análise de estruturas retinianas relevantes, así como o diagnóstico de diferentes enfermidades da retina. En xeral, os algoritmos desenvolvidos proporcionan resultados satisfactorios para a análise das imaxes de fondo de ollo, mesmo cando a dispoñibilidade de datos de adestramento etiquetados é limitada

Linear discriminant analysis reveals differences in root architecture in wheat seedlings by nitrogen uptake efficiency

Root architecture impacts water and nutrient uptake efficiency. Identifying exactly which root architectural properties influence these agronomic traits can prove challenging. In this paper approximately 300 wheat plants were divided into four groups using two binary classifications, high vs. low nitrogen uptake efficiency (NUpE), and high vs. low nitrate in medium. The root system architecture for each wheat plant was captured using 16 quantitative variables. The multivariate analysis tool, linear discriminant analysis, was used to construct composite variables, each a linear combination of the original variables, such that the score of the wheat plants on the new variables showed the maximum between-group variability. The results show that the distribution of root system architecture traits differ between low and high NUpE wheat plants and, less strongly, between low NUpE wheat plants grown on low vs. high nitrate media

Relation between plaque type, plaque thickness, blood shear stress, and plaque stress in coronary arteries assessed by X-ray Angiography and Intravascular Ultrasound

Purpose: Atheromatic plaque progression is affected, among others phenomena, by biomechanical, biochemical, and physiological factors. In this paper, the authors introduce a novel framework able to provide both morphological (vessel radius, plaque thickness, and type) and biomechanical (wall shear stress and Von Mises stress) indices of coronary arteries. Methods: First, the approach reconstructs the three-dimensional morphology of the vessel from intravascular ultrasound(IVUS) and Angiographic sequences, requiring minimal user interaction. Then, a computational pipeline allows to automatically assess fluid-dynamic and mechanical indices. Ten coronary arteries are analyzed illustrating the capabilities of the tool and confirming previous technical and clinical observations. Results: The relations between the arterial indices obtained by IVUS measurement and simulations have been quantitatively analyzed along the whole surface of the artery, extending the analysis of the coronary arteries shown in previous state of the art studies. Additionally, for the first time in the literature, the framework allows the computation of the membrane stresses using a simplified mechanical model of the arterial wall. Conclusions: Circumferentially (within a given frame), statistical analysis shows an inverse relation between the wall shear stress and the plaque thickness. At the global level (comparing a frame within the entire vessel), it is observed that heavy plaque accumulations are in general calcified and are located in the areas of the vessel having high wall shear stress. Finally, in their experiments the inverse proportionality between fluid and structural stresses is observed

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