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

Topology-Aware Uncertainty for Image Segmentation

Segmentation of curvilinear structures such as vasculature and road networks is challenging due to relatively weak signals and complex geometry/topology. To facilitate and accelerate large scale annotation, one has to adopt semi-automatic approaches such as proofreading by experts. In this work, we focus on uncertainty estimation for such tasks, so that highly uncertain, and thus error-prone structures can be identified for human annotators to verify. Unlike most existing works, which provide pixel-wise uncertainty maps, we stipulate it is crucial to estimate uncertainty in the units of topological structures, e.g., small pieces of connections and branches. To achieve this, we leverage tools from topological data analysis, specifically discrete Morse theory (DMT), to first capture the structures, and then reason about their uncertainties. To model the uncertainty, we (1) propose a joint prediction model that estimates the uncertainty of a structure while taking the neighboring structures into consideration (inter-structural uncertainty); (2) propose a novel Probabilistic DMT to model the inherent uncertainty within each structure (intra-structural uncertainty) by sampling its representations via a perturb-and-walk scheme. On various 2D and 3D datasets, our method produces better structure-wise uncertainty maps compared to existing works.Comment: 19 pages, 13 figures, 5 table

Computational studies of vascularized tumors

Cancer is a hard problem touching numerous branches of life science. One reason for the complexity of cancer is that tumors act across many different time and length scales ranging from the subcellular to the macroscopic level. Modern sciences still lack an integral understanding of cancer, however in recent years, increasing computational power enabled computational models to accompany and support conventional medical and biological methods bridging the scales from micro to macro. Here I report a multiscale computational model simulating the progression of solid tumors comprising the vasculature mimicked by artificial arterio-venous blood vessel networks. I present a numerical optimization procedure to determine radii of blood vessels in an artificial microcirculation based on physiological stimuli independently of Murray’s law. Comprising the blood vessels, the reported model enables the inspection of blood vessel remodeling dynamics (angiogenesis, vaso-dilation, vessel regression and collapse) during tumor growth. We successfully applied the method to simulated tumor blood vessel networks guided by optical mammography data. In subsequent model development, I included cellular details into the method enabling a computational study of the tumor microenvironment at cellular resolution. I found that small vascularized tumors at the angiogenic switch exhibit a large ecological niche diversity resulting in high evolutionary pressure favoring the colonal selecion hypothesis.Krebs ist ein schwieriges Thema und tritt in zahlreichen Gebieten auf. Ein Grund für die Komplexität des Tumorwachstums sind die unterschiedlichen Zeit- und Längenskalen. In der aktuellen Forschung fehlt immernoch ein ganzheitliches Verständnis von Krebs, obwohl die computergestützten Methoden in den vergangenen Jahren die konventionellen Methoden der Medizin und der Biologie erweitern und unterstützen. Damit wird die Kluft zwischen subzellulären und makroskopischen Prozessen bereits verringert. In der vorliegenden Arbeit dokumentiere ich ein computergestütztes Verfahren, welches das Tumorwachstum auf mehreren Skalen simuliert. Insbesondere wird das Blutgefäßsystem durch künstliche Gefäße nachgeahmt. Es wurde ein numerisches Optimierungsverfahren zur Bestimmung der Gefäßradien eines künstlichen Blutkreislaufes entwickelt, welches auf physiologischen Reizen basiert und unabhängig von Murray‘s Gesetz ist. Da das beschriebene Verfahren zur Simulation von Tumoren Blutgefäße beinhaltet, kann die Umbildung des Gefäßbaumes während des Tumorwachstums untersucht werden. Das Modell wurde erfolgreich mit krankhaften Gefäßsystemen verglichen. In der darauffolgenden Weiterentwicklung des Modells berücksichtigte ich zelluläre Feinheiten, die es mir erlaubten das Mikromilieu in zellulärer Auflösung zu untersuchen. Meine Resultate zeigen, dass bereits kleine Tumore eine hohe ökologische Vielfalt besitzen, was den Selektionsdruck erhöht und damit die Klon-Selektionstheorie begünstigt

An In-Depth Statistical Review of Retinal Image Processing Models from a Clinical Perspective

The burgeoning field of retinal image processing is critical in facilitating early diagnosis and treatment of retinal diseases, which are amongst the leading causes of vision impairment globally. Despite rapid advancements, existing machine learning models for retinal image processing are characterized by significant limitations, including disparities in pre-processing, segmentation, and classification methodologies, as well as inconsistencies in post-processing operations. These limitations hinder the realization of accurate, reliable, and clinically relevant outcomes. This paper provides an in-depth statistical review of extant machine learning models used in retinal image processing, meticulously comparing them based on their internal operating characteristics and performance levels. By adopting a robust analytical approach, our review delineates the strengths and weaknesses of current models, offering comprehensive insights that are instrumental in guiding future research and development in this domain. Furthermore, this review underscores the potential clinical impacts of these models, highlighting their pivotal role in enhancing diagnostic accuracy, prognostic assessments, and therapeutic interventions for retinal disorders. In conclusion, our work not only bridges the existing knowledge gap in the literature but also paves the way for the evolution of more sophisticated and clinically-aligned retinal image processing models, ultimately contributing to improved patient outcomes and advancements in ophthalmic care

Reconstruction of coronary arteries from X-ray angiography: A review.

