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Online Event Classification for Liver Needle Insertion Based on Force Patterns

  • Inko Elgezua
  • Sangha Song
  • Yo Kobayashi
  • Masakatsu G. Fujie
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 302)

Abstract

In recent years percutaneous treatments for cancer have won momentum in the medical field. With it new needle insertion robots appeared to overcome the difficulties associated with needle insertion into soft tissue. At first, the main focus was to achieve high needle placement accuracy, however, the focus nowadays has shifted toward needle steering and patient specific needle tissue interaction. In this paper we present a classification method to detect the type of tissue being punctured in real time. The purpose of the proposed method is to detect particular events that can be used in a situational awareness agent. First, we will introduce the methodology to create the statistical models used for classification, next, we prove the feasibility of the proposed classification method with experimental results and show that the proposed method hit a target even when tissue is deformed by analyzing needle insertion force patterns.

Keywords

Needle insertion robot Soft tissue modeling Situation awareness 

References

  1. 1.
    International Agency for Cancer Research, World Health Organization, “Globocan 2012: Cancer Fact Sheets” http://globocan.iarc.fr/Pages/fact_sheets_cancer.aspx.
  2. 2.
    S. Shiina, “Japanese experience in ablation therapies for hepatocellular carcinoma,” Hepatol Res., Sep 2007, 37 Suppl 2:S223–9.Google Scholar
  3. 3.
    P. Abitabile, U. Hartl, J. Lange, C.A. Maurer, “Radiofrequency ablation permits an effective treatment for colorectal liver metastasis”, European Journal of Surgical Oncology (EJSO), vol. 33, Issue 1, pp. 67–71, 2007.Google Scholar
  4. 4.
    S. Oura, T. Tamaki, I. Hirai, T. Yoshimasu, F. Ohta, R. Nakamura, Y. Okamura, “Radiofrequency ablation therapy in patients with breast cancers two centimeters or less in size”, Breast cancer, p. 48–54, 2007.Google Scholar
  5. 5.
    E.M. Boctor, M.A. Choti, E.C. Burdette, R.J. Webster, “Three- dimensional ultrasound-guided robotic needle placement: An experimental evaluation,” Int. J. Med. Robot. Comput. Assist. Surg., vol. 4, no. 2, pp. 180–191, 2008.Google Scholar
  6. 6.
    L. Maier-Hein, F. Pianka, A. Seitel, S.A. Müller, A. Tekbas, M. Seitel, I. Wolf, B.M. Schmied, H.P. Meinzer, “In vivo accuracy assessment of a needle-based navigation system for CT-guided radiofrequency ablation of the liver”, Medical Physics, 35, 5385–5396, 2008.Google Scholar
  7. 7.
    S.P. DiMaio, S.E. Salcudean, “Interactive simulation of needle insertion models,” Biomedical Engineering, IEEE Transactions on , vol.52, no.7, pp.1167-1179, July 2005.CrossRefGoogle Scholar
  8. 8.
    Glozman, D.; Shoham, M.; , “Image-Guided Robotic Flexible Needle Steering,” Robotics, IEEE Transactions on , vol.23, no.3, pp.459-467, June 2007.CrossRefGoogle Scholar
  9. 9.
    Alterovitz, R.; Goldberg, K.; Okamura, A.; , “Planning for Steerable Bevel-tip Needle Insertion Through 2D Soft Tissue with Obstacles,” Robotics and Automation, 2005. pp. 1640- 1645, April 2005.Google Scholar
  10. 10.
    Kobayashi Y.; Onishi A.; Hoshi T.; Kawamura K.; Fujie M. G.; “Modeling of Conditions where a Puncture Occurs during Needle Insertion considering Probability Distribution.” The International Conference on Intelligent Robots and Systems, IROS, pp. 1433–1440, September 2008.Google Scholar
  11. 11.
    DJ van Gerwen; J Dankelman,;JJ van den Dobbelsteen; “Measurement and Stochastic Modeling of Kidney Puncture Forces”, Annals of Biomedical Engineering, Springer US, October, 2013.Google Scholar
  12. 12.
    Elgezua I., Song S., Kobayashi Y., Fujie M. G., “Event Classification in Percutaneous Treatments based on Needle Insertion Force Pattern Analysis”, The International Conference on Control, Automation and Systems, ICCAS. Gwangju, Korea, October 20–22 2013.Google Scholar
  13. 13.
    Bonfe, M.; Boriero, F.; Dodi, R.; Fiorini, P.; Morandi, A.; Muradore, R.; Pasquale, L.; Sanna, A.; Secchi, C., “Towards automated surgical robotics: A requirements engineering approach,” Biomedical Robotics and Biomechatronics (BioRob), 2012 4th IEEE RAS & EMBS International Conference on, vol., no., pp. 56–61, 24–27, 2012.Google Scholar
  14. 14.
    Muradore, R.; Bresolin, D.; Geretti, L.; Fiorini, P.; Villa, T., “Robotic Surgery,” Robotics & Automation Magazine, IEEE, vol.18, no.3, pp.24,32, Sept. 2011.CrossRefGoogle Scholar
  15. 15.
    D.C. Rucker, J. Das, H.B. Gilbert, P.J. Swaney, M.I. Miga, N. Sarkar, R.J. Webster, “Sliding Mode Control of Steerable Needles,” Robotics, IEEE Transactions on , vol.29, no.5, pp.1289,1299, Oct. 2013.CrossRefGoogle Scholar
  16. 16.
    Duda R., Hart P., Stork D, “Pattern Classification”. 2nd edition, John Wiley & Sons, 2001.Google Scholar
  17. 17.
    Okamura, A.M.; Simone, C.; O’Leary, M.D.; , “Force modeling for needle insertion into soft tissue,” Biomedical Engineering, IEEE Transactions on , vol.51, no.10, pp.1707-1716, Oct. 2004.CrossRefGoogle Scholar
  18. 18.
    Fung YC., “Biomechanics - Mechanical properties of living tissues”. 2nd edition Springer-Verlag 1993.Google Scholar
  19. 19.
    Rumack C. M., Wilson S. R., Charboneau J.W., Levine D., “Diagnostic Ultrasound”, 4th edition, Elsevier, 2011.Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Inko Elgezua
    • 1
  • Sangha Song
    • 1
  • Yo Kobayashi
    • 1
  • Masakatsu G. Fujie
    • 2
  1. 1.Graduate School of Advanced Science and EngineeringWaseda UniversityTokyoJapan
  2. 2.Faculty of Science and EngineeringWaseda UniversityTokyoJapan

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