A generalized learning based framework for fast brain image registration

Minjeong Kim, Guorong Wu, Pew Thian Yap, Dinggang Shen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

This paper presents a generalized learning based framework for improving both speed and accuracy of the existing deformable registration method. The key of our framework involves the utilization of a support vector regression (SVR) to learn the correlation between brain image appearances and their corresponding shape deformations to a template, for helping significantly cut down the computation cost and improve the robustness to local minima by using the learned correlation to instantly predict a good subject-specific deformation initialization for any given subject under registration. Our framework consists of three major parts: 1) training of SVR models based on the statistics of image samples and their shape deformations to capture intrinsic image-deformation correlations, 2) deformation prediction for a new subject with the trained SVR models to generate a subject-resemblance intermediate template by warping the original template with the predicted deformations, and 3) estimating of the residual deformation from the intermediate template to the subject for refined registration. Any existing deformable registration methods can be easily employed for training the SVR models and estimating the residual deformation. We have tested in this paper the two widely used deformable registration algorithms, i.e., HAMMER [1] and diffeomorphic demons [2], for demonstration of our proposed frameowrk. Experimental results show that, compared to the registration using the original methods (with no deformation prediction), our framework achieves a significant speedup (6X faster than HAMMER, and 3X faster than diffeomorphic demons), while maintaining comparable (or even slighly better) registration accuracy.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages306-314
Number of pages9
Volume6362 LNCS
EditionPART 2
DOIs
Publication statusPublished - 2010 Nov 22
Externally publishedYes
Event13th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2010 - Beijing, China
Duration: 2010 Sep 202010 Sep 24

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume6362 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other13th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2010
CountryChina
CityBeijing
Period10/9/2010/9/24

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Kim, M., Wu, G., Yap, P. T., & Shen, D. (2010). A generalized learning based framework for fast brain image registration. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (PART 2 ed., Vol. 6362 LNCS, pp. 306-314). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6362 LNCS, No. PART 2). https://doi.org/10.1007/978-3-642-15745-5_38, https://doi.org/10.1007/978-3-642-15745-5_38