Skip to main content
SpringerLink
Log in

Unsupervised Learning of Diffeomorphic Image Registration via TransMorph

  • Conference paper
  • First Online:
Biomedical Image Registration (WBIR 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13386))

Included in the following conference series:

Abstract

In this work, we propose a learning-based framework for unsupervised and end-to-end learning of diffeomorphic image registration. Specifically, the proposed network learns to produce and integrate time-dependent velocity fields in an LDDMM setting. The proposed method guarantees a diffeomorphic transformation and allows the transformation to be easily and accurately inverted. We also showed that, without explicitly imposing a diffeomorphism, the proposed network can provide a significant performance gain while preserving the spatial smoothness in the deformation. The proposed method outperforms the state-of-the-art registration methods on two widely used publicly available datasets, indicating its effectiveness for image registration. The source code of this work is available at: https://bit.ly/3EtYUFN.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 59.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 79.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Arsigny, V., Commowick, O., Pennec, X., Ayache, N.: A log-euclidean framework for statistics on diffeomorphisms. In: Larsen, R., Nielsen, M., Sporring, J. (eds.) MICCAI 2006. LNCS, vol. 4190, pp. 924–931. Springer, Heidelberg (2006). https://doi.org/10.1007/11866565_113

    Chapter  Google Scholar 

  2. Ashburner, J.: A fast diffeomorphic image registration algorithm. Neuroimage 38(1), 95–113 (2007)

    Article  Google Scholar 

  3. Avants, B.B., Epstein, C.L., Grossman, M., Gee, J.C.: Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Med. Image Anal. 12(1), 26–41 (2008)

    Article  Google Scholar 

  4. Balakrishnan, G., Zhao, A., Sabuncu, M.R., Guttag, J., Dalca, A.V.: Voxelmorph: a learning framework for deformable medical image registration. IEEE Trans. Med. Imaging 38(8), 1788–1800 (2019)

    Article  Google Scholar 

  5. Beg, M.F., Miller, M.I., Trouvé, A., Younes, L.: Computing large deformation metric mappings via geodesic flows of diffeomorphisms. Int. J. Comput. Vision 61(2), 139–157 (2005)

    Article  Google Scholar 

  6. Chen, J., Frey, E.C., He, Y., Segars, W.P., Li, Y., Du, Y.: Transmorph: transformer for unsupervised medical image registration (2021). https://arxiv.org/abs/2111.10480

  7. Chen, J., He, Y., Frey, E.C., Li, Y., Du, Y.: ViT-V-Net: vision transformer for unsupervised volumetric medical image registration. In: Medical Imaging with Deep Learning (2021)

    Google Scholar 

  8. Dalca, A.V., Balakrishnan, G., Guttag, J., Sabuncu, M.R.: Unsupervised learning of probabilistic diffeomorphic registration for images and surfaces. Med. Image Anal. 57, 226–236 (2019)

    Article  Google Scholar 

  9. Heinrich, M.P., Jenkinson, M., Brady, M., Schnabel, J.A.: MRF-based deformable registration and ventilation estimation of lung CT. IEEE Trans. Med. Imaging 32(7), 1239–1248 (2013)

    Article  Google Scholar 

  10. Hering, A., et al.: Learn2reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning. arXiv preprint arXiv:2112.04489 (2021)

  11. Hernandez, M., Bossa, M.N., Olmos, S.: Registration of anatomical images using paths of diffeomorphisms parameterized with stationary vector field flows. Int. J. Comput. Vision 85(3), 291–306 (2009)

    Article  Google Scholar 

  12. Kim, B., et al.: CycleMorph: cycle consistent unsupervised deformable image registration. Med. Image Anal. 71, 102036 (2021)

    Article  Google Scholar 

  13. Langner, O., et al.: Presentation and validation of the radboud faces database. Cogn. Emot. 24(8), 1377–1388 (2010)

    Article  Google Scholar 

  14. Marcus, D.S., Wang, T.H., Parker, J., Csernansky, J.G., Morris, J.C., Buckner, R.L.: Open access series of imaging studies (OASIS): cross-sectional MRI data in young, middle aged, nondemented, and demented older adults. J. Cogn. Neurosci. 19(9), 1498–1507 (2007)

