Using multimodal MR data for segmentation and topology recovery of the cerebral superficial venous tree

Authors:
N. Passat;C. Ronse;J. Baruthio;J. -P. Armspach;M. Bosc;J. Foucher
Affiliations:
LSIIT, UMR 7005 CNRS-ULP, Strasbourg 1 University, France;LSIIT, UMR 7005 CNRS-ULP, Strasbourg 1 University, France;IPB-LNV, UMR 7004 CNRS-ULP, Strasbourg 1 University, France;IPB-LNV, UMR 7004 CNRS-ULP, Strasbourg 1 University, France;L2TI, EA 3043, Paris 13 University, France;INSERM U405, France
Venue:
ISVC'05 Proceedings of the First international conference on Advances in Visual Computing
Year:
2005

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Abstract

Magnetic resonance angiography (MRA) produces 3D data visualizing vascular structures by detecting the flowing blood signal. While segmentation methods generally detect vessels by only processing MRA, the proposed method uses both MRA and non-angiographic (MRI) images. It is based on the assumption that MRI provides anatomical information useful for vessel detection. This supplementary information can be used to correct the topology of the segmented vessels. Vessels are first segmented from MRA while the cortex is segmented from MRI. An algorithm, based on distance maps and topology preserving thinning, then uses both segmented structures for recovery of the missing parts of the brain superficial venous tree and removal of other vessels. This method has been performed and validated on 9 MRA/MRI data of the brain. The results show that the venous tree is correctly segmented and topologically recovered with a 84% accuracy.