Deep Learning for Vascular Segmentation and Applications in Phase Contrast Tomography Imaging

Automated blood vessel segmentation is vital for biomedical imaging, as vessel changes indicate many pathologies. Still, precise segmentation is difficult due to the complexity of vascular structures, anatomical variations across patients, the scarcity of annotated public datasets, and the quality of images. We present a thorough literature review, highlighting the state of machine learning techniques across diverse organs. Our goal is to provide a foundation on the topic and identify a robust baseline model for application to vascular segmentation in a new imaging modality, Hierarchical Phase Contrast Tomography (HiP CT). Introduced in 2020 at the European Synchrotron Radiation Facility, HiP CT enables 3D imaging of complete organs at an unprecedented resolution of ca. 20mm per voxel, with the capability for localized zooms in selected regions down to 1mm per voxel without sectioning. We have created a training dataset with double annotator validated vascular data from three kidneys imaged with HiP CT in the context of the Human Organ Atlas Project. Finally, utilising the nnU Net model, we conduct experiments to assess the models performance on both familiar and unseen samples, employing vessel specific metrics. Our results show that while segmentations yielded reasonably high scores such as clDice values ranging from 0.82 to 0.88, certain errors persisted. Large vessels that collapsed due to the lack of hydrostatic pressure (HiP CT is an ex vivo technique) were segmented poorly. Moreover, decreased connectivity in finer vessels and higher segmentation errors at vessel boundaries were observed. Such errors obstruct the understanding of the structures by interrupting vascular tree connectivity. Through our review and outputs, we aim to set a benchmark for subsequent model evaluations using various modalities, especially with the HiP CT imaging database

Patient-specific in silico 3D coronary model in cardiac catheterisation laboratories

Coronary artery disease is caused by the buildup of atherosclerotic plaque in the coronary arteries, affecting the blood supply to the heart, one of the leading causes of death around the world. X-ray coronary angiography is the most common procedure for diagnosing coronary artery disease, which uses contrast material and x-rays to observe vascular lesions. With this type of procedure, blood flow in coronary arteries is viewed in real-time, making it possible to detect stenoses precisely and control percutaneous coronary interventions and stent insertions. Angiograms of coronary arteries are used to plan the necessary revascularisation procedures based on the calculation of occlusions and the affected segments. However, their interpretation in cardiac catheterisation laboratories presently relies on sequentially evaluating multiple 2D image projections, which limits measuring lesion severity, identifying the true shape of vessels, and analysing quantitative data. In silico modelling, which involves computational simulations of patient-specific data, can revolutionise interventional cardiology by providing valuable insights and optimising treatment methods. This paper explores the challenges and future directions associated with applying patient-specific in silico models in catheterisation laboratories. We discuss the implications of the lack of patient-specific in silico models and how their absence hinders the ability to accurately predict and assess the behaviour of individual patients during interventional procedures. Then, we introduce the different components of a typical patient-specific in silico model and explore the potential future directions to bridge this gap and promote the development and utilisation of patient-specific in silico models in the catheterisation laboratories

