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

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

Curvilinear Structure Enhancement in Biomedical Images

Curvilinear structures can appear in many different areas and at a variety of scales. They can be axons and dendrites in the brain, blood vessels in the fundus, streets, rivers or fractures in buildings, and others. So, it is essential to study curvilinear structures in many fields such as neuroscience, biology, and cartography regarding image processing. Image processing is an important field for the help to aid in biomedical imaging especially the diagnosing the disease. Image enhancement is the early step of image analysis. In this thesis, I focus on the research, development, implementation, and validation of 2D and 3D curvilinear structure enhancement methods, recently established. The proposed methods are based on phase congruency, mathematical morphology, and tensor representation concepts. First, I have introduced a 3D contrast independent phase congruency-based enhancement approach. The obtained results demonstrate the proposed approach is robust against the contrast variations in 3D biomedical images. Second, I have proposed a new mathematical morphology-based approach called the bowler-hat transform. In this approach, I have combined the mathematical morphology with a local tensor representation of curvilinear structures in images. The bowler-hat transform is shown to give better results than comparison methods on challenging data such as retinal/fundus images. The bowler-hat transform is shown to give better results than comparison methods on challenging data such as retinal/fundus images. Especially the proposed method is quite successful while enhancing of curvilinear structures at junctions. Finally, I have extended the bowler-hat approach to the 3D version to prove the applicability, reliability, and ability of it in 3D

Detection and Physical Interaction with Deformable Linear Objects

Deformable linear objects (e.g., cables, ropes, and threads) commonly appear in our everyday lives. However, perception of these objects and the study of physical interaction with them is still a growing area. There have already been successful methods to model and track deformable linear objects. However, the number of methods that can automatically extract the initial conditions in non-trivial situations for these methods has been limited, and they have been introduced to the community only recently. On the other hand, while physical interaction with these objects has been done with ground manipulators, there have not been any studies on physical interaction and manipulation of the deformable linear object with aerial robots. This workshop describes our recent work on detecting deformable linear objects, which uses the segmentation output of the existing methods to provide the initialization required by the tracking methods automatically. It works with crossings and can fill the gaps and occlusions in the segmentation and output the model desirable for physical interaction and simulation. Then we present our work on using the method for tasks such as routing and manipulation with the ground and aerial robots. We discuss our feasibility analysis on extending the physical interaction with these objects to aerial manipulation applications.Comment: Presented at ICRA 2022 2nd Workshop on Representing and Manipulating Deformable Objects (https://deformable-workshop.github.io/icra2022/

Contrast-enhanced micro-computed tomography and image processing integrated approach for microstructural analysis of biological soft fibrous tissues

