Tracking of Instruments in Minimally Invasive Surgery for Surgical Skill Analysis

  • Stefanie Speidel
  • Michael Delles
  • Carsten Gutt
  • Rüdiger Dillmann
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4091)


Intraoperative assistance systems aim to improve the quality of the surgery and enhance the surgeon’s capabilities. Preferable would be a system which provides support depending on the surgery context and surgical skills accomplished. Therefore, the automated analysis and recognition of surgical skills during an intervention is necessary. In this paper a robust tracking of instruments in minimally invasive surgery based on endoscopic image sequences is presented. The instruments were not modified and the tracking was tested on sequences acquired during a real intervention. The generated trajectory of the instruments provides information which can be further used for surgical gesture interpretation.


Minimally Invasive Surgery Humanoid Robot Surgical Skill Endoscopic Image Robust Tracking 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Taylor, R., Stoianovici, D.: Medical Robotics in Computer-Integrated Surgery. IEEE Transactions on Robotics and Automation (2003)Google Scholar
  2. 2.
    Satava, R., Cuschieri, A., Hamdorf, J.: Metrics for objective assessment. Journal of Surgical Endoscopy (2003)Google Scholar
  3. 3.
    Lin, H., Shafran, I., Murphy, T., Okamura, A., Yuh, D., Hager, G.: Automatic Detection and Segmentation of Robot-Assisted Surgical Motions. In: Duncan, J.S., Gerig, G. (eds.) MICCAI 2005. LNCS, vol. 3749, pp. 802–810. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  4. 4.
    Rosen, J., Solazzo, M., Hannaford, B., Sinanan, M.: Objective Evaluation of Laparoscopic Skills Based on Haptic Information and Tool/Tissue Interactions. Journal of Computer Aided Surgery (2002)Google Scholar
  5. 5.
    Lo, B., Darzi, A., Yang, G.: Episode Classification for the Analysis of Tissue / Instrument Interaction with Multiple Visual Cues. In: Ellis, R.E., Peters, T.M. (eds.) MICCAI 2003. LNCS, vol. 2878, pp. 230–237. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  6. 6.
    Mayer, H., Nagy, I., Knoll, A.: Skill Transfer and Learning by Demonstration in a Realistic Scenario of Laparoscopic Surgery. In: International Conference on Humanoid Robots (2003)Google Scholar
  7. 7.
    Pardowitz, M., Zöllner, R., Dillmann, R.: Incremental Acquisition of Task Knowledge Applying Heuristic Relevance Estimation. In: International Conference on Robotics and Automation (2006)Google Scholar
  8. 8.
    Zöllner, R., Rogalla, O., Dillmann, R., Zöllner, M.: Understanding Users Intention: Programming Fine Manipulation Tasks by Demonstration. In: International Conference on Intelligent Robots and Systems (2002)Google Scholar
  9. 9.
    Vogt, F., Krüger, S., Niemann, H., Schick, C.: A System for Real-Time Endoscopic Image Enhancement. In: Ellis, R.E., Peters, T.M. (eds.) MICCAI 2003. LNCS, vol. 2879, pp. 356–363. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  10. 10.
    Guthart, G.S., Salisbury, J.K.: The intuitive telesurgery system: Overview and application. In: International Conference on Robotics and Automation (2000)Google Scholar
  11. 11.
    Phung, S., Bouzerdoum, A., Chai, D.: Skin Segmentation Using Color Pixel Classification: Analysis and Comparison. IEEE Transactions on Pattern Analysis and Machine Intelligence (2005)Google Scholar
  12. 12.
    Isard, M., Blake, A.: Condensation - conditional density propagation for visual tracking. International Journal of Computer Vision (1998)Google Scholar
  13. 13.
    Azad, P., Ude, A., Dillmann, R., Cheng, G.: A Full Body Human Motion Capture System using Particle Filtering and on-the-fly Edge Detection. In: International Conference on Humanoid Robots (2004)Google Scholar
  14. 14.
    Azad, P.: Integrating Vision Toolkit (IVT),
  15. 15.
    Deutscher, J., Blake, A., Reid, I.: Articulated body motion capture by annealed particle filtering. In: International Conference on Computer Vision and Pattern Recognition (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Stefanie Speidel
    • 1
  • Michael Delles
    • 1
  • Carsten Gutt
    • 2
  • Rüdiger Dillmann
    • 1
  1. 1.Institute of Computer Science and EngineeringUniversity of KarlsruheGermany
  2. 2.Department of General, Visceral and Accident SurgeryUniversity of HeidelbergGermany

Personalised recommendations