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EnglishBrain connectivity studies aim at describing the connections within the brain. Diffusion and functional MRI techniques provide different kinds of information to understand brain connectivity non-invasively. Fiber tract segmentation is the task of identifying pathways of neuronal axons connecting different brain areas from MRI data. In this work we propose a method to investigate the role of both diffusion and functional MRI data for supervised tract segmentation based on learning the pairwise relationships between streamlines. Experiments on real data demonstrate the promise of the approach.
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| Titolo: | Brain Connectivity Analysis by Reduction to Pair Classification |
| Autori: | |
| Data di pubblicazione: | 2010 |
| Abstract: | Brain connectivity studies aim at describing the connections within the brain. Diffusion and functional MRI techniques provide different kinds of information to understand brain connectivity non-invasively. Fiber tract segmentation is the task of identifying pathways of neuronal axons connecting different brain areas from MRI data. In this work we propose a method to investigate the role of both diffusion and functional MRI data for supervised tract segmentation based on learning the pairwise relationships between streamlines. Experiments on real data demonstrate the promise of the approach. |
| Handle: | http://hdl.handle.net/11582/6328 |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
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