End-to-End Unsupervised Deformable Image Registration with a Convolutional Neural Network

  • Bob D. de Vos
  • Floris F. Berendsen
  • Max A. Viergever
  • Marius Staring
  • Ivana Išgum
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10553)

Abstract

In this work we propose a deep learning network for deformable image registration (DIRNet). The DIRNet consists of a convolutional neural network (ConvNet) regressor, a spatial transformer, and a resampler. The ConvNet analyzes a pair of fixed and moving images and outputs parameters for the spatial transformer, which generates the displacement vector field that enables the resampler to warp the moving image to the fixed image. The DIRNet is trained end-to-end by unsupervised optimization of a similarity metric between input image pairs. A trained DIRNet can be applied to perform registration on unseen image pairs in one pass, thus non-iteratively. Evaluation was performed with registration of images of handwritten digits (MNIST) and cardiac cine MR scans (Sunnybrook Cardiac Data). The results demonstrate that registration with DIRNet is as accurate as a conventional deformable image registration method with short execution times.

Keywords

Deep learning Deformable image registration Convolution neural network Spatial transformer Cardiac cine MRI 

Notes

Acknowledgments

This study was funded by the Netherlands Organization for Scientific Research (NWO): project 12726.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Bob D. de Vos
    • 1
  • Floris F. Berendsen
    • 2
  • Max A. Viergever
    • 1
  • Marius Staring
    • 2
  • Ivana Išgum
    • 1
  1. 1.Image Sciences InstituteUniversity Medical Center UtrechtUtrechtThe Netherlands
  2. 2.Division of Image ProcessingLeiden University Medical CenterLeidenThe Netherlands

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