Skip to main content

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

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

Keywords

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

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-319-67558-9_24
  • Chapter length: 9 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   59.99
Price excludes VAT (USA)
  • ISBN: 978-3-319-67558-9
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   79.99
Price excludes VAT (USA)
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.

Notes

  1. 1.

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

  2. 2.

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

References

  1. Clevert, D.A., Unterthiner, T., Hochreiter, S.: Fast and accurate deep network learning by exponential linear units (ELUs). In: ICLR (2016)

    Google Scholar 

  2. Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: PMLR, vol. 37, pp. 448–456 (2015)

    Google Scholar 

  3. Jaderberg, M., Simonyan, K., Zisserman, A., Kavukcuoglu, K.: Spatial transformer networks. In: Cortes, C., Lawrence, N.D., Lee, D.D., Sugiyama, M., Garnett, R. (eds.) Advances in Neural Information Processing Systems 28, pp. 2017–2025. Curran Associates, Inc., Red Hook (2015)

    Google Scholar 

  4. Kingma, D., Ba, J.: Adam: a method for stochastic optimization. In: ICLR (2015)

    Google Scholar 

  5. LeCun, Y., Cortes, C.: The MNIST database of handwritten digits (1998)

    Google Scholar 

  6. Liao, R., Miao, S., de Tournemire, P., Grbic, S., Kamen, A., Mansi, T., Comaniciu, D.: An artificial agent for robust image registration. arXiv preprint arXiv:1611.10336 (2016)

  7. Marstal, K., Berendsen, F., Staring, M., Klein, S.: SimpleElastix: a user-friendly, multi-lingual library for medical image registration. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (2016)

    Google Scholar 

  8. Miao, S., Wang, Z.J., Liao, R.: A CNN regression approach for real-time 2D/3D registration. IEEE Trans. Med. Imaging 35(5), 1352–1363 (2016)

    CrossRef  Google Scholar 

  9. Radau, P., Lu, Y., Connelly, K., Paul, G., Dick, A., Wright, G.: Evaluation framework for algorithms segmenting short axis cardiac MRI (2009)

    Google Scholar 

  10. Rueckert, D., Sonoda, L.I., Hayes, C., Hill, D.L.G., Leach, M.O., Hawkes, D.J.: Nonrigid registration using free-form deformations: application to breast MR images. IEEE Trans. Med. Imaging 18(8), 712–721 (1999)

    CrossRef  Google Scholar 

  11. Wu, G., Kim, M., Wang, Q., Munsell, B.C., Shen, D.: Scalable high performance image registration framework by unsupervised deep feature representations learning. IEEE Trans. Biomed. Eng. 63(7), 1505–1516 (2016)

    CrossRef  Google Scholar 

Download references

Acknowledgments

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

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Bob D. de Vos .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

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: , 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

Download citation

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

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67557-2

  • Online ISBN: 978-3-319-67558-9

  • eBook Packages: Computer ScienceComputer Science (R0)