Right generalized cylinder model for vascular segmentation

International audienceA cylindrical geometric model, the Right Generalized Model (RGC), is presented in this chapter. This model follows the definition given by Binford. The model can define complex (as well as simple) cylinders with few parameters, and it could be used to construct digital phantoms, simulate vascular shapes, create 3D models to train physicians or, even, guide a vessel tracking algorithm to segment vessel from 3D vascular images. The vessel tracking algorithm is presented together with the strategy to compute RGC models from bi-dimensional contours

Fast 3D pre-segmentation of arteries in computed tomography angiograms

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Algorithm for blood-vessel segmentation in 3D images based on a right generalized cylinder model: application to carotid arteries

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Angiographic image processing to detect and quantify arterial lesions

National audienceLes maladies cardiovasculaires étant la première cause de mortalité dans le monde, l'imagerie médicale vasculaire est depuis longtemps mise à contribution pour apporter des éléments clés au diagnostic et au choix thérapeutique. La notion d'angiographie est traditionnellement associée à l'imagerie de la lumière vasculaire, car les recommandations thérapeutiques reposent principalement sur le degré de rétrécissement de cette lumière, source de complications hémodynamiques. Cependant, un intérêt croissant est porté à la structure, voire aux propriétés biomécaniques locales, de la paroi vasculaire, afin d'évaluer la vulnérabilité de cette paroi, source d'événements aigus tels que AVC et l'infarctus du myocarde. La constante amélioration des différentes modalités d'imagerie est contre-balancée par la quête de la réduction des doses de rayonnement ionisant et/ou des temps d'acquisition. Par conséquent, restent d'actualité les problèmes de bruit, d'irrégularité de contrastes, d'artéfacts d'acquisition, etc., et les techniques de segmentation doivent constamment s'adapter aux nouvelles caractéristiques d'images, aux différents territoires anatomiques, aux nouveaux besoins en termes d'application, tout en faisant face à une quantité croissante de données à traiter, due à la résolution spatiale de plus en plus fine et à la généralisation de la dimension temporelle. Pour ce faire, les travaux s'appuient souvent sur des modèles explicites, géométriques et/ou anatomiques, et/ou sur des techniques d'apprentissage automatique, particulièrement utiles dans le contexte de la détection d'anomalies. Dans mon exposé, je passerai rapidement en revue les différents aspects du traitement d'images vasculaires, en les illustrant par des exemples de travaux réalisés avec mes collaborateurs

Models, algorithms and applications in vascular image segmentation

https://www.creatis.insa-lyon.fr/intranet/docid/Biblio_ID/articles/Orkisz_MGV_2008.pdf articl

Modelling of flow diverter & CFD

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Segmentation and quantification of blood vessels in 3d images using a right generalized cylinder state model

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An interactive coronary centerline extraction framework for CTA image analysis

International audienceComputed tomography angiography (CTA) images are increasingly used to diagnose coronary artery disease. Various (semi-) automatic image processing techniques can assist the physicians in the detection and quantification of the lesions. Extraction of the artery centerline is a key to "unroll" the artery and generate a curved planar reformation (CPR) display, which is very helpful to visually assess the arteries. The centerline is also frequently used as input to subsequent segmentation and/or detection stages. Method: The method requires the artery endpoints (ostium and distal locations) as input. These points are manually located by the user (radiologist). The coronary centerline between these points is automatically extracted using a modified minimal cost path approach (Dijsktra). The cost is calculated from the vesselness map computed on the original CTA image. This map measures the probability of a voxel to be inside the vessel lumen and uses the image gradient norm along concentric rays in order to find the best radius that models the vessel as a cylinder. A graphical user-friendly interface is provided to put the seed points and to assess the extracted centerline, in 2D and 3D visualizations. Evaluation: Eight datasets from the Rotterdam Coronary Artery Algorithm Evaluation Framework were used. Four measures were addressed: overlap (OV), overlap until first error (OF), overlap clinically relevant (OT) and the average accuracy inside the vessel (AI). An 84.3% overlap with expert human manual annotations was achieved, until the first failure (OF) 65.3%, in clinically relevant segments (radius > 1.5 mm, OT) 84.4%. In terms of accuracy within the vessel (AI) an average of 0.41 mm was obtained. Conclusion: The framework showed robust and accurate centerline extractions in multi-vendor datasets. Additional filters and cost functions can be easily added to it in order to extend the framework capabilities

Fast marching contours for the segmentation of vessel lumen in CTA cross-sections

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