A deep learning approach to 3D segmentation of brain vasculature

The segmentation of blood-vessels is an important preprocessing step for the quantitative analysis of brain vasculature. We approach the segmentation task for two-photon brain angiograms using a fully convolutional 3D deep neural network.Published versio

Aneurysm Identification in Cerebral Models with Multiview Convolutional Neural Network

Stroke is the third most common cause of death and a major contributor to long-term disability worldwide. Severe stroke is most often caused by the rupture of a cerebral aneurysm, a weakened area in a blood vessel. The detection and quantification of cerebral aneurysms are essential for the prevention and treatment of aneurysmal rupture and cerebral infarction. Here, we propose a novel aneurysm detection method in a three-dimensional (3D) cerebrovascular model based on convolutional neural networks (CNNs). The multiview method is used to obtain a sequence of 2D images on the cerebral vessel branch model. The pretrained CNN is used with transfer learning to overcome the small training sample problem. The data augmentation strategy with rotation, mirroring and flipping helps improve the performance dramatically, particularly on our small datasets. The hyperparameter of the view number is determined in the task. We have applied the labeling task on 56 3D mesh models with aneurysms (positive) and 65 models without aneurysms (negative). The average accuracy of individual projected images is 87.86%, while that of the model is 93.4% with the best view number. The framework is highly effective with quick training efficiency that can be widely extended to detect other organ anomalies

Anatomical Modeling of Cerebral Microvascular Structures: Application to Identify Biomarkers of Microstrokes

