Skip to main content

Self-Supervised Domain Adaptation for Patient-Specific, Real-Time Tissue Tracking

Part of the Lecture Notes in Computer Science book series (LNIP,volume 12263)


Estimating tissue motion is crucial to provide automatic motion stabilization and guidance during surgery. However, endoscopic images often lack distinctive features and fine tissue deformation can only be captured with dense tracking methods like optical flow. To achieve high accuracy at high processing rates, we propose fine-tuning of a fast optical flow model to an unlabeled patient-specific image domain. We adopt multiple strategies to achieve unsupervised fine-tuning. First, we utilize a teacher-student approach to transfer knowledge from a slow but accurate teacher model to a fast student model. Secondly, we develop self-supervised tasks where the model is encouraged to learn from different but related examples. Comparisons with out-of-the-box models show that our method achieves significantly better results. Our experiments uncover the effects of different task combinations. We demonstrate that unsupervised fine-tuning can improve the performance of CNN-based tissue tracking and opens up a promising future direction.


  • Patient-specific models
  • Motion estimation
  • Endoscopic surgery

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

Buying options

USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-59716-0_6
  • Chapter length: 11 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
USD   109.00
Price excludes VAT (USA)
  • ISBN: 978-3-030-59716-0
  • 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   149.99
Price excludes VAT (USA)
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.


  1. 1.

    Training samples should at best be identical to application samples. We therefore also propose to obtain training samples directly prior to the surgical intervention in the operation room. Intra-operative training time was on average 15 min.


  1. Armin, M.A., Barnes, N., Khan, S., Liu, M., Grimpen, F., Salvado, O.: Unsupervised learning of endoscopy video frames’ correspondences from global and local transformation. In: Stoyanov, D., et al. (eds.) CARE/CLIP/OR 2.0/ISIC -2018. LNCS, vol. 11041, pp. 108–117. Springer, Cham (2018).

    CrossRef  Google Scholar 

  2. Buciluă, C., Caruana, R., Niculescu-Mizil, A.: Model compression. In: ACM SIGKDD, pp. 535–541 (2006)

    Google Scholar 

  3. Butler, D.J., Wulff, J., Stanley, G.B., Black, M.J.: A naturalistic open source movie for optical flow evaluation. In: IEEE ECCV, pp. 611–625 (2012).

  4. Doersch, C., Zisserman, A.: Multi-task self-supervised visual learning. In: IEEE ICCV, pp. 2051–2060 (2017)

    Google Scholar 

  5. Dosovitskiy, A., et al.: FlowNet: learning optical flow with convolutional networks. In: IEEE ICCV (2015).

  6. French, G., Mackiewicz, M., Fisher, M.: Self-ensembling for visual domain adaptation. arXiv:1706.05208 (2017)

  7. Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? The Kitti vision benchmark suite, pp. 3354–3361, May 2012.

  8. Giannarou, S., Visentini-Scarzanella, M., Yang, G.Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE TPAMI 35(1), 130–143 (2013).

    CrossRef  Google Scholar 

  9. Guerre, A., Lamard, M., Conze, P.H., Cochener, B., Quellec, G.: Optical flow estimation in ocular endoscopy videos using flownet on simulated endoscopy data. In: IEEE International Symposium on Biomedical Imaging (ISBI), pp. 1463–1466 (2018).

  10. Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. arXiv:1503.02531 (2015)

  11. Horn, B.K.P., Schunck, B.G.: Determining optical flow. Artif. Intell. 17, 185–203 (1981).

    CrossRef  Google Scholar 

  12. Ihler, S., Laves, M.H., Ortmaier, T.: Patient-specific domain adaptation for fast optical flow based on teacher-student knowledge transfer. arXiv:2007.04928 (2020)

  13. Ilg, E., Mayer, N., Saikia, T., Keuper, M., Dosovitskiy, A., Brox, T.: FlowNet 2.0: evolution of optical flow estimation with deep networks. In: IEEE CVPR, July 2017.

  14. Liu, P., King, I., Lyu, M.R., Xu, J.: DDFlow: learning optical flow with unlabeled data distillation. In: AAAI, vol. 33, pp. 8770–8777 (2019).

  15. Meister, S., Hur, J., Roth, S.: UnFlow: unsupervised learning of optical flow with a bidirectional census loss. In: AAAI, New Orleans, Louisiana, pp. 7251–7259, February 2018. arXiv:1711.07837

  16. Menze, M., Geiger, A.: Object scene flow for autonomous vehicles. In: IEEE CVPR (2015).

  17. Mountney, P., Stoyanov, D., Yang, G.: Three-dimensional tissue deformation recovery and tracking. IEEE Signal Process. Mag. 27(4), 14–24 (2010).

    CrossRef  Google Scholar 

  18. Reda, F., Pottorff, R., Barker, J., Catanzaro, B.: flownet2-pytorch: pytorch implementation of flownet 2.0: evolution of optical flow estimation with deep networks (2017).

  19. Sun, D., Roth, S., Black, M.J.: Secrets of optical flow estimation and their principles. In: IEEE CVPR, pp. 2432–2439, June 2010.

  20. Sun, D., Yang, X., Liu, M.Y., Kautz, J.: Models matter, so does training: an empirical study of CNNs for optical flow estimation. arXiv:1809.05571 (2018)

  21. Sun, D., Yang, X., Liu, M.Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: IEEE CVPR, pp. 8934–8943 (2018).

  22. Wulff, J., Black, M.J.: Efficient sparse-to-dense optical flow estimation using a learned basis and layers. In: IEEE CVPR, pp. 120–130 (2015).

  23. Yip, M.C., Lowe, D.G., Salcudean, S.E., Rohling, R.N., Nguan, C.Y.: Tissue tracking and registration for image-guided surgery. IEEE Trans. Med. Imaging 31(11), 2169–2182 (2012).

    CrossRef  Google Scholar 

  24. Yu, J.J., Harley, A.W., Derpanis, K.G.: Back to basics: unsupervised learning of optical flow via brightness constancy and motion smoothness. In: Hua, G., Jégou, H. (eds.) ECCV 2016. LNCS, vol. 9915, pp. 3–10. Springer, Cham (2016).

    CrossRef  Google Scholar 

Download references


This work has received funding from the European Union as being part of the EFRE OPhonLas project.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Sontje Ihler .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Ihler, S., Kuhnke, F., Laves, MH., Ortmaier, T. (2020). Self-Supervised Domain Adaptation for Patient-Specific, Real-Time Tissue Tracking. In: , et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12263. Springer, Cham.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-59715-3

  • Online ISBN: 978-3-030-59716-0

  • eBook Packages: Computer ScienceComputer Science (R0)