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Most cerebrovascular diseases (including strokes and aneurysms) are treated endovascularly with cathetersthat are navigated from the groin through the vessels to the brain. Many patients have complex anatomy of the aortic arch and supra-aortic vessels, which can make it difficult to select the best catheters for navigation, resulting in longer procedures and more complications or failures. To this end, we propose a framework dedicated to the analysis of the aortic arch and supra-aortic trunks. This framework can automatically compute anatomical and geometrical features from meshes segmented beforehand via CNN-based pipeline. These features such as arch type, tortuosity and angulations describe the navigational difficulties encountered during catheterization. Quantitative and qualitative validation was performed by experienced neuroradiologists, leading to reliable vessel characterization.

International audience; Neurovascular pathologies are often treated with the help of imaging to guide catheters inside arteries.However, positioning a micro-catheter into the aortic arch and threading it through blood vessels for embolization, mechanical thrombectomy or stenting is a challenging task.Indeed, adverse aortic arch anatomies are frequently encountered, especially when the aortic arch is dilated, or the supra-aortic branches are elongated and tortuous.In this article, we propose a pipeline using convolutional neural networks for the segmentation of the aortic arch from magnetic resonance images for further anatomy classification purpose.This pipeline is composed of two successive modules, dedicated to the localization and the accurate segmentation of the aortic arch and the origin of supra-aortic branches, respectively.These segmentations are then used to generate 3D models from which the anatomy and the type of the aortic arches can be characterized.A quantitative evaluation of this approach, carried out on various U-Net architectures and different optimizers, leads to satisfactory segmentation results, then allowing a reliable characterization.

National audience