VoxelMorph: A Learning Framework for Deformable Medical Image Registration

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

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 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 function. In…

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Authors

5

Topics & keywords

Keywords
  • Image registration
  • Computer science
  • Artificial intelligence
  • Pairwise comparison
  • Leverage (statistics)
  • Convolutional neural network
  • Medical imaging
  • Computer vision
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