Soft Tissue Tracking for Minimally Invasive Surgery: Learning Local Deformation Online

  • Peter Mountney
  • Guang-Zhong Yang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5242)


Accurate estimation and tracking of dynamic tissue deformation is important to motion compensation, intra-operative surgical guidance and navigation in minimally invasive surgery. Current approaches to tissue deformation tracking are generally based on machine vision techniques for natural scenes which are not well suited to MIS because tissue deformation cannot be easily modeled by using ad hoc representations. Such techniques do not deal well with inter-reflection changes and may be susceptible to instrument occlusion. The purpose of this paper is to present an online learning based feature tracking method suitable for in vivo applications. It makes no assumptions about the type of image transformations and visual characteristics, and is updated continuously as the tracking progresses. The performance of the algorithm is compared with existing tracking algorithms and validated on simulated, as well as in vivo cardiovascular and abdominal MIS data. The strength of the algorithm in dealing with drift and occlusion is validated and the practical value of the method is demonstrated by decoupling cardiac and respiratory motion in robotic assisted surgery.


Feature tracking matching tissue deformation 

Supplementary material

Electronic Supplementary Material 1 (14,031 KB)

Electronic Supplementary Material 2 (19,048 KB)


  1. 1.
    Wengert, C., Bossard, L., Häberling, A., Baur, C., Székely, G., Cattin, P.C.: Endoscopic Navigation for Minimally Invasive Suturing. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007, Part II. LNCS, vol. 4792, pp. 620–627. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  2. 2.
    Ortmaier, T., Groger, M., Boehm, D.H., Falk, V., Hirzinger, G.: Motion Estimation in Beating Heart Surgery. IEEE Trans. on Biomedical Engineering (52), 1729–1740 (2005)Google Scholar
  3. 3.
    Mountney, P., Lo, B.P.L., Thiemjarus, S., Stoyanov, D., Yang, G.-Z.: A Probabilistic Framework for Tracking Deformable Soft Tissue in Minimally Invasive Surgery. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007, Part II. LNCS, vol. 4792, pp. 34–41. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  4. 4.
    Mylonas, G., Stoyanov, D., Deligianni, F., Darzi, A., Yang, G.-Z.: Gaze-contingent soft tissue deformation tracking for minimally invasive robotic surgery. In: Duncan, J.S., Gerig, G. (eds.) MICCAI 2005. LNCS, vol. 3749, pp. 843–850. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  5. 5.
    Comaniciu, D., Ramesh, V., Meer, P.: Kernel-Based Object Tracking. IEEE Trans. on Pattern Analysis and Machine Intelligence (25), 564–577 (2003)Google Scholar
  6. 6.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision (60), 91–110 (2004)Google Scholar
  7. 7.
    Lucas, B.D., Kanade, T.: An Iterative Image Registration Technique with an Application to Stereo Vision. In: Proc. IJCAI, pp. 674–679 (1981)Google Scholar
  8. 8.
    Amit, Y., Geman, D.: Shape Quantization and Recognition with Randomized Trees. Neural Computation 9(7), 1545–1588 (1997)CrossRefGoogle Scholar
  9. 9.
    Lepetit, V., Lagger, P., Fua, P.: Randomized trees for real-time keypoint recognition. In: Proc CVPR, vol. (2), pp. 775–781 (2005)Google Scholar
  10. 10.
    Rosten, E., Drummond, T.: Machine learning for high-speed corner detection. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 430–443. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  11. 11.
    Shi, J., Tomasi, C.: Good Features to Track. In: Proc of CVPR, pp. 593–600 (1994)Google Scholar
  12. 12.
    Quinlan, J.R.: Induction of decision trees. Machine Learning 1 (1986)Google Scholar
  13. 13.
    Collins, R., Liu, Y., Leordeanu, M.: On-Line Selection of Discriminative Tracking Features. IEEE Trans Pattern Analysis and Machine Intelligence 10(27), 1631–1643 (2005)CrossRefGoogle Scholar
  14. 14.
    Imperial College Visual Information Processing In: Vivo database,

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Peter Mountney
    • 1
    • 2
  • Guang-Zhong Yang
    • 1
    • 2
  1. 1.Department of ComputingImperial CollegeLondonUK
  2. 2.Institute of Biomedical EngineeringImperial CollegeLondonUK

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