Skip to main content
Springer Nature Link
Log in

D-net: Siamese Based Network for Arbitrarily Oriented Volume Alignment

  • Conference paper
  • First Online:
Shape in Medical Imaging (ShapeMI 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12474))

Included in the following conference series:

Abstract

Alignment of contrast and non contrast-enhanced imaging is essential for quantification of changes in several biomedical applications. In particular, the extraction of cartilage shape from contrast-enhanced Computed Tomography (CT) of tibiae requires accurate alignment of the bone, currently performed manually. Existing deep learning-based methods for alignment require a common template or are limited in rotation range. Therefore, we present a novel network, D-net, to estimate arbitrary rotation and translation between 3D CT scans that additionally does not require a prior template. D-net is an extension to the branched Siamese encoder-decoder structure connected by new mutual, non-local links, which efficiently capture long-range connections of similar features between two branches. The 3D supervised network is trained and validated using preclinical CT scans of mouse tibiae with and without contrast enhancement in cartilage. The presented results show a significant improvement in the estimation of CT alignment, outperforming the current comparable methods.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /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

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Baiker, M., Staring, M., Löwik, C.W.G.M., Reiber, J.H.C., Lelieveldt, B.P.F.: Automated registration of whole-body follow-up MicroCT data of mice. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011. LNCS, vol. 6892, pp. 516–523. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-23629-7_63

    Chapter  Google Scholar 

  2. Chee, E., Wu, Z.: Airnet: self-supervised affine registration for 3d medical images using neural networks. arXiv preprint arXiv:1810.02583 (2018)

  3. Dunnhofer, M., et al.: Siam-U-Net: encoder-decoder siamese network for knee cartilage tracking in ultrasound images. Med. Image Anal. 60, 101631 (2020)

    Article  Google Scholar 

  4. Haskins, G., et al.: Learning deep similarity metric for 3D MR-TRUS image registration. Int. J. Comput. Assist. Radiol. Surg. 14(3), 417–425 (2019)

    Article  Google Scholar 

  5. Haskins, G., Kruger, U., Yan, P.: Deep learning in medical image registration: a survey. Mach. Vis. Appl. 31(1), 8 (2020)

    Article  Google Scholar 

  6. Hu, Y., et al.: Label-driven weakly-supervised learning for multimodal deformable image registration. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp. 1070–1074. IEEE (2018)

    Google Scholar 

  7. Kwon, D., et al.: Siamese U-net with healthy template for accurate segmentation of intracranial hemorrhage. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11766, pp. 848–855. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32248-9_94

    Chapter  Google Scholar 

  8. Liao, R., et al.: An artificial agent for robust image registration. In: Thirty-First AAAI Conference on Artificial Intelligence (2017)

    Google Scholar 

  9. Lim, N.H., Fowkes, M.M.: Radiopaque compound containing diiodotyrosine, 5 Jun 2019, EU Patent EP3490614A1

    Google Scholar 

  10. Ma, K., et al.: Multimodal image registration with deep context reinforcement learning. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10433, pp. 240–248. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66182-7_28

    Chapter  Google Scholar 

  11. Ou, Y., Sotiras, A., Paragios, N., Davatzikos, C.: DRAMMS: deformable registration via attribute matching and mutual-saliency weighting. Med. Image Anal. 15(4), 622–639 (2011)

    Article  Google Scholar 

  12. Papież, B.W., Szmul, A., Grau, V., Brady, J.M., Schnabel, J.A.: Non-local graph-based regularization for deformable image registration. In: Müller, H., et al. (eds.) MICCAI 2019. LNCS, vol. 11766, pp. 199–207. Springer, Cham (2016). https://doi.org/10.1007/978-3-030-32248-9_94

    Chapter  Google Scholar 

  13. Salehi, S.S.M., Khan, S., Erdogmus, D., Gholipour, A.: Real-time deep pose estimation with geodesic loss for image-to-template rigid registration. IEEE TMI 38(2), 470–481 (2018)

    Google Scholar 

  14. Schnabel, J.A., Heinrich, M.P., Papież, B.W., Brady, J.M.: Advances and challenges in deformable image registration: from image fusion to complex motion modelling. Med. Image Anal. 33, 145–148 (2016)

    Article  Google Scholar 

  15. Kwon, D., et al.: Siamese U-net with healthy template for accurate segmentation of intracranial hemorrhage. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11766, pp. 848–855. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32248-9_94

    Chapter  Google Scholar 

  16. Sloan, J.M., Goatman, K.A., Siebert, J.P.: Learning rigid image registration-utilizing convolutional neural networks for medical image registration. In: 11th International Joint Conference on Biomedical Engineering Systems and Technologies, pp. 89–99. SCITEPRESS-Science and Technology Publications (2018)

    Google Scholar 

  17. de Vos, B.D., Berendsen, F.F., Viergever, M.A., Sokooti, H., Staring, M., Išgum, I.: A deep learning framework for unsupervised affine and deformable image registration. Med. Image Anal. 52, 128–143 (2019)

    Article  Google Scholar 

  18. Wang, C., Papanastasiou, G., Chartsias, A., Jacenkow, G., Tsaftaris, S.A., Zhang, H.: FIRE: unsupervised bi-directional inter-modality registration using deep networks. arXiv preprint arXiv:1907.05062 (2019)

  19. Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7794–7803 (2018)

    Google Scholar 

  20. Zhou, Y., Barnes, C., Lu, J., Yang, J., Li, H.: On the continuity of rotation representations in neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5745–5753 (2019)

    Google Scholar 

Download references

Acknowledgement

This work was supported by a Kennedy Trust for Rheumatology Research Studentship, the Centre for OA Pathogenesis Versus Arthritits (Versus Arthritis grant 21621). The authors acknowledge Patricia das Neves Borges as the researcher who collected the preclinical CT dataset, as part of the National Centre for Replacement, Refinement and Reduction of Animals in Research (NC3R grant NC/M000141/1). B. W. Papież acknowledges Rutherford Fund at Health Data Research UK

Author information

Authors and Affiliations

  1. Kennedy Institute of Rheumatology, Nuffield Department of Rheumatology, Orthapaedics and Musculoskeletal Sciences, University of Oxford, Oxford, UK

    Jian-Qing Zheng & Ngee Han Lim

  2. Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK

    Bartłomiej W. Papież

Authors
  1. Jian-Qing Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ngee Han Lim
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bartłomiej W. Papież
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jian-Qing Zheng .

Editor information

Editors and Affiliations

  1. Harvard Medical School, Boston, USA

    Martin Reuter

  2. Klinikum der Universität München, LMU München, München, Germany

    Christian Wachinger

  3. Inria Sophia-Antipolis, Valbonne, France

    Hervé Lombaert

  4. University of North Carolina at Chapel Hill, Kitware Inc., Carrboro, NC, USA

    Beatriz Paniagua

  5. ETH Zurich, Zürich, Switzerland

    Orcun Goksel

  6. Istanbul Technical University, Istanbul, Turkey

    Islem Rekik

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zheng, JQ., Lim, N.H., Papież, B.W. (2020). D-net: Siamese Based Network for Arbitrarily Oriented Volume Alignment. In: Reuter, M., Wachinger, C., Lombaert, H., Paniagua, B., Goksel, O., Rekik, I. (eds) Shape in Medical Imaging. ShapeMI 2020. Lecture Notes in Computer Science(), vol 12474. Springer, Cham. https://doi.org/10.1007/978-3-030-61056-2_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-61056-2_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-61055-5

  • Online ISBN: 978-3-030-61056-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Societies and partnerships