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

Nicolas Passat 1, 2 Christian Ronse 1 Joseph Baruthio 2 Jean-Paul Armspach 2 Marcel Bosc 3 Jack Foucher 4
1 LSIIT - Laboratoire des Sciences de l'Image, de l'Informatique et de la Télédétection
2 LNV - Laboratoire de Neuroimagerie in Vivo
3 L2TI - Laboratoire de Traitement et Transport de l'Information
4 Laboratoire de Psychopathologie et de Pharmacologie de la Cognition
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.
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Nicolas Passat, Christian Ronse, Joseph Baruthio, Jean-Paul Armspach, Marcel Bosc, et al.. Using multimodal MR data for segmentation and topology recovery of the cerebral superficial venous tree. International Symposium on Visual Computing (ISVC), 2005, Lake Tahoe, United States. pp.60-67, ⟨10.1007/11595755_8⟩. ⟨hal-01694970⟩

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