Dense 3D Depth Recovery for Soft Tissue Deformation During Robotically Assisted Laparoscopic Surgery

  • Danail Stoyanov
  • Ara Darzi
  • Guang Zhong Yang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3217)


Recovering tissue deformation during robotic assisted minimally invasive surgery is an important step towards motion compensation and stabilization. This paper presents a practical strategy for dense 3D depth recovery and temporal motion tracking for deformable surfaces. The method combines image rectification with constrained disparity registration for reliable depth estimation. The accuracy and practical value of the technique is validated with a tissue phantom with known 3D geometry and motion characteristics. It has been shown that the performance of the proposed approach compares favorably against existing methods. Example results of the technique applied to in vivo robotic assisted minimally invasive surgery data are also provided.


Minimally Invasive Surgery Stereo Camera Normalize Cross Correlation Deformable Surface Stereo Image Pair 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Ballantyne, G.: Robotic surgery, telerobotic surgery, telepresence, and telementoring. In: Surgical Endoscopy, vol. 2, pp. 1389–1402. Springer, Heidelberg (2002)Google Scholar
  2. 2.
    Thakral, A., Wallace, J., Tomlin, D., Seth, N., Thakor, N.: Surgical motion adaptive robotic technology (S.M.A.R.T): taking the motion out of physiological motion. In: Niessen, W.J., Viergever, M.A. (eds.) MICCAI 2001. LNCS, vol. 2208, pp. 317–325. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  3. 3.
    Nakamura, Y., Kishi, K., Kawakami, H.: Heartbeat synchronization for robotic cardiac surgery. In: Proceedings of the 2001 IEEE International Conference on Robotics and Automation, pp. 2014–2019 (2001) Google Scholar
  4. 4.
    Gröger, M., Ortmaier, T., Sepp, W., Hirzinger, G.: Tracking local motion on the beating heart. In: Proc. of SPIE Medical Imaging Conference, vol. 4681, pp. 233–241 (2002)Google Scholar
  5. 5.
    Mourgues, F., Devernay, F., Coste- Manière, E.: 3D reconstruction of the operating field for image overlay in 3D-endoscopic surgery. In: Proceedings of International Symposium on Augmented Reality (2001)Google Scholar
  6. 6.
    Okatani, T., Deguchi, K.: Shape reconstruction from an endoscope image by shape from shading technique for a point light source at the projection centre. Computer Vision and Image Understanding 66, 119–131 (1997)CrossRefGoogle Scholar
  7. 7.
  8. 8.
    Morgues, F., Coste-Manière, È.: Flexible calibration of actuated stereoscopic endoscope for overlay in robot assisted surgery. In: Dohi, T., Kikinis, R. (eds.) MICCAI 2002. LNCS, vol. 2488, pp. 24–34. Springer, Heidelberg (2002)Google Scholar
  9. 9.
    Zhang, Z.: A flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 1330–1334 (2000)CrossRefGoogle Scholar
  10. 10.
    Fusiello, A., Trucco, E., Verri, A.: A compact algorithm for rectification of stereo pairs. Machine Vision and Applications 12, 16–22 (2000)CrossRefGoogle Scholar
  11. 11.
    Loop, C., Zhang, Z.: Computing rectifying homographies for stereo vision. In: Proceedings of Computer Vision and Pattern Recognition, pp. 125–131 (1999)Google Scholar
  12. 12.
    Brown, M., Burschka, D., Hager, G.: Advances in computational stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence 25, 993–1008 (2003)CrossRefGoogle Scholar
  13. 13.
    Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. The International Journal of Computer Vision 47, 7–42 (2002)zbMATHCrossRefGoogle Scholar
  14. 14.
    Veeser, S., Dunn, M., Yang, G.-Z.: Multiresolution image registration for two-dimensional gel electrophoresis. Proteomics 1, 856–870 (2001)CrossRefGoogle Scholar
  15. 15.
    Nocedal, J., Wright, S.: Numerical optimization. Springer, Heidelberg (1999)zbMATHCrossRefGoogle Scholar
  16. 16.
    Hartley, R., Zisserman, A.: Multiple view geometry in computer vision. Cambridge University Press, Cambridge (2000)zbMATHGoogle Scholar
  17. 17.
    Birchfield, S., Tomasi, C.: Depth discontinuities by pixel-to-pixel stereo. In: Proceedings of The 6th International Conference on Computer Vision, pp. 1073–1080. IEEE Computer Society Press, Los Alamitos (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Danail Stoyanov
    • 1
  • Ara Darzi
    • 2
  • Guang Zhong Yang
    • 1
    • 2
  1. 1.Royal Society/Wolfson Foundation Medical Image Computing LaboratoryImperial College of Science, Technology and MedicineLondonUK
  2. 2.Department of Surgical Oncology and TechnologyImperial College of Science, Technology and MedicineLondonUK

Personalised recommendations