Les réseaux microvasculaires corticaux sont responsables du transport de l’oxygène et des substrats énergétiques vers les neurones. Ces réseaux réagissent dynamiquement aux demandes énergétiques lors d’une activation neuronale par le biais du couplage neurovasculaire. Afin d’élucider le rôle de la composante microvasculaire dans ce processus de couplage, l’utilisation de la modélisation in-formatique pourrait se révéler un élément clé. Cependant, la manque de méthodologies de calcul appropriées et entièrement automatisées pour modéliser et caractériser les réseaux microvasculaires reste l’un des principaux obstacles. Le développement d’une solution entièrement automatisée est donc important pour des explorations plus avancées, notamment pour quantifier l’impact des mal-formations vasculaires associées à de nombreuses maladies cérébrovasculaires. Une observation courante dans l’ensemble des troubles neurovasculaires est la formation de micro-blocages vascu-laires cérébraux (mAVC) dans les artérioles pénétrantes de la surface piale. De récents travaux ont démontré l’impact de ces événements microscopiques sur la fonction cérébrale. Par conséquent, il est d’une importance vitale de développer une approche non invasive et comparative pour identifier leur présence dans un cadre clinique. Dans cette thèse,un pipeline de traitement entièrement automatisé est proposé pour aborder le prob-lème de la modélisation anatomique microvasculaire. La méthode de modélisation consiste en un réseau de neurones entièrement convolutif pour segmenter les capillaires sanguins, un générateur de modèle de surface 3D et un algorithme de contraction de la géométrie pour produire des mod-èles graphiques vasculaires ne comportant pas de connections multiples. Une amélioration de ce pipeline est développée plus tard pour alléger l’exigence de maillage lors de la phase de représen-tation graphique. Un nouveau schéma permettant de générer un modèle de graphe est développé avec des exigences d’entrée assouplies et permettant de retenir les informations sur les rayons des vaisseaux. Il est inspiré de graphes géométriques déformants construits en respectant les morpholo-gies vasculaires au lieu de maillages de surface. Un mécanisme pour supprimer la structure initiale du graphe à chaque exécution est implémenté avec un critère de convergence pour arrêter le pro-cessus. Une phase de raffinement est introduite pour obtenir des modèles vasculaires finaux. La modélisation informatique développée est ensuite appliquée pour simuler les signatures IRM po-tentielles de mAVC, combinant le marquage de spin artériel (ASL) et l’imagerie multidirectionnelle pondérée en diffusion (DWI). L’hypothèse est basée sur des observations récentes démontrant une réorientation radiale de la microvascularisation dans la périphérie du mAVC lors de la récupéra-tion chez la souris. Des lits capillaires synthétiques, orientés aléatoirement et radialement, et des angiogrammes de tomographie par cohérence optique (OCT), acquis dans le cortex de souris (n = 5) avant et après l’induction d’une photothrombose ciblée, sont analysés. Les graphes vasculaires informatiques sont exploités dans un simulateur 3D Monte-Carlo pour caractériser la réponse par résonance magnétique (MR), tout en considérant les effets des perturbations du champ magnétique causées par la désoxyhémoglobine, et l’advection et la diffusion des spins nucléaires. Le pipeline graphique proposé est validé sur des angiographies synthétiques et réelles acquises avec différentes modalités d’imagerie. Comparé à d’autres méthodes effectuées dans le milieu de la recherche, les expériences indiquent que le schéma proposé produit des taux d’erreur géométriques et topologiques amoindris sur divers angiogrammes. L’évaluation confirme également l’efficacité de la méthode proposée en fournissant des modèles représentatifs qui capturent tous les aspects anatomiques des structures vasculaires. Ensuite, afin de trouver des signatures de mAVC basées sur le signal IRM, la modélisation vasculaire proposée est exploitée pour quantifier le rapport de perte de signal intravoxel minimal lors de l’application de plusieurs directions de gradient, à des paramètres de séquence variables avec et sans ASL. Avec l’ASL, les résultats démontrent une dif-férence significative (p <0,05) entre le signal calculé avant et 3 semaines après la photothrombose. La puissance statistique a encore augmenté (p <0,005) en utilisant des angiogrammes capturés à la semaine suivante. Sans ASL, aucun changement de signal significatif n’est trouvé. Des rapports plus élevés sont obtenus à des intensités de champ magnétique plus faibles (par exemple, B0 = 3) et une lecture TE plus courte (<16 ms). Cette étude suggère que les mAVC pourraient être carac-térisés par des séquences ASL-DWI, et fournirait les informations nécessaires pour les validations expérimentales postérieures et les futurs essais comparatifs.----------ABSTRACT Cortical microvascular networks are responsible for carrying the necessary oxygen and energy substrates to our neurons. These networks react to the dynamic energy demands during neuronal activation through the process of neurovascular coupling. A key element in elucidating the role of the microvascular component in the brain is through computational modeling. However, the lack of fully-automated computational frameworks to model and characterize these microvascular net-works remains one of the main obstacles. Developing a fully-automated solution is thus substantial for further explorations, especially to quantify the impact of cerebrovascular malformations associ-ated with many cerebrovascular diseases. A common pathogenic outcome in a set of neurovascular disorders is the formation of microstrokes, i.e., micro occlusions in penetrating arterioles descend-ing from the pial surface. Recent experiments have demonstrated the impact of these microscopic events on brain function. Hence, it is of vital importance to develop a non-invasive and translatable approach to identify their presence in a clinical setting. In this thesis, a fully automatic processing pipeline to address the problem of microvascular anatom-ical modeling is proposed. The modeling scheme consists of a fully-convolutional neural network to segment microvessels, a 3D surface model generator and a geometry contraction algorithm to produce vascular graphical models with a single connected component. An improvement on this pipeline is developed later to alleviate the requirement of water-tight surface meshes as inputs to the graphing phase. The novel graphing scheme works with relaxed input requirements and intrin-sically captures vessel radii information, based on deforming geometric graphs constructed within vascular boundaries instead of surface meshes. A mechanism to decimate the initial graph struc-ture at each run is formulated with a convergence criterion to stop the process. A refinement phase is introduced to obtain final vascular models. The developed computational modeling is then ap-plied to simulate potential MRI signatures of microstrokes, combining arterial spin labeling (ASL) and multi-directional diffusion-weighted imaging (DWI). The hypothesis is driven based on recent observations demonstrating a radial reorientation of microvasculature around the micro-infarction locus during recovery in mice. Synthetic capillary beds, randomly- and radially oriented, and op-tical coherence tomography (OCT) angiograms, acquired in the barrel cortex of mice (n=5) before and after inducing targeted photothrombosis, are analyzed. The computational vascular graphs are exploited within a 3D Monte-Carlo simulator to characterize the magnetic resonance (MR) re-sponse, encompassing the effects of magnetic field perturbations caused by deoxyhemoglobin, and the advection and diffusion of the nuclear spins. The proposed graphing pipeline is validated on both synthetic and real angiograms acquired with different imaging modalities. Compared to other efficient and state-of-the-art graphing schemes, the experiments indicate that the proposed scheme produces the lowest geometric and topological error rates on various angiograms. The evaluation also confirms the efficiency of the proposed scheme in providing representative models that capture all anatomical aspects of vascular struc-tures. Next, searching for MRI-based signatures of microstokes, the proposed vascular modeling is exploited to quantify the minimal intravoxel signal loss ratio when applying multiple gradient di-rections, at varying sequence parameters with and without ASL. With ASL, the results demonstrate a significant difference (p<0.05) between the signal-ratios computed at baseline and 3 weeks after photothrombosis. The statistical power further increased (p<0.005) using angiograms captured at week 4. Without ASL, no reliable signal change is found. Higher ratios with improved significance are achieved at low magnetic field strengths (e.g., at 3 Tesla) and shorter readout TE (<16 ms). This study suggests that microstrokes might be characterized through ASL-DWI sequences, and provides necessary insights for posterior experimental validations, and ultimately, future transla-tional trials