Despite continuous progress in X-ray angiography systems, X-ray coronary angiography is fundamentally limited by its 2D representation of moving coronary arterial trees, which can negatively impact assessment of coronary artery disease and guidance of percutaneous coronary intervention. To provide clinicians with 3D/3D+time information of coronary arteries, methods computing reconstructions of coronary arteries from X-ray angiography are required. Because of several aspects (e.g. cardiac and respiratory motion, type of X-ray system), reconstruction from X-ray coronary angiography has led to vast amount of research and it still remains as a challenging and dynamic research area. In this paper, we review the state-of-the-art approaches on reconstruction of high-contrast coronary arteries from X-ray angiography. We mainly focus on the theoretical features in model-based (modelling) and tomographic reconstruction of coronary arteries, and discuss the evaluation strategies. We also discuss the potential role of reconstructions in clinical decision making and interventional guidance, and highlight areas for future research

Cancer theranostics: multifunctional gold nanoparticles for diagnostics and therapy

Doctorate in Biology, Specialty in BiotechnologyThe use of gold nanoparticles (AuNPs) has been gaining momentum in molecular diagnostics due to their unique physico-chemical properties these systems present huge advantages, such as increased sensitivity, reduced cost and potential for single-molecule characterisation. Because of their versatility and easy of functionalisation, multifunctional AuNPs have also been proposed as optimal delivery systems for therapy (nanovectors). Being able to produce such systems would mean the dawn of a new age in theranostics (diagnostics and therapy)driven by nanotechnology vehicles. Nanotechnology can be exploit for cancer theranostics via the development of diagnostics systems such as colorimetric and imunoassays, and in therapy approaches through gene therapy, drug delivery and tumour targeting systems. The unique characteristics of nanoparticles in the nanometre range, such as high surface-tovolume ratio or shape/size-dependent optical properties, are drastically different from those of their bulk materials and hold pledge in the clinical field for disease therapeutics This PhD project intends to optimise a gold-nanoparticle based technique for the detection of oncogenes’ transcripts (c-Myc and BCR-ABL) that can be used for the evaluation of the expression profile in cancer cells, while simultaneously developing an innovative platform of multifunctional gold nanoparticles (tumour markers, cell penetrating peptides, fluorescent dyes) loaded with siRNA capable of silencing the selected proto-oncogenes, which can be used to evaluate the level of expression and determine the efficiency of silencing. This work is a part of an ongoing collaboration between Research Centre for Human Molecular Genetics, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Portugal and Biofunctional Nanoparticles and Surfaces Group, Instituto de Nanociencia de Aragón, Spain within a European project [NanoScieE+ - NANOTRUCK]. In order to achieve this goal we developed effective conjugation strategies to combine, in a highly controlled way, biomolecules to the surface of AuNPs with specific functions such as: ssDNA oligos to detect specific sequences and for mRNA quantification; Biofunctional spacers: Poly(ethylene glycol) (PEG) spacers used to increase solubility and biocompatibility and confer chemical functionality; Cell penetrating peptides: to overcome the lipophilic barrier of the cellular membranes and deliver molecules into cells using TAT peptide to achieve cytoplasm and nucleus; Quaternary ammonium: to introduce stable positively charged in gold nanoparticles surface; and RNA interference: siRNA complementary to a master regulator gene, the proto-oncogene c-Myc, that is implicated in cell growth, proliferation, loss of differentiation, and cell death. In order to establish that they are viable alternatives to the available methods, these innovative nanoparticles were extensively characterized on their chemical functionalization, ease of uptake, cellular toxicity and inflammation, and knockdown of MYC protein expression in several cancer cell lines and in in vivo models.Fundação para a Ciência e Tecnologia - (SFRH/BD/62957/2009); PTDC/BIO/66514/2006; NANOLIGHT-PTDC/QUI-QUI/112597/2009; Silencing the silencers via multifunctional gold nanoconjugates towards cancer therapy - PTDC/BBB-NAN/1812/201