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Cerebral vascular atlas generation for anatomical knowledge modeling and segmentation purpose

Abstract : Magnetic resonance angiography (MRA) is currently used for cerebral flowing blood visualization. Many segmentation methods have been proposed for brain vessel segmentation, in order to help analyzing the huge data (generally more than 10^7 voxels) provided by MRA acquisitions. Recently, a new family of segmentation algorithms, involving high level anatomical knowledge, has been studied. These new algorithms require a way to model and store this knowledge. An efficient and general approach to reach that goal consists in using atlases. In this paper a method is proposed to create vascular atlases of the brain, containing information useful for vessel segmentation purpose. This atlas creation process, designed for phase-contrast MRA (PC-MRA), is composed of four steps: segmentation, quantification, registration and data fusion. It uses a region-growing algorithm for vessel segmentation, a skeleton and vessel size determination algorithm, based on discrete geometry, for determination of quantitative properties, and a topology preserving non-rigid registration method to fuse the information. This method, which has been applied to a 18 PC-MRA database, enables to create vascular atlases containing information on brain vessels position, density, size and orientation. The generated atlases are essentially devoted to segmentation purpose but can also be used for anatomical description or pathology detection.
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Nicolas Passat, Christian Ronse, Joseph Baruthio, Jean-Paul Armspach, Claude Maillot. Cerebral vascular atlas generation for anatomical knowledge modeling and segmentation purpose. Computer Vision and Pattern Recognition (CVPR), 2005, San Diego, United States. pp.331-337, ⟨10.1109/CVPR.2005.97⟩. ⟨hal-01694969⟩



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