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Optimal Transport for Diffeomorphic Registration

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MICAI2017.pdf (1.822Mb)
Date
2017
Dewey
Analyse
Sujet
Diffeomorphic Registration
Conference name
Medical Image Computing and Computer Assisted Intervention − MICCAI 2017
Conference date
09-2017
Conference city
Quebec
Conference country
Canada
Book title
Medical Image Computing and Computer Assisted Intervention − MICCAI 2017
Author
Descoteaux M., Maier-Hein L., Franz A., Jannin P., Collins D., Duchesne S. (eds)
Publisher
Springer
Publisher city
Berlin Heidelberg
Year
2018
Book URL
10.1007/978-3-319-66182-7
URI
https://basepub.dauphine.fr/handle/123456789/20860
Collections
  • CEREMADE : Publications
Metadata
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Author
Feydy, Jean
62 Centre de Mathématiques et de Leurs Applications [CMLA]
66 Département de Mathématiques et Applications - ENS Paris [DMA]
Charlier, Benjamin
424479 Institut Montpelliérain Alexander Grothendieck [IMAG]
Vialard, François-Xavier
60 CEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
Peyré, Gabriel
66 Département de Mathématiques et Applications - ENS Paris [DMA]
Type
Communication / Conférence
Item number of pages
291-299
Abstract (EN)
This paper introduces the use of unbalanced optimal transport methods as a similarity measure for diffeomorphic matching of imaging data. The similarity measure is a key object in diffeomorphic registration methods that, together with the regularization on the deformation, defines the optimal deformation. Most often, these similarity measures are local or non local but simple enough to be computationally fast. We build on recent theoretical and numerical advances in optimal transport to propose fast and global similarity measures that can be used on surfaces or volumetric imaging data. This new similarity measure is computed using a fast generalized Sinkhorn algorithm. We apply this new metric in the LDDMM framework on synthetic and real data, fibres bundles and surfaces and show that better matching results are obtained.

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