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

간 조영술을 위한 혈관 모델 기반의 국부 적응 2D-3D 정합 알고리즘 기법 연구

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2017. 2. 신영길.Two-dimensional–three-dimensional (2D–3D) registration between intra-operative 2D digital subtraction angiography (DSA) and pre-operative 3D computed tomography angiography (CTA) can be used for roadmapping purposes. However, through the projection of 3D vessels, incorrect intersections and overlaps between vessels are produced because of the complex vascular structure, which make it difficult to obtain the correct solution of 2D–3D registration. To overcome these problems, we propose a registration method that selects a suitable part of a 3D vascular structure for a given DSA image and finds the optimized solution to the partial 3D structure. The proposed algorithm can reduce the registration errors because it restricts the range of the 3D vascular structure for the registration by using only the relevant 3D vessels with the given DSA. To search for the appropriate 3D partial structure, we first construct a tree model of the 3D vascular structure and divide it into several subtrees in accordance with the connectivity. Then, the best matched subtree with the given DSA image is selected using the results from the coarse registration between each subtree and the vessels in the DSA image. Finally, a fine registration is conducted to minimize the difference between the selected subtree and the vessels of the DSA image. In experimental results obtained using 10 clinical datasets, the average distance errors in the case of the proposed method were 2.34 ± 1.94 mm. The proposed algorithm converges faster and produces more correct results than the conventional method in evaluations on patient datasets.Chapter 1 Introduction 1 1.1 Background 1 1.2 Problem statement 6 1.3 Main contributions 8 1.4 Contents organization 10 Chapter 2 Related Works 12 2.1 Overview 12 2.1.1 Definitions 14 2.1.2 Intensity-based and feature-based registration 17 2.2 Neurovascular applications 19 2.3 Liver applications 22 2.4 Cardiac applications 27 2.4.1 Rigid registration 27 2.4.2 Non-rigid registration 31 Chapter 3 3D Vascular Structure Model 33 3.1 Vessel segmentation 34 3.1.1 Overview 34 3.1.2 Vesselness filter 36 3.1.3 Vessel segmentation 39 3.2 Skeleton extraction 40 3.2.1 Overview 40 3.2.2 Skeleton extraction based on fast marching method 41 3.3 Graph construction 45 3.4 Generation of subtree structures from 3D tree model 46 Chapter 4 Locally Adaptive Registration 52 4.1 2D centerline extraction 53 4.1.1 Extraction from a single DSA image 54 4.1.2 Extraction from angiographic image sequence 55 4.2 Coarse registration for the detection of the best matched subtree 58 4.3 Fine registration with selected 3D subtree 61 Chapter 5 Experimental Results 63 5.1 Materials 63 5.2 Phantom study 65 5.3 Performance evaluation 69 5.3.1 Evaluation for a single DSA image 69 5.3.2 Evaluation for angiographic image sequence 75 5.4 Comparison with other methods 77 5.5 Parameter study 87 Chapter 6 Conclusion 90 Bibliography 92 초록 109Docto

Computer Vision Techniques for Transcatheter Intervention

Minimally invasive transcatheter technologies have demonstrated substantial promise for the diagnosis and treatment of cardiovascular diseases. For example, TAVI is an alternative to AVR for the treatment of severe aortic stenosis and TAFA is widely used for the treatment and cure of atrial fibrillation. In addition, catheter-based IVUS and OCT imaging of coronary arteries provides important information about the coronary lumen, wall and plaque characteristics. Qualitative and quantitative analysis of these cross-sectional image data will be beneficial for the evaluation and treatment of coronary artery diseases such as atherosclerosis. In all the phases (preoperative, intraoperative, and postoperative) during the transcatheter intervention procedure, computer vision techniques (e.g., image segmentation, motion tracking) have been largely applied in the field to accomplish tasks like annulus measurement, valve selection, catheter placement control, and vessel centerline extraction. This provides beneficial guidance for the clinicians in surgical planning, disease diagnosis, and treatment assessment. In this paper, we present a systematical review on these state-of-the-art methods.We aim to give a comprehensive overview for researchers in the area of computer vision on the subject of transcatheter intervention. Research in medical computing is multi-disciplinary due to its nature, and hence it is important to understand the application domain, clinical background, and imaging modality so that methods and quantitative measurements derived from analyzing the imaging data are appropriate and meaningful. We thus provide an overview on background information of transcatheter intervention procedures, as well as a review of the computer vision techniques and methodologies applied in this area

