Simultaneous Stereoscope Localization and Soft-Tissue Mapping for Minimal Invasive Surgery

  • Peter Mountney
  • Danail Stoyanov
  • Andrew Davison
  • Guang-Zhong Yang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4190)


Minimally Invasive Surgery (MIS) has recognized benefits of reduced patient trauma and recovery time. In practice, MIS procedures present a number of challenges due to the loss of 3D vision and the narrow field-of-view provided by the camera. The restricted vision can make navigation and localization within the human body a challenging task. This paper presents a robust technique for building a repeatable long term 3D map of the scene whilst recovering the camera movement based on Simultaneous Localization and Mapping (SLAM). A sequential vision only approach is adopted which provides 6 DOF camera movement that exploits the available textured surfaces and reduces reliance on strong planar structures required for range finders. The method has been validated with a simulated data set using real MIS textures, as well as in vivo MIS video sequences. The results indicate the strength of the proposed algorithm under the complex reflectance properties of the scene, and the potential for real-time application for integrating with the existing MIS hardware.


Minimally Invasive Surgery Camera Movement Ground Truth Data World Coordinate System Sequential Vision 
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.
    Keller, K., Ackerman, J.: Real-Time Structured Light Depth Extraction. In: Proc. of Three Dimensional Image Capture and Applications III SPIE, pp. 11–18 (2000)Google Scholar
  2. 2.
    Hoffman, J., Spranger, M., Gohring, D., Jungel, M.: Making Use of What You Don’t See: Negative Information in Markov Localization. In: Proc. of Intelligent Robots and Systems (2005)Google Scholar
  3. 3.
    Thrakal, A.W.J., Tomlin, D., Seth, N., Thakor, N.: Surgical Motion Adaptive Robotic Technology (Smart): 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
  4. 4.
    Chatila, R., Laumond, J.: Position Referencing and Consistent World Modeling for Mobile Robots. In: Proc. of Robotics and Automation, pp. 138–145 (1985)Google Scholar
  5. 5.
    Stoyanov, D., Darzi, A., Yang, G.-.Z.: Dense 3d Depth Recovery for Soft Tissue Deformation During Robotically Assisted Laparoscopic Surgery. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. LNCS, vol. 3217, pp. 41–48. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  6. 6.
    Burschka, D., Li, M., Ishii, M., Taylor, R.H., Hager, G.D.: Scale-Invariant Registration of Monocular Endoscopic Images to Ct-Scans for Sinus Surgery. Medical Image Analysis 9(5), 413–439 (2005)CrossRefGoogle Scholar
  7. 7.
    Davison, A.J.: Real-Time Simultaneous Localisation and Mapping with a Single Camera. In: Proc. of 9th IEEE ICCV, 1403 (2003)Google Scholar
  8. 8.
    Se, S., Jasiobedzki, P.: Instant Scene Modeler for Crime Scene Reconstruction. In: IEEE A3DISS (2005)Google Scholar
  9. 9.
    Zhang, P., Milios, E.E., Gu, J.: Vision Data Registration for Robot Self-Localization in 3D. In: Proc. of Intelligent Robots and Systems, pp. 2315–2320 (2005)Google Scholar
  10. 10.
    Shi, J., Tomasi, C.: Good Features to Track. In: Proc. of CVPR, pp. 593–600 (1994)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Peter Mountney
    • 1
  • Danail Stoyanov
    • 1
  • Andrew Davison
    • 1
  • Guang-Zhong Yang
    • 1
    • 2
  1. 1.Royal Society/Wolfson Foundation Medical Image Computing Laboratory 
  2. 2.Department of Surgical Oncology and TechnologyImperial CollegeLondonUK

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