Nel sistema muscolo-scheletrico, tendini e legamenti svolgono un ruolo importante al fine di garantire mobilità e stabilità. Questi tessuti sono composti principalmente da collagene e presentano una struttura altamente fibrosa. Evidenziare i componenti della microstruttura di legamenti e tendini in immagini tridimensionali (3D) è di fondamentale importanza per estrarre informazioni significative che posso anvere ripercussioni sulla scienza di base e sulle applicazioni ortopediche. In particolare, le proprietà meccaniche delle microstrutture fibrose sono fortemente influenzate da alcune caratteristiche geometriche, come la volume fraction, l’orientamento e il diametro; tuttavia, determinare l'orientamento e il diametro della fibra 3D è impegnativo. In questa prospettiva, questa tesi mirava ad unire tomografia microcomputerizzata (microCT) ed elaborazione delle immagini in un approccio integrato al fine di identificare e migliorare le informazioni microstrutturali sui tessuti biologici fibrosi, includendo i dati di volume e orientamento. La procedura complessiva è stata applicata per la prima volta su campioni di tendine del ginocchio umano e su legamento collaterale bovino. In una prima fase, sono state testate preparazioni specifiche del campione, inclusa una disidratazione chimica o soluzioni di acido fosfotungstico (PTA) al 2 % in acqua (H2O) o in soluzione di etanolo al 70% (EtOH), così da migliorare il contrasto dell'immagine di questi specifici tessuti. Inoltre, utilizzando i dati scansionati, è stata sviluppata una nuova tecnica di elaborazione delle immagini basata sul filtro 3D hessiano multiscala per evidenziare le strutture fibrose ed ottenere informazioni quantitative sulle fibre. È interessante notare che, per qualsiasi strategia di preparazione del campione di tendini/legamenti, l'approccio proposto è risultato adeguato per rilevare e caratterizzare le proprietà del fascicolo. I risultati del test hanno mostrato che la disposizione delle fibre è fortemente allineata lungo la direzione longitudinale principale nel tendine del tendine, più delle fibre del legamento collaterale bovino. Inoltre, questa tecnica è stata ulteriormente applicata al fine di determinare come il Legamento Crociato Anteriore (LCA) umano risponda a carichi uniassiali rispetto a valori crescenti di deformazione, considerando sia un tessuto sano che uno in condizioni patologiche, cioè acquisito da un paziente con l'artrosi. Anche in questi casi, l'approccio integrato si è rivelato valido ed affidabile nell'individuare orientamento e dimensione dei fascicoli presenti e, quindi, attraverso un modello meccanico strutturale - basato su specifiche leggi costitutive - nello stimare il modulo elastico di questi tessuti. Sono state infatti stimate le curve sforzo-deformazione, ottenendo un valore di modulo elastico di 60.8 MPa e 7.7 MPa rispettivamente per il LCA sano e patologico. In conclusione, è stato progettato e validato in via preliminare un nuovo protocollo microCT per il miglioramento del contrasto dedicato all'analisi microstrutturale dei tessuti molli biologici con caratteristiche fibrose. In una peculiare applicazione al LCA, le informazioni ottenute con il protocollo sono state utilizzate per implementare un modello meccanico dei tessuti fibrosi, stimando così il comportamento biomeccanico dei tessuti sani e patologici.ABSTRACT In the musculoskeletal system, tendons and ligaments play an important role in ensuring mobility and stability. These tissues are primarily composed of collagen and present a highly fibrous structure. Highlighting the microstructure components of ligaments and tendons in three-dimensional (3D) images is crucial for extracting meaningful information impacting basic science and orthopaedic applications. In particular, the mechanical properties of the fibrous microstructures are strongly influenced by their volume fraction, orientation, and diameter. However, determining the 3D fibre orientation and diameter is challenging. In this picture, this thesis aimed at integrating microcomputed tomography (microCT) and image processing approach to identify and enhance microstructural information about biological soft fibrous tissues, including volume and orientation. The overall procedure was first applied on human hamstring tendon and bovine collateral ligament samples. In a first phase specific sample preparations – including either a chemical dehydration, or by 2% of phosphotungstic acid (PTA) in water (H2O) or in 70% ethanol (EtOH) solution – were tested to enhance image contrast of these specific soft tissues. Further, using the scanned data, a novel image processing technique based on 3D Hessian multiscale filter for highlighting fibrous structures was developed to obtain quantitative fibre information. Interestingly, for any strategy of tendon/ligament sample preparation, the proposed approach was adequate for detecting and characterizing fascicle features. The test results showed the fibre arrangement strongly aligned along the main longitudinal direction in the human hamstring tendon more than fibres on the bovine collateral ligament. Moreover, this technique was further applied in order to determine how the human Anterior Cruciate Ligament (ACL) responds to uniaxial loads with respect to increasing values of strain, considering both a healthy tissue and a one under pathological conditions, i.e., acquired from a patient with osteoarthritis. Also in these cases, the integrated approach was valuable and reliable in identifying orientation and size of present fascicles and, thus, through a structural mechanical model - based on specific constitutive law - to estimate the elastic modulus of these tissues. In fact, stress-strain curves were estimated, obtaining a value of elastic modulus of 60.8 MPa and 7.7 MPa for the healthy and pathological ACLs, respectively. In conclusion, a novel contrast enhancement microCT protocol was designed and preliminarily validated for the microstructural analysis of biological soft fibrous tissues. In a peculiar application to ACL, the information obtained with the protocol was used to implement a mechanical model of fibrous tissues, thus estimating the biomechanical behaviour of the healthy and pathological tissues

Analysis and processing of dynamic and structural magnetic resonance imaging signals for studying small vessel disease

