articleIEEE Transactions on Medical ImagingFeb 4, 2019GREEN OA

VoxelMorph: A Learning Framework for Deformable Medical Image Registration

GBGuha BalakrishnanAZAmy ZhaoMRMert R. SabuncuJGJohn GuttagAVAdrian V. Dalca

Massachusetts Institute of Technology · Cornell University · +1 more institution

PubMed
Indexed inarxivcrossrefpubmed

Abstract

We present VoxelMorph, a fast learning-based framework for deformable, pairwise medical image registration. Traditional registration methods optimize an objective function for each pair of images, which can be time-consuming for large datasets or rich deformation models. In contrast to this approach, and building on recent learning-based methods, we formulate registration as a function that maps an input image pair to a deformation field that aligns these images. We parameterize the function via a convolutional neural network (CNN), and optimize the parameters of the neural network on a set of images. Given a new pair of scans, VoxelMorph rapidly computes a deformation field by directly evaluating the…

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Authors

5
  • GB
    Guha BalakrishnanCorresponding

    Massachusetts Institute of Technology

  • AZ
    Amy Zhao

    Massachusetts Institute of Technology

  • MR
    Mert R. Sabuncu

    Cornell University

  • JG
    John Guttag

    Massachusetts Institute of Technology

  • AV
    Adrian V. Dalca

    Athinoula A. Martinos Center for Biomedical Imaging

Topics & keywords

Keywords
  • Image registration
  • Pairwise comparison
  • Leverage (statistics)
  • Convolutional neural network
  • Matching (statistics)
  • Medical imaging
  • Code (set theory)
  • Pattern recognition (psychology)
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