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

VTrails: Inferring Vessels with Geodesic Connectivity Trees

The analysis of vessel morphology and connectivity has an impact on a number of cardiovascular and neurovascular applications by providing patient-specific high-level quantitative features such as spatial location, direction and scale. In this paper we present an end-to-end approach to extract an acyclic vascular tree from angiographic data by solving a connectivity-enforcing anisotropic fast marching over a voxel-wise tensor field representing the orientation of the underlying vascular tree. The method is validated using synthetic and real vascular images. We compare VTrails against classical and state-of-the-art ridge detectors for tubular structures by assessing the connectedness of the vesselness map and inspecting the synthesized tensor field as proof of concept. VTrails performance is evaluated on images with different levels of degradation: we verify that the extracted vascular network is an acyclic graph (i.e. a tree), and we report the extraction accuracy, precision and recall

Automated Reconstruction of Evolving Curvilinear Tree Structures

Curvilinear networks are prevalent in nature and span many different scales, ranging from micron-scale neural structures in the brain to petameter-scale dark-matter arbors binding massive galaxy clusters. Reliably reconstructing them in an automated fashion is of great value in many different scientific domains. However, it remains an open Computer Vision problem. In this thesis we focus on automatically delineating curvilinear tree structures in images of the same object of interest taken at different time instants. Unlike virtually all of the existing methods approaching the task of tree structures delineation we process all the images at once. This is useful in the more ambiguous regions and allows to reason for the tree structure that fits best to all the acquired data. We propose two methods that utilize this principle of temporal consistency to achieve results of higher quality compared to single time instant methods. The first, simpler method starts by building an overcomplete graph representation of the final solution in all time instants while simultaneously obtaining correspondences between image features across time. We then define an objective function with a temporal consistency prior and reconstruct the structures in all images at once by solving a mathematical optimization. The role of the prior is to encourage solutions where for two consecutive time instants corresponding candidate edges are either both retained or both rejected from the final solution. The second multiple time instant method uses the same overcomplete graph principle but handles the temporal consistency in a more robust way. Instead of focusing on the very local consistency of single edges of the overcomplete graph we propose a method for describing topological relationships. This favors solutions whose connectivity is consistent over time. We show that by making the temporal consistency more global we achieve additional robustness to errors in the initial features matching step, which is shared by both the approaches. In the end, this yields superior performance. Furthermore, an added benefit of both our approaches is the ability to automatically detect places where significant changes have occurred over time, which is challenging when considering large amounts of data. We also propose a simple single time instant method for delineating tree structures. It computes a Minimum Spanning Arborescence of an initial overcomplete graph and proceeds to optimally prune spurious branches. This yields results of lower but still competitive quality compared to the mathematical optimization based methods, while keeping low computational complexity. Our methods can applied to both 2D and 3D data. We demonstrate their performance in 3D on microscopy volumes of mouse brain and rat brain. We also test them in 2D on time-lapse images of a growing runner bean and aerial images of a road network

Archaeological evaluation report : Cuerden Strategic Site, South Ribble, Lancashire

Lancashire County Council and Maple Grove Development Limited have obtained planning consent for a major mixed-use development at Cuerden, in the Central Lancashire borough of South Ribble. The study area extends to 65 hectares and comprises land to the south of the M65, to the west of A49 Wigan Road, and east of Stanifield Lane, near Cuerden in Lancashire (centred on NGR SD 55526 24603). The hybrid planning application (Planning Ref: 07/2017/0211/ORM) comprises a wide range of residential and commercial premises, car parks and roads, the construction of which is likely to cause damage to any surviving archaeological remains. The potential for archaeological remains to survive across the development site was highlighted in an archaeological assessment that was prepared to support the planning application. This concluded that intrusive site investigation to establish the presence or absence of archaeological remains was merited. In the light of this conclusion, Lancashire County Council commissioned Salford Archaeology to devise an execute an appropriate programme of archaeological investigation which, in the first instance, comprised the excavation of 15 evaluation trenches that aimed to establish the presence, extent and nature or any below-ground remains, to enable informed recommendations to be made for the future of any surviving features. The trenches were targeted on a medieval/post-medieval farmstead, and a range of cropmarks indicative of earlier field-systems and occupation, perhaps of prehistoric origin. In addition, the two putative routes of a Roman road between the fort at Wigan and the industrial settlement at Walton-le-Dale are projected along the western and eastern edges of the site, and these courses were targeted by evaluation trenches. The results obtained from the evaluation have demonstrated the survival of a suite of structural remains relating to the medieval/post-medieval farmstead, together with several negative features of post-medieval date. The trenching of known or suspected cropmarks also produced positive results. The presence of ditches and gullies attest to ancient field systems, which diverge from the existing pattern of field boundaries. Such features appear on the basis of their typology and stratigraphy to pre-date the medieval/post-medieval enclosures and, significantly, may potentially represent prehistoric activity or settlement in the area. It is not considered that any of these remains are of national importance that would necessitate preservation in-situ, although the features encountered during the evaluation are considered to be of high local/borough or regional significance, particularly those features of potential prehistoric origin. In order to offset the harm of development on the archaeological resource of the site, it would be appropriate to implement a further stage of intrusive archaeological excavation in advance of development