Automatic Spatiotemporal Analysis of Cardiac Image Series

RÉSUMÉ À ce jour, les maladies cardiovasculaires demeurent au premier rang des principales causes de décès en Amérique du Nord. Chez l’adulte et au sein de populations de plus en plus jeunes, la soi-disant épidémie d’obésité entraînée par certaines habitudes de vie tels que la mauvaise alimentation, le manque d’exercice et le tabagisme est lourde de conséquences pour les personnes affectées, mais aussi sur le système de santé. La principale cause de morbidité et de mortalité chez ces patients est l’athérosclérose, une accumulation de plaque à l’intérieur des vaisseaux sanguins à hautes pressions telles que les artères coronaires. Les lésions athérosclérotiques peuvent entraîner l’ischémie en bloquant la circulation sanguine et/ou en provoquant une thrombose. Cela mène souvent à de graves conséquences telles qu’un infarctus. Outre les problèmes liés à la sténose, les parois artérielles des régions criblées de plaque augmentent la rigidité des parois vasculaires, ce qui peut aggraver la condition du patient. Dans la population pédiatrique, la pathologie cardiovasculaire acquise la plus fréquente est la maladie de Kawasaki. Il s’agit d’une vasculite aigüe pouvant affecter l’intégrité structurale des parois des artères coronaires et mener à la formation d’anévrismes. Dans certains cas, ceux-ci entravent l’hémodynamie artérielle en engendrant une perfusion myocardique insuffisante et en activant la formation de thromboses. Le diagnostic de ces deux maladies coronariennes sont traditionnellement effectués à l’aide d’angiographies par fluoroscopie. Pendant ces examens paracliniques, plusieurs centaines de projections radiographiques sont acquises en séries suite à l’infusion artérielle d’un agent de contraste. Ces images révèlent la lumière des vaisseaux sanguins et la présence de lésions potentiellement pathologiques, s’il y a lieu. Parce que les séries acquises contiennent de l’information très dynamique en termes de mouvement du patient volontaire et involontaire (ex. battements cardiaques, respiration et déplacement d’organes), le clinicien base généralement son interprétation sur une seule image angiographique où des mesures géométriques sont effectuées manuellement ou semi-automatiquement par un technicien en radiologie. Bien que l’angiographie par fluoroscopie soit fréquemment utilisé partout dans le monde et souvent considéré comme l’outil de diagnostic “gold-standard” pour de nombreuses maladies vasculaires, la nature bidimensionnelle de cette modalité d’imagerie est malheureusement très limitante en termes de spécification géométrique des différentes régions pathologiques. En effet, la structure tridimensionnelle des sténoses et des anévrismes ne peut pas être pleinement appréciée en 2D car les caractéristiques observées varient selon la configuration angulaire de l’imageur. De plus, la présence de lésions affectant les artères coronaires peut ne pas refléter la véritable santé du myocarde, car des mécanismes compensatoires naturels (ex. vaisseaux----------ABSTRACT Cardiovascular disease continues to be the leading cause of death in North America. In adult and, alarmingly, ever younger populations, the so-called obesity epidemic largely driven by lifestyle factors that include poor diet, lack of exercise and smoking, incurs enormous stresses on the healthcare system. The primary cause of serious morbidity and mortality for these patients is atherosclerosis, the build up of plaque inside high pressure vessels like the coronary arteries. These lesions can lead to ischemic disease and may progress to precarious blood flow blockage or thrombosis, often with infarction or other severe consequences. Besides the stenosis-related outcomes, the arterial walls of plaque-ridden regions manifest increased stiffness, which may exacerbate negative patient prognosis. In pediatric populations, the most prevalent acquired cardiovascular pathology is Kawasaki disease. This acute vasculitis may affect the structural integrity of coronary artery walls and progress to aneurysmal lesions. These can hinder the blood flow’s hemodynamics, leading to inadequate downstream perfusion, and may activate thrombus formation which may lead to precarious prognosis. Diagnosing these two prominent coronary artery diseases is traditionally performed using fluoroscopic angiography. Several hundred serial x-ray projections are acquired during selective arterial infusion of a radiodense contrast agent, which reveals the vessels’ luminal area and possible pathological lesions. The acquired series contain highly dynamic information on voluntary and involuntary patient movement: respiration, organ displacement and heartbeat, for example. Current clinical analysis is largely limited to a single angiographic image where geometrical measures will be performed manually or semi-automatically by a radiological technician. Although widely used around the world and generally considered the gold-standard diagnosis tool for many vascular diseases, the two-dimensional nature of this imaging modality is limiting in terms of specifying the geometry of various pathological regions. Indeed, the 3D structures of stenotic or aneurysmal lesions may not be fully appreciated in 2D because their observable features are dependent on the angular configuration of the imaging gantry. Furthermore, the presence of lesions in the coronary arteries may not reflect the true health of the myocardium, as natural compensatory mechanisms may obviate the need for further intervention. In light of this, cardiac magnetic resonance perfusion imaging is increasingly gaining attention and clinical implementation, as it offers a direct assessment of myocardial tissue viability following infarction or suspected coronary artery disease. This type of modality is plagued, however, by motion similar to that present in fluoroscopic imaging. This issue predisposes clinicians to laborious manual intervention in order to align anatomical structures in sequential perfusion frames, thus hindering automation o

