Skip to main content

FSDiffReg: Feature-Wise and Score-Wise Diffusion-Guided Unsupervised Deformable Image Registration for Cardiac Images

  • Conference paper
  • First Online:
Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 (MICCAI 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14229))

  • 4185 Accesses

Abstract

Unsupervised deformable image registration is one of the challenging tasks in medical imaging. Obtaining a high-quality deformation field while preserving deformation topology remains demanding amid a series of deep-learning-based solutions. Meanwhile, the diffusion model’s latent feature space shows potential in modeling the deformation semantics. To fully exploit the diffusion model’s ability to guide the registration task, we present two modules: Feature-wise Diffusion-Guided Module (FDG) and Score-wise Diffusion-Guided Module (SDG). Specifically, FDG uses the diffusion model’s multi-scale semantic features to guide the generation of the deformation field. SDG uses the diffusion score to guide the optimization process for preserving deformation topology with barely any additional computation. Experiment results on the 3D medical cardiac image registration task validate our model’s ability to provide refined deformation fields with preserved topology effectively. Code is available at: https://github.com/xmed-lab/FSDiffReg.git.

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

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 139.99
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

Similar content being viewed by others

References

  1. Alam, F., Rahman, S.U., Ullah, S., Gulati, K.: Medical image registration in image guided surgery: issues, challenges and research opportunities. Biocybern. Biomed. Eng. 38(1), 71–89 (2018)

    Article  Google Scholar 

  2. 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 

  3. 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 

  4. Baranchuk, D., Voynov, A., Rubachev, I., Khrulkov, V., Babenko, A.: Label-efficient semantic segmentation with diffusion models. In: International Conference on Learning Representations (2022). https://openreview.net/forum?id=SlxSY2UZQT

  5. Bernard, O., et al.: Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved? IEEE Trans. Med. Imaging 37(11), 2514–2525 (2018)

    Article  Google Scholar 

  6. Che, T., et al.: AMNet: adaptive multi-level network for deformable registration of 3D brain MR images. Med. Image Anal. 85, 102740 (2023)

    Article  Google Scholar 

  7. 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 

  8. Giger, M.L., Karssemeijer, N., Schnabel, J.A.: Breast image analysis for risk assessment, detection, diagnosis, and treatment of cancer. Annu. Rev. Biomed. Eng. 15, 327–357 (2013)

    Article  Google Scholar 

  9. Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. In: Advances in Neural Information Processing Systems, vol. 33, pp. 6840–6851 (2020)

    Google Scholar 

  10. Huang, Y., Ahmad, S., Fan, J., Shen, D., Yap, P.T.: Difficulty-aware hierarchical convolutional neural networks for deformable registration of brain MR images. Med. Image Anal. 67, 101817 (2021)

    Article  Google Scholar 

  11. Jain, M., Rai, C., Jain, J., Gambhir, D.: Amalgamation of machine learning and slice-by-slice registration of MRI for early prognosis of cognitive decline. Int. J. Adv. Comput. Sci. Appl. 12(1) (2021)

    Google Scholar 

  12. Khalil, A., Ng, S.C., Liew, Y.M., Lai, K.W.: An overview on image registration techniques for cardiac diagnosis and treatment. Cardiol. Res. Pract. 2018 (2018)

    Google Scholar 

  13. Kim, B., Han, I., Ye, J.C.: DiffuseMorph: unsupervised deformable image registration using diffusion model. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13691, pp. 347–364. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-19821-2_20

    Chapter  Google Scholar 

  14. Kim, B., Kim, D.H., Park, S.H., Kim, J., Lee, J.G., Ye, J.C.: CycleMorph: cycle consistent unsupervised deformable image registration. Med. Image Anal. 71, 102036 (2021)

    Article  Google Scholar 

  15. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)

  16. Klein, S., Staring, M., Murphy, K., Viergever, M.A., Pluim, J.P.: Elastix: a toolbox for intensity-based medical image registration. IEEE Trans. Med. Imaging 29(1), 196–205 (2009)

    Article  Google Scholar 

  17. Krebs, J., Mansi, T., Mailhé, B., Ayache, N., Delingette, H.: Unsupervised probabilistic deformation modeling for robust diffeomorphic registration. In: Stoyanov, D., et al. (eds.) DLMIA/ML-CDS 2018. LNCS, vol. 11045, pp. 101–109. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00889-5_12

    Chapter  Google Scholar 

  18. Kwon, M., Jeong, J., Uh, Y.: Diffusion models already have a semantic latent space. In: The Eleventh International Conference on Learning Representations (2023). https://openreview.net/forum?id=pd1P2eUBVfq

  19. 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 

  20. Mok, T.C.W., Chung, A.C.S.: Large deformation diffeomorphic image registration with Laplacian pyramid networks. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12263, pp. 211–221. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59716-0_21

    Chapter  Google Scholar 

  21. Shen, Z., Zhang, M., Zhao, H., Yi, S., Li, H.: Efficient attention: attention with linear complexities. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 3531–3539 (2021)

    Google Scholar 

  22. Song, Y., Sohl-Dickstein, J., Kingma, D.P., Kumar, A., Ermon, S., Poole, B.: Score-based generative modeling through stochastic differential equations. arXiv preprint arXiv:2011.13456 (2020)

  23. Tumanyan, N., Geyer, M., Bagon, S., Dekel, T.: Plug-and-play diffusion features for text-driven image-to-image translation. arXiv preprint arXiv:2211.12572 (2022)

Download references

Acknowledgements

This work was supported by the Hong Kong Innovation and Technology Fund under Project ITS/030/21 & PRP/041/22FX, as well as by Foshan HKUST Projects under Grants FSUST21-HKUST10E and FSUST21-HKUST11E.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Xiaomeng Li .

Editor information

Editors and Affiliations

1 Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (pdf 49 KB)

Rights and permissions

Reprints and permissions

Copyright information

© 2023 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

Qin, Y., Li, X. (2023). FSDiffReg: Feature-Wise and Score-Wise Diffusion-Guided Unsupervised Deformable Image Registration for Cardiac Images. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14229. Springer, Cham. https://doi.org/10.1007/978-3-031-43999-5_62

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-43999-5_62

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-43998-8

  • Online ISBN: 978-3-031-43999-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics