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Compact tracking of surgical instruments through structured markers

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Abstract

Virtual and augmented reality surgery calls for reliable and efficient tracking of the surgical instruments in the virtual or real operating theatre. The most diffused approach uses three or more not aligned markers, attached to each instrument and surveyed by a set of cameras. However, the structure required to carry the markers does modify the instrument’s mass distribution and can interfere with surgeon movements. To overcome these problems, we propose here a new methodology, based on structured markers, to compute the six degrees of freedom of a surgical instrument. Two markers are attached on the instrument axis and one of them has a stripe painted over its surface. We also introduce a procedure to compute with high accuracy the markers center on the cameras image, even when partially occluded by the instrument’s axis or by other structures. Experimental results demonstrate the reliability and accuracy of the proposed approach. The introduction of structured passive markers can open new possibilities to accurate tracking, combining markers detection with real-time image processing.

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References

  1. Ahn SJ, Rauh W, Warnecke H (2001) Least-squares orthogonal distances fitting of circle, sphere, ellipse, hyperbola, and parabola. Patt Recogn 34:2283–2303

    Article  Google Scholar 

  2. Borghese NA, Ferrigno G, Pedotti A (1990) An algorithm for 3D automatic movement analysis means of standard TV cameras. IEEE Trans Biomed Eng 37:1221–1225

    Article  PubMed  CAS  Google Scholar 

  3. Calrontech website, http://www.clarontech.com/measurement_demos.php. Accessed 8 Oct 2012

  4. Cappozzo A, Leo T, Pedotti A (1975) A general computing method for the analysis of human locomotion. J Biomech 8:307–320

    Article  PubMed  CAS  Google Scholar 

  5. Chen TC, Chung KL (2001) An efficient randomized algorithm for detecting circles. Comput Vision Image Underst 83(2):172–191

    Article  Google Scholar 

  6. Chernov N, Lesor C (2005) Least squares fitting of circles. J Math Image Vision 23:239–252

    Article  Google Scholar 

  7. Chiari L, Croce U, Leardini A, Cappozzo A (2005) Human movement analysis using stereophotogrammetry. Part 2: instrumental errors. Gait Posture 21(2):197–211

    Article  PubMed  Google Scholar 

  8. Colquhoum C (2007) Flexible image guided surgery marker. US Patent 2007(0055232):A1

    Google Scholar 

  9. Comaniciu D, Meer P (2002) Mean shift: a robust approach towards feature space analysis. IEEE Trans PAMI 24(5):603–615

    Article  Google Scholar 

  10. Faugeras O (1993) Three dimensional computer vision. MIT Press, Cambridge

    Google Scholar 

  11. Frosio I, Borghese NA (2008) Real-time accurate circle fitting with occlusions. Patt Recogn 41(3):1041–1055

    Article  Google Scholar 

  12. Frosio I, Pedersini F, Borghese NA (2009) Autocalibration of MEMS accelerometers. IEEE Trans Instr Meas 58(6):2034–2041

    Article  Google Scholar 

  13. Frosio I, Alzati A, Bertolini M, Turrini C, Borghese NA (2012) Linear pose estimate from corresponding conics. Pattern Recogn 45(12):4169–4181

    Article  Google Scholar 

  14. Haque S, Hrinivasan S (2006) A meta-analysis of the training effectiveness of virtual reality surgical simulators. IEEE Trans Inf Tech Biomed 10(1):51–58

    Article  Google Scholar 

  15. Hartley R, Zisserman A (2003) Multiple view Geometry, 2nd edn. Cambridge University Press, Cambridge

  16. Hong J, Matsumoto N, Ouchida R, Komune S, Hashizume M (2009) Medical navigation system for otologic surgery based on hybrid registration and virtual intraoperative computed tomography. IEEE Trans Biom Eng 56(2):426–432

    Article  Google Scholar 

  17. Kosaka A, Saito A, Shibasaki T, Asano T, Matsuzaki H, Furuhashi Y (2004) Three dimensional position and sensing system, US Patent 6,724,930B1

  18. Lanz O (2006) Approximate Bayesian multi-body tracking. IEEE Trans Pattern Anal Mach Intell 28(9):1–14

    Article  Google Scholar 

  19. Northern Digital webstite, http://www.ndigital.com. Accessed 8 Oct 2012

  20. Novotny PM, Stoll JA, Vasilyev NV, del Nido PJ, Dupont PE, Howe RD (2007) GPU based real-time instrument tracking with three dimensional ultrasound. Med Image Anal 11(5):458–464

    Article  PubMed  Google Scholar 

  21. Patruno F, Aliverti A, Dellacà RL, Burns D, Pedotti A (2005) Redundant system of passive markers for ultrasound scanhead tracking. IEEE Trans Biomed Eng 52(1):88–96

    Article  PubMed  Google Scholar 

  22. Rabuffetti M, Crenna P, Leardini A (2008) Quantitative comparison of five current protocols in gait analysis. Gait Posture 28(2):207–216

    Article  PubMed  Google Scholar 

  23. Winter D (2009) Biomechanics and motor control of human movement, 4th edn. Wiley, Chichester

    Book  Google Scholar 

  24. Wolf PR (2000) Elements of photogrammetry, 3rd edn. McGrawHill, New York

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Correspondence to N. Alberto Borghese.

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Alberto Borghese, N., Frosio, I. Compact tracking of surgical instruments through structured markers. Med Biol Eng Comput 51, 823–833 (2013). https://doi.org/10.1007/s11517-013-1052-7

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  • DOI: https://doi.org/10.1007/s11517-013-1052-7

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