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

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Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support (DLMIA 2017, ML-CDS 2017)

Part of the book series: Lecture Notes in Computer Science ((LNIP,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.

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Notes

  1. 1.

    http://deeplearning.net/software/theano/ (version 0.8.2).

  2. 2.

    https://lasagne.readthedocs.io/en/latest/ (version 0.2.dev1).

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Acknowledgments

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

Author information

Authors and Affiliations

  1. Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands

    Bob D. de Vos, Max A. Viergever & Ivana Išgum

  2. Division of Image Processing, Leiden University Medical Center, Leiden, The Netherlands

    Floris F. Berendsen & Marius Staring

Authors
  1. Bob D. de Vos
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  2. Floris F. Berendsen
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  3. Max A. Viergever
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  4. Marius Staring
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  5. Ivana Išgum
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Corresponding author

Correspondence to Bob D. de Vos .

Editor information

Editors and Affiliations

  1. University College London, London, United Kingdom

    M. Jorge Cardoso

  2. McGill University, Montreal, Québec, Canada

    Tal Arbel

  3. University of Adelaide, Adelaide, South Australia, Australia

    Gustavo Carneiro

  4. IBM Research - Almaden, San Jose, California, USA

    Tanveer Syeda-Mahmood

  5. Universidade do Porto, Porto, Portugal

    João Manuel R.S. Tavares

  6. IBM Research - Almaden, San Jose, USA

    Mehdi Moradi

  7. University of Queensland, Brisbane, Queensland, Australia

    Andrew Bradley

  8. Tel Aviv University, Tel Aviv, Israel

    Hayit Greenspan

  9. Universidade Estadual Paulista, Bauru, Brazil

    João Paulo Papa

  10. Case Western Reserve University, Cleveland, Ohio, USA

    Anant Madabhushi

  11. Instituto Superior Técnico, Lisboa, Portugal

    Jacinto C. Nascimento

  12. Universidade do Porto, Porto, Portugal

    Jaime S. Cardoso

  13. University of Oxford, Oxford, United Kingdom

    Vasileios Belagiannis

  14. University of South Australia, Adelaide, South Australia, Australia

    Zhi Lu

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de Vos, B.D., Berendsen, F.F., Viergever, M.A., Staring, M., Išgum, I. (2017). End-to-End Unsupervised Deformable Image Registration with a Convolutional Neural Network. In: Cardoso, M., et al. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support . DLMIA ML-CDS 2017 2017. Lecture Notes in Computer Science(), vol 10553. Springer, Cham. https://doi.org/10.1007/978-3-319-67558-9_24

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  • DOI: https://doi.org/10.1007/978-3-319-67558-9_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67557-2

  • Online ISBN: 978-3-319-67558-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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