Skip to main content

Time-of-Flight 3-D Endoscopy

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNIP,volume 5761)

Abstract

This paper describes the first accomplishment of the Time-of-Flight (ToF) measurement principle via endoscope optics. The applicability of the approach is verified by in-vitro experiments. Off-the-shelf ToF camera sensors enable the per-pixel, on-chip, real-time, marker-less acquisition of distance information. The transfer of the emerging ToF measurement technique to endoscope optics is the basis for a new generation of ToF rigid or flexible 3-D endoscopes. No modification of the endoscope optic itself is necessary as only an enhancement of illumination unit and image sensors is necessary. The major contribution of this paper is threefold: First, the accomplishment of the ToF measurement principle via endoscope optics; second, the development and validation of a complete calibration and post-processing routine; third, accomplishment of extensive in-vitro experiments. Currently, a depth measurement precision of 0.89 mm at 20 fps with 3072 3-D points is achieved.

Keywords

  • Minimally Invasive Surgery
  • Digital Holography
  • Operation Area
  • Porcine Stomach
  • Endoscope Optic

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.

The authors gratefully acknowledge funding of the Erlangen Graduate School in Advanced Optical Technologies (SAOT) by the German National Science Foundation (DFG) in the framework of the excellence initiative.

References

  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)

    CrossRef  Google Scholar 

  2. Votanopoulos, K., Brunicardi, F., Thornby, J., Bellows, C.: Impact of three-dimensional vision in laparoscopic training. World Journal Of Surgery 32(1), 110–118 (January 2008)

    Google Scholar 

  3. Yoshida, T., Inoue, H., Hara, E., Umezawa, A., Ohtsuka, K., Endo, S., Tamegai, Y., Kashida, H., Tanaka, J., Kudo, S.: Newly developed 3D endoscopic system: preliminary experience. Endoscopy 35(2), 181–184 (2003)

    CrossRef  Google Scholar 

  4. Burschka, D., Li, M., Taylor, R., Hager, G.D.: Scale-Invariant Registratiou of Monocular Endoscopic Images to CT-Scans for Sinus Surgery. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. LNCS, vol. 3217, pp. 413–421. Springer, Heidelberg (2004)

    CrossRef  Google Scholar 

  5. Mountney, P., Stoyanov, D., Davison, A.J., Yang, G.-Z.: Simultaneous Stereoscope Localization and Soft-Tissue Mapping for Minimal Invasive Surgery. In: Larsen, R., Nielsen, M., Sporring, J. (eds.) MICCAI 2006. LNCS, vol. 4190, pp. 347–354. Springer, Heidelberg (2006)

    CrossRef  Google Scholar 

  6. Hu, M., Penney, G., Edwards, P., Figl, M., Hawkes, D.: 3D Reconstruction of Internal Organ Surfaces for Minimal Invasive Surgery. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007, Part I. LNCS, vol. 4791, pp. 68–77. Springer, Heidelberg (2007)

    CrossRef  Google Scholar 

  7. Lo, B., Scarzanella, M.V., Stoyanov, D., Yang, G.Z.: Belief Propagation for Depth Cue Fusion in Minimally Invasive Surgery. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008, Part II. LNCS, vol. 5242, pp. 104–112. Springer, Heidelberg (2008)

    CrossRef  Google Scholar 

  8. Kolenovic, E., Osten, W., Klattenhoff, R., Lai, S., von Kopylow, C., Jüptner, W.: Miniaturized Digital Holography Sensor for Distal Three-Dimensional Endoscopy. Appl. Opt. 42(25), 5167–5172 (2003)

    CrossRef  Google Scholar 

  9. Hayashibe, M., Suzuki, N., Nakamura, Y.: Laser-scan endoscope system for intraoperative geometry acquisition and surgical robot safety management. Medical Image Analysis 10(4), 509–519 (2006); Special Issue on Functional Imaging and Modelling of the Heart (FIMH 2005)

    CrossRef  Google Scholar 

  10. Hayashibe, M., Suzuki, N., Hattori, A., Nakamura, Y.: Intraoperative Fast 3D Shape Recovery of Abdominal Organs in Laparoscopy. In: Dohi, T., Kikinis, R. (eds.) MICCAI 2002. LNCS, vol. 2489, pp. 356–363. Springer, Heidelberg (2002)

    CrossRef  Google Scholar 

  11. Xu, Z., Schwarte, R., Heinol, H., Buxbaum, B., Ringbeck, T.: Smart Pixel – Photometric Mixer Device (PMD) / New System Concept of a 3D-Imaging-on-a-Chip. In: 5th International Conference on Mechatronics and Machine Vision in Practice, Nanjing, pp. 259–264 (1998)

    Google Scholar 

  12. Zhang, Z.: A Flexible New Technique For Camera Calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(11), 1330–1334 (2000)

    CrossRef  Google Scholar 

  13. Tomasi, C., Manduchi, R.: Bilateral Filtering for Gray and Color Images. In: Sixth International Conference on Computer Vision (ICCV 1998), pp. 839–846 (1998)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Penne, J. et al. (2009). Time-of-Flight 3-D Endoscopy. In: Yang, GZ., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009. MICCAI 2009. Lecture Notes in Computer Science, vol 5761. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04268-3_58

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-04268-3_58

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04267-6

  • Online ISBN: 978-3-642-04268-3

  • eBook Packages: Computer ScienceComputer Science (R0)