Sparse Bayesian registration

Loïc Le Folgoc; Hervé Delingette; Antonio Criminisi; Nicholas Ayache

doi:10.1007/978-3-319-10404-1_30

Sparse Bayesian registration

Loïc Le Folgoc, Hervé Delingette, Antonio Criminisi, Nicholas Ayache

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

We propose a Sparse Bayesian framework for non-rigid registration. Our principled approach is flexible, in that it efficiently finds an optimal, sparse model to represent deformations among any preset, widely overcomplete range of basis functions. It addresses open challenges in state-of-the-art registration, such as the automatic joint estimate of model parameters (e.g. noise and regularization levels). We demonstrate the feasibility and performance of our approach on cine MR, tagged MR and 3D US cardiac images, and show state-of-the-art results on benchmark datasets evaluating accuracy of motion and strain.

Original language	English
Title of host publication	Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014 - 17th International Conference, Proceedings
Publisher	Springer Verlag
Pages	235-242
Number of pages	8
Edition	PART 1
ISBN (Print)	9783319104034
DOIs	https://doi.org/10.1007/978-3-319-10404-1_30
Publication status	Published - 1 Jan 2014
Externally published	Yes
Event	17th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014 - Boston, MA, United States Duration: 14 Sept 2014 → 18 Sept 2014

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 1
Volume	8673 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	17th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014
Country/Territory	United States
City	Boston, MA
Period	14/09/14 → 18/09/14

Access to Document

10.1007/978-3-319-10404-1_30

Cite this

Le Folgoc, L., Delingette, H., Criminisi, A., & Ayache, N. (2014). Sparse Bayesian registration. In Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014 - 17th International Conference, Proceedings (PART 1 ed., pp. 235-242). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8673 LNCS, No. PART 1). Springer Verlag. https://doi.org/10.1007/978-3-319-10404-1_30

Le Folgoc, Loïc ; Delingette, Hervé ; Criminisi, Antonio et al. / Sparse Bayesian registration. Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014 - 17th International Conference, Proceedings. PART 1. ed. Springer Verlag, 2014. pp. 235-242 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1).

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abstract = "We propose a Sparse Bayesian framework for non-rigid registration. Our principled approach is flexible, in that it efficiently finds an optimal, sparse model to represent deformations among any preset, widely overcomplete range of basis functions. It addresses open challenges in state-of-the-art registration, such as the automatic joint estimate of model parameters (e.g. noise and regularization levels). We demonstrate the feasibility and performance of our approach on cine MR, tagged MR and 3D US cardiac images, and show state-of-the-art results on benchmark datasets evaluating accuracy of motion and strain.",

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Le Folgoc, L, Delingette, H, Criminisi, A & Ayache, N 2014, Sparse Bayesian registration. in Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014 - 17th International Conference, Proceedings. PART 1 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 1, vol. 8673 LNCS, Springer Verlag, pp. 235-242, 17th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014, Boston, MA, United States, 14/09/14. https://doi.org/10.1007/978-3-319-10404-1_30

Sparse Bayesian registration. / Le Folgoc, Loïc; Delingette, Hervé; Criminisi, Antonio et al.
Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014 - 17th International Conference, Proceedings. PART 1. ed. Springer Verlag, 2014. p. 235-242 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8673 LNCS, No. PART 1).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - We propose a Sparse Bayesian framework for non-rigid registration. Our principled approach is flexible, in that it efficiently finds an optimal, sparse model to represent deformations among any preset, widely overcomplete range of basis functions. It addresses open challenges in state-of-the-art registration, such as the automatic joint estimate of model parameters (e.g. noise and regularization levels). We demonstrate the feasibility and performance of our approach on cine MR, tagged MR and 3D US cardiac images, and show state-of-the-art results on benchmark datasets evaluating accuracy of motion and strain.

AB - We propose a Sparse Bayesian framework for non-rigid registration. Our principled approach is flexible, in that it efficiently finds an optimal, sparse model to represent deformations among any preset, widely overcomplete range of basis functions. It addresses open challenges in state-of-the-art registration, such as the automatic joint estimate of model parameters (e.g. noise and regularization levels). We demonstrate the feasibility and performance of our approach on cine MR, tagged MR and 3D US cardiac images, and show state-of-the-art results on benchmark datasets evaluating accuracy of motion and strain.

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DO - 10.1007/978-3-319-10404-1_30

M3 - Conference contribution

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SN - 9783319104034

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Le Folgoc L, Delingette H, Criminisi A, Ayache N. Sparse Bayesian registration. In Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014 - 17th International Conference, Proceedings. PART 1 ed. Springer Verlag. 2014. p. 235-242. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1). doi: 10.1007/978-3-319-10404-1_30

Sparse Bayesian registration

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