DEEP MOVEMENT: Deep learning of movie files for management of endovascular thrombectomy

Objectives: Treatment and outcomes of acute stroke have been revolutionised by mechanical thrombectomy. Deep learning has shown great promise in diagnostics but applications in video and interventional radiology lag behind. We aimed to develop a model that takes as input digital subtraction angiography (DSA) videos and classifies the video according to (1) the presence of large vessel occlusion (LVO), (2) the location of the occlusion, and (3) the efficacy of reperfusion. / Methods: All patients who underwent DSA for anterior circulation acute ischaemic stroke between 2012 and 2019 were included. Consecutive normal studies were included to balance classes. An external validation (EV) dataset was collected from another institution. The trained model was also used on DSA videos post mechanical thrombectomy to assess thrombectomy efficacy. / Results: In total, 1024 videos comprising 287 patients were included (44 for EV). Occlusion identification was achieved with 100% sensitivity and 91.67% specificity (EV 91.30% and 81.82%). Accuracy of location classification was 71% for ICA, 84% for M1, and 78% for M2 occlusions (EV 73, 25, and 50%). For post-thrombectomy DSA (n = 194), the model identified successful reperfusion with 100%, 88%, and 35% for ICA, M1, and M2 occlusion (EV 89, 88, and 60%). The model could also perform classification of post-intervention videos as mTICI < 3 with an AUC of 0.71. / Conclusions: Our model can successfully identify normal DSA studies from those with LVO and classify thrombectomy outcome and solve a clinical radiology problem with two temporal elements (dynamic video and pre and post intervention). / Key Points: • DEEP MOVEMENT represents a novel application of a model applied to acute stroke imaging to handle two types of temporal complexity, dynamic video and pre and post intervention. • The model takes as an input digital subtraction angiograms of the anterior cerebral circulation and classifies according to (1) the presence or absence of large vessel occlusion, (2) the location of the occlusion, and (3) the efficacy of thrombectomy. • Potential clinical utility lies in providing decision support via rapid interpretation (pre thrombectomy) and automated objective gradation of thrombectomy outcomes (post thrombectomy)

DS6, Deformation-aware Semi-supervised Learning: Application to Small Vessel Segmentation with Noisy Training Data

Blood vessels of the brain are providing the human brain with the required nutrients and oxygen. As a vulnerable part of the cerebral blood supply, pathology of small vessels can cause serious problems such as Cerebral Small Vessel Diseases (CSVD). It has also been shown that CSVD is related to neurodegeneration, such as in Alzheimer's disease. With the advancement of 7 Tesla MRI systems, higher spatial image resolution can be achieved, enabling the depiction of very small vessels in the brain. Non-Deep Learning based approaches for vessel segmentation, e.g. Frangi's vessel enhancement with subsequent thresholding are capable of segmenting medium to large vessels but often fail to segment small vessels. The sensitivity of these methods to small vessels can be increased by extensive parameter tuning or by manual corrections, albeit making them time-consuming, laborious, and not feasible for larger datasets. This paper proposes a deep learning architecture to automatically segment small vessels in 7 Tesla 3D Time-of-Flight (ToF) Magnetic Resonance Angiography (MRA) data. The algorithm was trained and evaluated on a small imperfect semi-automatically segmented dataset of only 11 subjects; using six for training, two for validation and three for testing. Deep learning model based on U-Net Multi-Scale Supervision was trained using the training subset and were made equivariant to elastic deformations in a self-supervised manner using deformation-aware learning to improve the generalisation performance. The proposed technique was evaluated quantitatively and qualitatively against the test set and achieved a dice score of 80.44$\pm$0.83. Furthermore, the result of the proposed method was compared against a selected manually segmented region (62.07 resultant dice) and has shown a considerable improvement (18.98%) with deformation-aware learning

Automatic multiscale vascular image segmentation algorithm for coronary angiography

[Abstract] Cardiovascular diseases, particularly severe stenosis, are the main cause of death in the western world. The primary method of diagnosis, considered to be the standard in the detection and quantification of stenotic lesions, is a coronary angiography. This article proposes a new automatic multiscale segmentation algorithm for the study of coronary trees that offers results comparable to the best existing semi-automatic method. According to the state-of-the-art, a representative number of coronary angiography images that ensures the generalisation capacity of the algorithm has been used. All these images were selected by clinics from an Haemodynamics Unit. An exhaustive statistical analysis was performed in terms of sensitivity, specificity and Jaccard. Algorithm improvements imply that the clinician can perform tests on the patient and, bypassing the images through the system, can verify, in that moment, the intervention of existing differences in a coronary tree from a previous test, in such a way that it could change its clinical intra-intervention criteria.Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; GRC2014/049Ministerio de Economía y Competitividad; TIN2015-70648-