Bronchoscopy Navigation beyond Electromagnetic Tracking Systems: A Novel Bronchoscope Tracking Prototype

  • Xiongbiao Luo
  • Takayuki Kitasaka
  • Kensaku Mori
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6891)


A novel bronchoscope tracking prototype was designed and validated for bronchoscopic navigation. We construct a novel mouth- or nose-piece bronchoscope model to directly measure the movement information of a bronchoscope outside of a patient’s body. Fusing the measured movement information based on sequential Monte Carlo (SMC) sampler, we exploit accurate and robust intra-operative alignment between the pre- and intra-operative image data for augmenting surgical bronchoscopy. We validate our new prototype on phantom datasets. The experimental results demonstrate that our proposed prototype is a promising approach to navigate a bronchoscope beyond EMT systems.


Ground Truth Data Orientation Error Sequential Monte Carlo Insertion Depth Virtual Bronchoscopic 
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  1. 1.
    Schwarz, Y., Greif, J., Becker, H.D., Ernst, A., Mehta, A.: Real-time electromagnetic navigation bronchoscopy to peripheral lung lesions using overlaid CT images: the first human study. Chest 129(4), 988–994 (2006)CrossRefGoogle Scholar
  2. 2.
    Soper, T.D., Haynor, D.R., Glenny, R.W., Seibel, E.J.: In vivo validation of a hybrid tracking system for navigation of an ultrathin bronchoscope within peripheral airways. IEEE TBME 57(3), 736–745 (2010)Google Scholar
  3. 3.
    Deligianni, F., Chung, A.J., Yang, G.Z.: Nonrigid 2-D/3-D registration for patient specific bronchoscopy simulation with statistical shape modeling: Phantom validation. IEEE TMI 25(11), 1462–1471 (2006)Google Scholar
  4. 4.
    Deguchi, D., Mori, K., Feuerstein, M., Kitasaka, T., Maurer Jr., C.R., Suenaga, Y., Takabatake, H., Mori, M., Natori, H.: Selective image similarity measure for bronchoscope tracking based on image registration. MedIA 13(4), 621–633 (2009)Google Scholar
  5. 5.
    Luo, X., Feuerstein, M., Kitasaka, T., Mori, K.: A novel bronchoscope tracking method for bronchoscopic navigation using a low cost optical mouse sensor. In: Wong, K.H., Holmes, D.R. (eds.) SPIE Medical Imaging 2011, Florida USA, vol. 7964, pp. 79641T (2011)Google Scholar
  6. 6.
    Isard, M., Blake, A.: Condensation - conditional density propagation for visual tracking. International Journal of Computer Vision 29(1), 5–28 (1998)CrossRefGoogle Scholar
  7. 7.
    Luo, X., Reichl, T., Feuerstein, M., Kitasaka, T., Mori, K.: Modified hybrid bronchoscope tracking based on sequential monte carlo sampler: Dynamic phantom validation. In: Kimmel, R., Klette, R., Sugimoto, A. (eds.) ACCV 2010, Part III. LNCS, vol. 6494, pp. 409–421. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  8. 8.
    Arulampalam, M., Maskell, S., Gordon, N., Clapp, T.: A tutorial on particle filters for nonlinear/non-gaussian Bayesian tracking. IEEE TSP 50(2), 174–188 (2002)Google Scholar
  9. 9.
    Schneider, M., Stevens, C.: Development and testing of a new magnetic-tracking device for image guidance. In: Cleary, K.R., Miga, M.I. (eds.) SPIE Medical Imaging 2007, California USA, vol. 6509, pp. 65090I (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Xiongbiao Luo
    • 1
  • Takayuki Kitasaka
    • 2
  • Kensaku Mori
    • 3
    • 1
  1. 1.Graduate School of Information ScienceNagoya UniversityJapan
  2. 2.Faculty of Information ScienceAichi Institute of TechnologyJapan
  3. 3.Information and Communications HeadquartersNagoya UniversityJapan

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