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Conference papers
Atlas-based method for segmentation of cerebral vascular trees from phase-contrast magnetic resonance angiography
Abstract : Phase-contrast magnetic resonance angiography (PC-MRA) can produce phase images which are 3-dimensional pictures of vascular structures.
However, it also provides magnitude images, containing anatomical - but no vascular - data. Classically, algorithms dedicated to PC-MRA segmentation detect the cerebral vascular tree by only working on phase images. We propose here a new approach for segmentation of cerebral blood vessels in PC-MRA using both types of images. This approach is based on the hypothesis that a magnitude image contains anatomical information useful for vascular structures detection. That information can then be transposed from a normal case to any patient image by image registration.
An atlas of the whole head has been developed in order to store such anatomical knowledge. It divides a magnitude image into several ``vascular areas'', each one having specific vessel properties. The atlas can be applied on any magnitude image of an entire or nearly entire head by deformable matching, thus helping to segment blood vessels from the associated phase image. The segmentation method used afterwards is composed of a topology-conserving region growing algorithm using adaptative threshold values depending on the current region of the atlas. This algorithm builds the arterial and venous trees by iteratively adding voxels which are selected according to their greyscale value and the variation of values in their neighborhood. The topology conservation is guaranteed by only selecting simple points during the growing process. The method has been performed on 15 PC-MRA's of the brain. The results have been validated using MIP and 3D surface rendering visualization; a comparison to other results obtained without an atlas proves that atlas-based methods are an effective way to optimize vascular segmentation strategies.
https://hal.univ-reims.fr/hal-01694728
Contributor : Nicolas Passat <>
Submitted on : Wednesday, January 31, 2018 - 5:00:53 PM
Last modification on : Thursday, July 19, 2018 - 3:34:01 PM
Long-term archiving on: : Friday, May 25, 2018 - 9:03:48 PM
Contributor : Nicolas Passat <>
Submitted on : Wednesday, January 31, 2018 - 5:00:53 PM
Last modification on : Thursday, July 19, 2018 - 3:34:01 PM
Long-term archiving on: : Friday, May 25, 2018 - 9:03:48 PM
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Passat MI 2004.pdf
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