Fast Optimal Transport Averaging of Neuroimaging Data
Gramfort, A.; Peyré, Gabriel; Cuturi, Marco (2015), Fast Optimal Transport Averaging of Neuroimaging Data, in Sebastien Ourselin, Daniel C. Alexander, Carl-Fredrik Westin, M. Jorge Cardoso, Information Processing in Medical Imaging 24th International Conference, IPMI 2015, Sabhal Mor Ostaig, Isle of Skye, UK, June 28 - July 3, 2015, Proceedings, Springer : Berlin Heidelberg, p. 261-272. 10.1007/978-3-319-19992-4_20
TypeCommunication / Conférence
External document linkhttps://arxiv.org/abs/1503.08596v2
Book titleInformation Processing in Medical Imaging 24th International Conference, IPMI 2015, Sabhal Mor Ostaig, Isle of Skye, UK, June 28 - July 3, 2015, Proceedings
Book authorSebastien Ourselin, Daniel C. Alexander, Carl-Fredrik Westin, M. Jorge Cardoso
MetadataShow full item record
CEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
Graduate School of Informatics [Kyoto]
Abstract (EN)Knowing how the Human brain is anatomically and function-ally organized at the level of a group of healthy individuals or patientsis the primary goal of neuroimaging research. Yet computing an averageof brain imaging data de ned over a voxel grid or a triangulation re-mains a challenge. Data are large, the geometry of the brain is complexand the between subjects variability leads to spatially or temporally non-overlapping e ects of interest. To address the problem of variability, dataare commonly smoothed before performing a linear group averaging. Inthis work we build on ideas originally introduced by Kantorovich  topropose a new algorithm that can average e ciently non-normalized datade ned over arbitrary discrete domains using transportation metrics. Weshow how Kantorovich means can be linked to Wasserstein barycenters inorder to take advantage of the entropic smoothing approach used by .It leads to a smooth convex optimization problem and an algorithm withstrong convergence guarantees. We illustrate the versatility of this tooland its empirical behavior on functional neuroimaging data, functionalMRI and magnetoencephalography (MEG) source estimates, de ned onvoxel grids and triangulations of the folded cortical surface.
Subjects / KeywordsTransport of Neuroimaging Data
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Solomon, Justin; De Goes, Fernando; Peyré, Gabriel; Cuturi, Marco; Butscher, Adrian; Nguyen, Andy; Du, Tao; Guibas, Leonidas (2015) Article accepté pour publication ou publié
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