Cerebral small vessel disease (CSVD) describes multiple and dynamic pathological processes disrupting the optimum functioning of perforating arterioles, capillaries and venules, increasing the risk of stroke and dementia. Although the pathogenesis of this disease is still elusive, the breakdown of the blood-brain barrier (BBB), which would hinder brain waste clearance, is thought to play a pivotal factor in it. Nonetheless, the microscopic origin and nature of these abnormalities and the lack of a ground truth make the study of CSVD in vivo in humans via magnetic resonance imaging (MRI) challenging and signal processing schemes likely to be sub-optimal. In this doctoral thesis, we proposed signal analysis and processing techniques to improve the quantification and characterisation of subtle and clinically relevant neuroimaging features of CSVD. We applied our proposals to analyses of structural and dynamic-contrast enhanced MRI (sMRI and DCE-MRI) to better characterise CSVD. DCE-MRI is commonly used to investigate cerebrovascular dysfunction, but the extremely subtle nature of the signal in CSVD makes it unclear whether signal changes are caused by microscopic yet critical BBB abnormalities. Moreover, ethical and safety considerations in vivo and the lack of validation frameworks hinder optimising imaging protocols and processing schemes. To cope with these issues, we thus proposed an open-source computational human brain model for mimicking the four-dimensional DCE-MRI acquisition process. With it, we quantified the substantial impact of spatiotemporal considerations on permeability mapping, detected sources of errors that had been overlooked in the past, and provided evidence of the harmful effect of post-processing or lack thereof on DCE-MRI assessments. Perivascular spaces (PVS) in the brain, which are involved in brain waste clearance, can become visible in sMRI scans of patients with neuroimaging features of CSVD, but their automatic quantification is challenging due to the size of PVS, the incidence and presence of imaging artefacts, and the lack of a ground truth. We first proposed a computational model of sMRI to study and compare current PVS segmentation techniques and identify major areas of improvement. We confirmed that optimal segmentation requires tuning depending on image quality and that motion artefacts are particularly detrimental to PVS quantification. We then proposed a processing strategy that distinguished high-quality from motion-corrupted images and processed them accordingly. We demonstrated such an approximation leads to estimates that correlate better with clinical visual scores and agree more with full manual counts. After optimisation using our proposals, we also found PVS measurements were associated with BBB permeability, in accordance with the link between brain waste clearance and endothelial dysfunction. This work provides means for understanding the effect of image acquisition and processing on the assessment of subtle markers of brain health to maximise confidence of studies of endothelial dysfunction and brain waste clearance via MRI. It also constitutes a cornerstone on which future optimisation and development can be based upon

Automatic hepatic vessels segmentation using RORPO vessel enhancement filter and 3D V-Net with variant Dice loss function

The segmentation of hepatic vessels is crucial for liver surgical planning. It is also a challenging task because of its small diameter. Hepatic vessels are often captured in images of low contrast and resolution. Our research uses filter enhancement to improve their contrast, which helps with their detection and final segmentation. We have designed a specific fusion of the Ranking Orientation Responses of Path Operators (RORPO) enhancement filter with a raw image, and we have compared it with the fusion of different enhancement filters based on Hessian eigenvectors. Additionally, we have evaluated the 3D U-Net and 3D V-Net neural networks as segmentation architectures, and have selected 3D V-Net as a better segmentation architecture in combination with the vessel enhancement technique. Furthermore, to tackle the pixel imbalance between the liver (background) and vessels (foreground), we have examined several variants of the Dice Loss functions, and have selected the Weighted Dice Loss for its performance. We have used public 3D Image Reconstruction for Comparison of Algorithm Database (3D-IRCADb) dataset, in which we have manually improved upon the annotations of vessels, since the dataset has poor-quality annotations for certain patients. The experiments demonstrate that our method achieves a mean dice score of 76.2%, which outperforms other state-of-the-art techniques.Web of Science131art. no. 54

Automating the Reconstruction of Neuron Morphological Models: the Rivulet Algorithm Suite

The automatic reconstruction of single neuron cells is essential to enable large-scale data-driven investigations in computational neuroscience. The problem remains an open challenge due to various imaging artefacts that are caused by the fundamental limits of light microscopic imaging. Few previous methods were able to generate satisfactory neuron reconstruction models automatically without human intervention. The manual tracing of neuron models is labour heavy and time-consuming, making the collection of large-scale neuron morphology database one of the major bottlenecks in morphological neuroscience. This thesis presents a suite of algorithms that are developed to target the challenge of automatically reconstructing neuron morphological models with minimum human intervention. We first propose the Rivulet algorithm that iteratively backtracks the neuron fibres from the termini points back to the soma centre. By refining many details of the Rivulet algorithm, we later propose the Rivulet2 algorithm which not only eliminates a few hyper-parameters but also improves the robustness against noisy images. A soma surface reconstruction method was also proposed to make the neuron models biologically plausible around the soma body. The tracing algorithms, including Rivulet and Rivulet2, normally need one or more hyper-parameters for segmenting the neuron body out of the noisy background. To make this pipeline fully automatic, we propose to use 2.5D neural network to train a model to enhance the curvilinear structures of the neuron fibres. The trained neural networks can quickly highlight the fibres of interests and suppress the noise points in the background for the neuron tracing algorithms. We evaluated the proposed methods in the data released by both the DIADEM and the BigNeuron challenge. The experimental results show that our proposed tracing algorithms achieve the state-of-the-art results

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