Inference of Cerebrovascular Topology with Geodesic Minimum Spanning Trees.

A vectorial representation of the vascular network that embodies quantitative features - location, direction, scale, bifurcations - has many potential cardio- and neuro-vascular applications. We present VTrails, an end-to-end approach to extract geodesic vascular minimum spanning trees from angiographic data by solving a connectivity-optimised anisotropic level-set over a voxel-wise tensor field representing the orientation of the underlying vasculature. Evaluating real and synthetic vascular images, we compare VTrails against the state-of-the-art ridge detectors for tubular structures by assessing the connectedness of the vesselness map and inspecting the synthesized tensor field. The inferred geodesic trees are then quantitatively evaluated within a topologically-aware framework, by comparing the proposed method against popular vascular segmentation tool-kits on clinical angiographies. VTrails potentials are discussed towards integrating group-wise vascular image analyses. The performance of VTrails demonstrates its versatility and usefulness also for patient-specific applications in interventional neuroradiology and vascular surgery

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

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