Abstract
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.
This work was supported by the ANR DEMOS grant, the Chaire Havas-Dauphine Économie des nouvelles données, and the AO1 grant MALAC3D from Université Paul Sabatier. The authors also thank the reviewers for their constructive and insightful comments.
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Keywords
- Image Registration
- Dimensionality Reduction Method
- Grid Step Size
- Simple Gradient Descent
- Target Overlap
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Vialard, FX., Risser, L. (2014). Spatially-Varying Metric Learning for Diffeomorphic Image Registration: A Variational Framework. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014. MICCAI 2014. Lecture Notes in Computer Science, vol 8673. Springer, Cham. https://doi.org/10.1007/978-3-319-10404-1_29
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DOI: https://doi.org/10.1007/978-3-319-10404-1_29
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