Detection and quantification of soft plaque in coronary arteries form MSCT image

Els mètodes de diagnòstic més actuals es basen en la inspecció visual d’escanejos MSCT amb programes software d’imatge mèdica, on el radiòleg especialista, realitza manualment els procediments per arribar a la detecció de la placa. Aquest projecte presenta un mètode semiautomàtic per detectar les plaques toves a les artèries coronàries i quantificar-ne el seu volum a partir d’un conjunt d’imatges tridimensionals MSCT del tors d’un pacient. El mètode presentat requereix una mínima aportació de l’usuari i consisteix en les següents parts: (1) extracció de la línia geomètrica central de l’artèria amb la implementació d’un algoritme de rastreig multiescala, (2) una primera segmentació de la llum de la vena per optimitzar l’extracció inicial de la línia central de l’artèria, (3) una segona segmentació de la llum i de la paret de la vena basada en la intensitat dels voxels, (4) anàlisi del volum al llarg de la vena per detectar la presència d’una placa tova, i (4) quantificació del volum de les plaques detectades. El mètode ha estat avaluat amb imatges mèdiques tridimensionals en format DICOM obtingudes de pacients reals amb l’escàner Phillips MSCT 64-slice del Rush Hospital de Chicago. El mètode proposat s’inicia amb la selecció d’una ‘llavor’ o voxel de l’escaneig, continguda dins d’una artèria coronària, amb la qual l’algoritme de rastreig partirà per resseguir la línia central de la vena seleccionada. L’objectiu d’aquesta etapa és extreure la trajectòria de l’artèria coronària dins de la imatge tridimensional per tal de poder-la segmentar. S’adopta un algoritme de rastreig eficient i ràpid basat en un filtre multiescala, que permet extreure els autovectors i autovalors de la matriu Hessiana de la imatge. A cada pas de l’algoritme, aquest proporciona una estimació del punt geomètric central de la secció transversal de l’artèria i la seva direcció local, que ens permet predir el següent punt en la trajectòria de la vena. L’inconvenient principal d’aquest algoritme és la seva precisió, al proporcionar-nos una bona aproximació a la línia central de la vena però insuficient per a obtenir una posterior segmentació de la paret de l’artèria de qualitat. Es corregeix l’extracció hessiana inicial de la línia central amb una primera segmentació EM (Estimació i Maximització) de la llum de la vena basada en un model de mescla de Gaussianes que classifica els voxels en funció de la seva intensitat. Per cada punt de la línia central inicial, es recomputa el centre de gravetat de la secció transversal que el conté i es recalculen els vectors de direcció mitjançant la matriu de Householder. Amb una línia central precisa, es pot dur a terme la segmentació acurada de la paret de la vena La segmentació de la llum i la paret de l’artèria es basa en un algoritme EM aplicat a un model de mescla de Gaussianes. Es mostra com la funció de densitat de probabilitat de la intensitat dels voxels de la imatge 3D és de distribució Gaussiana. L’objectiu d’aquesta etapa és classificar els voxels veïns dels punts de la línia central de l’artèria, podent ser aquests llum de la vena, paret de la vena o miocardi. L’algoritme iteratiu EM associa una funció de densitat de probabilitat Gaussiana a cadascun d’aquests tres components del cor, i n’estima els valors de pes , mitja iPiμ i variànçia iσ que les caracteritzen. Amb aquest coneixement estadístic, es classifiquen els voxels veïns de la línia central per segmentar la llum i la paret de la vena. Finalment, s’apliquen una sèrie de filtres morfològics per omplir forats i eliminar puntes de la segmentació. La darrera etapa és la detecció de plaques toves. Amb l’estudi dels volums normalitzats entre l’interior i la paret de la vena, s’aconsegueix detectar el tipus de placa tova que produeix l’estrenyiment de la llum. Es parteix l’artèria en segments, determinats per plans ortogonals a la línia central, es computen els voxels corresponents a la llum i a la paret entre plans consecutius i es normalitzen els dos volums amb la distància entre plans. S’analitzen els màxims i mínims de les dues corbes considerant uns llindars determinats pel coneixement previ de la mida de les plaques toves. Si es troben plaques, es quantifica el seu volum aproximat mitjançant propietats convexes amb mètode ‘convhull’