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• W4280489573 abstract "An accurate detection of intracranial aneurysms is of paramount importance for a timely diagnosis and a possible treatment. Indeed, intracranial aneurysms (ICA) need to be detected at an early stage, and their evolution must be closely monitored before any treatment becomes hazardous. Numerous methods have been proposed to detect ICA either on Digital Subtraction Angiography (DSA) on Computed Tomography Angiography (CTA), or Magnetic Resonance Angiography (MRA) Time-Of-Flight (TOF) modalities. In the present study, we are particularly interested in the saccular ICA occurring onto the vascular tree's bifurcations, and we specifically focus our research on MRA-TOF acquisitions. We propose a synthetic model of both the artery bifurcation and the aneurysm itself. We are able to very accurately model some vasculature bifurcations as they are represented on TOF acquisitions. Their geometrical disposition, the various background noises and the aneurysm's shapes and positions are rigorously reproduced. The purpose of this approach is to alleviate the burden of a ground-truth manual segmentation commonly required when using deep-learning for object detection or semantic segmentation. Our model is highly configurable and intends to produce vast datasets used to feed a Convolutional Neural Network (CNN) for the automatic detection and segmentation of the saccular ICAs. In this preliminary study we only intend to propose a model for 3D aneurysm-bearing bifurcations. Evidently, a thorough evaluation of the model's accuracy is conducted. A preliminary experiment was conducted on a reduced dataset in order to assess the applicability of our bifurcation model. In future works, we will enhance the bifurcation model and propose an in-depth evaluation via Deep Learning methods." @default.
• W4280489573 created "2022-05-22" @default.
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• W4280489573 date "2022-08-21" @default.
• W4280489573 modified "2023-10-18" @default.
• W4280489573 title "Toward a 3D Arterial Tree Bifurcation Model for Intra-Cranial Aneurysm Detection and Segmentation" @default.
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• W4280489573 doi "https://doi.org/10.1109/icpr56361.2022.9956393" @default.
• W4280489573 hasPublicationYear "2022" @default.
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