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

A graph-based mathematical morphology reader

This survey paper aims at providing a "literary" anthology of mathematical morphology on graphs. It describes in the English language many ideas stemming from a large number of different papers, hence providing a unified view of an active and diverse field of research

Variational methods and its applications to computer vision

Many computer vision applications such as image segmentation can be formulated in a ''variational'' way as energy minimization problems. Unfortunately, the computational task of minimizing these energies is usually difficult as it generally involves non convex functions in a space with thousands of dimensions and often the associated combinatorial problems are NP-hard to solve. Furthermore, they are ill-posed inverse problems and therefore are extremely sensitive to perturbations (e.g. noise). For this reason in order to compute a physically reliable approximation from given noisy data, it is necessary to incorporate into the mathematical model appropriate regularizations that require complex computations. The main aim of this work is to describe variational segmentation methods that are particularly effective for curvilinear structures. Due to their complex geometry, classical regularization techniques cannot be adopted because they lead to the loss of most of low contrasted details. In contrast, the proposed method not only better preserves curvilinear structures, but also reconnects some parts that may have been disconnected by noise. Moreover, it can be easily extensible to graphs and successfully applied to different types of data such as medical imagery (i.e. vessels, hearth coronaries etc), material samples (i.e. concrete) and satellite signals (i.e. streets, rivers etc.). In particular, we will show results and performances about an implementation targeting new generation of High Performance Computing (HPC) architectures where different types of coprocessors cooperate. The involved dataset consists of approximately 200 images of cracks, captured in three different tunnels by a robotic machine designed for the European ROBO-SPECT project.Open Acces

Learning Approach to Delineation of Curvilinear Structures in 2D and 3D Images

Detection of curvilinear structures has long been of interest due to its wide range of applications. Large amounts of imaging data could be readily used in many fields, but it is practically not possible to analyze them manually. Hence, the need for automated delineation approaches. In the recent years Computer Vision witnessed a paradigm shift from mathematical modelling to data-driven methods based on Machine Learning. This led to improvements in performance and robustness of the detection algorithms. Nonetheless, most Machine Learning methods are general-purpose and they do not exploit the specificity of the delineation problem. In this thesis, we present learning methods suited for this task and we apply them to various kinds of microscopic and natural images, proving the general applicability of the presented solutions. First, we introduce a topology loss - a new training loss term, which captures higher-level features of curvilinear networks such as smoothness, connectivity and continuity. This is in contrast to most Deep Learning segmentation methods that do not take into account the geometry of the resulting prediction. In order to compute the new loss term, we extract topology features of prediction and ground-truth using a pre-trained network, whose filters are activated by structures at different scales and orientations. We show that this approach yields better results in terms of conventional segmentation metrics and overall topology of the resulting delineation. Although segmentation of curvilinear structures provides useful information, it is not always sufficient. In many cases, such as neuroscience and cartography, it is crucial to estimate the network connectivity. In order to find the graph representation of the structure depicted in the image, we propose an approach for joint segmentation and connection classification. Apart from pixel probabilities, this approach also returns the likelihood of a proposed path being a part of the reconstructed network. We show that segmentation and path classification are closely related tasks and can benefit from the synergy. The aforementioned methods rely on Machine Learning, which requires significant amounts of annotated ground-truth data to train models. The labelling process often requires expertise, it is costly and tiresome. To alleviate this problem, we introduce an Active Learning method that significantly decreases the time spent on annotating images. It queries the annotator only about the most informative examples, in this case the hypothetical paths belonging to the structure of interest. Contrary to conventional Active Learning methods, our approach exploits local consistency of linear paths to pick the ones that stand out from their neighborhood. Our final contribution is a method suited for both Active Learning and proofreading the result, which often requires more time than the automated delineation itself. It investigates edges of the delineation graph and determines the ones that are especially significant for the global reconstruction by perturbing their weights. Our Active Learning and proofreading strategies are combined with a new efficient formulation of an optimal subgraph computation and reduce the annotation effort by up to 80%

Reconstructing Curvilinear Networks using Path Classifiers and Integer Programming

We propose a novel Bayesian approach to automated delineation of curvilinear structures that form complex and potentially loopy networks. By representing the image data as a graph of potential paths, we first show how to weight these paths using discriminatively-trained classifiers that are both robust and generic enough to be applied to very different imaging modalities. We then present an Integer Programming approach to finding the optimal subset of paths, subject to structural and topological constraints that eliminate implausible solutions. Unlike earlier approaches that assume a tree topology for the networks, ours explicitly models the fact that the networks may contain loops, and can reconstruct both cyclic and acyclic ones. We demonstrate the effectiveness of our approach on a variety of challenging datasets including aerial images of road networks and micrographs of neural arbors, and show that it outperforms state-of-the-art techniques

Distributed texture-based terrain synthesis

Terrain synthesis is an important field of Computer Graphics that deals with the generation of 3D landscape models for use in virtual environments. The field has evolved to a stage where large and even infinite landscapes can be generated in realtime. However, user control of the generation process is still minimal, as well as the creation of virtual landscapes that mimic real terrain. This thesis investigates the use of texture synthesis techniques on real landscapes to improve realism and the use of sketch-based interfaces to enable intuitive user control