Spatially-Varying Metric Learning for Diffeomorphic Image Registration: A Variational Framework
Vialard, François-Xavier; Risser, Laurent (2014), Spatially-Varying Metric Learning for Diffeomorphic Image Registration: A Variational Framework, International Conference on Medical Image Computing and Computer-Assisted Intervention: MICCAI 2014, Springer : Berlin Heidelberg, p. 227-234. 10.1007/978-3-319-10404-1_29
Type
Communication / ConférenceDate
2014Conference title
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014 17th International ConferenceConference date
2014-09Conference city
BostonConference country
United StatesBook title
International Conference on Medical Image Computing and Computer-Assisted Intervention: MICCAI 2014Publisher
Springer
Published in
Berlin Heidelberg
ISBN
978-3-319-10403-4
Number of pages
826Pages
227-234
Publication identifier
Metadata
Show full item recordAuthor(s)
Vialard, François-XavierCEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
Risser, Laurent
Institut de Mathématiques de Toulouse UMR5219 [IMT]
Abstract (EN)
This paper introduces a variational strategy to learn spatially-varying metrics on large groups of images, in the Large Deformation Diffeomorphic Metric Mapping (LDDMM) framework. Spatially-varying metrics we learn not only favor local deformations but also correlated deformations in different image regions and in different directions. In addition, metric parameters can be efficiently estimated using a gradient descent method. We first describe the general strategy and then show how to use it on 3D medical images with reasonable computational ressources. Our method is assessed on the 3D brain images of the LPBA40 dataset. Results are compared with ANTS-SyN and LDDMM with spatially-homogeneous metrics.Subjects / Keywords
Image Registration; Dimensionality Reduction Method; Grid Step Size; Simple Gradient Descent; Target OverlapRelated items
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