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Deformable Image Registration Based on Similarity-Steered CNN Regression

  • Xiaohuan Cao
  • Jianhua Yang
  • Jun Zhang
  • Dong Nie
  • Minjeong Kim
  • Qian Wang
  • Dinggang ShenEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10433)

Abstract

Existing deformable registration methods require exhaustively iterative optimization, along with careful parameter tuning, to estimate the deformation field between images. Although some learning-based methods have been proposed for initiating deformation estimation, they are often template-specific and not flexible in practical use. In this paper, we propose a convolutional neural network (CNN) based regression model to directly learn the complex mapping from the input image pair (i.e., a pair of template and subject) to their corresponding deformation field. Specifically, our CNN architecture is designed in a patch-based manner to learn the complex mapping from the input patch pairs to their respective deformation field. First, the equalized active-points guided sampling strategy is introduced to facilitate accurate CNN model learning upon a limited image dataset. Then, the similarity-steered CNN architecture is designed, where we propose to add the auxiliary contextual cue, i.e., the similarity between input patches, to more directly guide the learning process. Experiments on different brain image datasets demonstrate promising registration performance based on our CNN model. Furthermore, it is found that the trained CNN model from one dataset can be successfully transferred to another dataset, although brain appearances across datasets are quite variable.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Xiaohuan Cao
    • 1
    • 2
  • Jianhua Yang
    • 1
  • Jun Zhang
    • 2
  • Dong Nie
    • 2
  • Minjeong Kim
    • 2
  • Qian Wang
    • 3
  • Dinggang Shen
    • 2
    Email author
  1. 1.School of AutomationNorthwestern Polytechnical UniversityXi’anChina
  2. 2.Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillUSA
  3. 3.School of Biomedical Engineering, Med-X Research InstituteShanghai Jiao Tong UniversityShanghaiChina

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