    Article  Google Scholar 

  15. Mok, T.C., Chung, A.: Fast symmetric diffeomorphic image registration with convolutional neural networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4644–4653 (2020)

    Google Scholar 

  16. Mok, T.C.W., Chung, A.C.S.: Conditional deformable image registration with convolutional neural network. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12904, pp. 35–45. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87202-1_4

    Chapter  Google Scholar 

  17. Siebert, H., Hansen, L., Heinrich, M.P.: Fast 3d registration with accurate optimisation and little learning for learn2reg 2021. arXiv preprint arXiv:2112.03053 (2021)

  18. Vercauteren, T., Pennec, X., Perchant, A., Ayache, N.: Diffeomorphic demons: efficient non-parametric image registration. Neuroimage 45(1), S61–S72 (2009)

    Article  Google Scholar 

  19. Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)

    Article  Google Scholar 

  20. Yang, X., Kwitt, R., Styner, M., Niethammer, M.: Quicksilver: fast predictive image registration-a deep learning approach. Neuroimage 158, 378–396 (2017)

    Article  Google Scholar 

  21. Zhang, L., Zhang, L., Mou, X., Zhang, D.: FSIM: a feature similarity index for image quality assessment. IEEE Trans. Image Process. 20(8), 2378–2386 (2011)

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgment

This work was supported by a grant from the National Cancer Institute, U01-CA140204.

Author information

Authors and Affiliations

  1. Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Medical Institutes, Baltimore, MD, USA

    Junyu Chen, Eric C. Frey & Yong Du

Authors
  1. Junyu Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Eric C. Frey
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yong Du
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Junyu Chen .

Editor information

Editors and Affiliations

  1. Radboud University Nijmegen Medical Center, Nijmegen, The Netherlands

    Alessa Hering

  2. Helmholtz Center Munich, Neuherberg, Germany

    Julia Schnabel

  3. University of Virginia, Charlottesville, VA, USA

    Miaomiao Zhang

  4. CONICET, Universidad Nacional del Litoral, Santa Fe, Argentina

    Enzo Ferrante

  5. Universität zu Lübeck, Lübeck, Germany

    Mattias Heinrich

  6. GALILEO, Technical University of Munich, Munich, Germany

    Daniel Rueckert

Appendix A. Additional Qualitative Results

Appendix A. Additional Qualitative Results

Fig. 3.
figure 3

Qualitative comparisons of the deformation field smoothness. TM yielded a deformation field with noticeable folded voxels, but TM-TVF generated a smoother field with state-of-the-art registration accuracy (as seen in Tables 1 and 2). \(\text {TM-TVF}_{\textit{LDDMM}}\) generated a highly regularized deformation field with nearly no visible folded voxels.

Full size image
Fig. 4.
figure 4

Qualitative comparisons of facial expression registration. \(\text {TM-TVF}_{\textit{LDDMM}}\) produced a smooth and invertible transformation, but all other transformations were not. Additionally, \(\text {TM-TVF}\) yielded the best qualitative results for both forward and backward registration. Note that the transformation inversions for VM-2, CycleMorph, and TM were approximated using \(Id-u\), where u denotes the displacement field.

Full size image

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Chen, J., Frey, E.C., Du, Y. (2022). Unsupervised Learning of Diffeomorphic Image Registration via TransMorph. In: Hering, A., Schnabel, J., Zhang, M., Ferrante, E., Heinrich, M., Rueckert, D. (eds) Biomedical Image Registration. WBIR 2022. Lecture Notes in Computer Science, vol 13386. Springer, Cham. https://doi.org/10.1007/978-3-031-11203-4_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-11203-4_11

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-11202-7

  • Online ISBN: 978-3-031-11203-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation