medigraphic.com

2018, Number 6

<< Back Next >>

Cir Cir 2018; 86 (6)

Orthogonal cameras system for tracking of laparoscopic instruments in training environments

Pérez-Escamirosa F, Oropesa I, Sánchez-González P, Tapia-Jurado J, Ruiz-Lizarraga J, Minor-Martínez A

Language: English
References: 35
Page: 548-555
PDF size: 502.19 Kb.


ABSTRACT

Introduction: Motion analysis is a valuable tool for assessment of psychomotor skills in laparoscopy. Nonetheless, it requires technologies for tracking the activity of the laparoscopic instruments during training. This paper presents a sensor-free system to track the movements of laparoscopic instruments based on an orthogonal camera system and video image processing. Methods: The movements of the laparoscopic instruments are tracked with two webcams placed in an orthogonal configuration. The position and orientation in the three-dimensional workspace are obtained using color markers placed on the tip of the instruments. Results: Accuracy tests show a resolution of 0.14 mm for displacement, with 1694 cm3 of total workspace, and 0.54° in the angular movements. Mean relative errors of the tracking system were ‹1%. The orthogonal cameras show high precision, linearity, and repeatability of motion recording of the laparoscopic instruments. Conclusion: The proposed system offers unconstrained manipulation of the instruments and a low-cost alternative for traditional tracking technologies.


REFERENCES

  1. Cuschieri A, Buess G, Périssat J, editors. Operative Manual of Endoscopic Surgery. Berlin, Heidelberg: Springer Berlin Heidelberg; 1992.

  2. Cuschieri A. Laparoscopic surgery: current status, issues and future developments. Surgeon. 2005;3:125-30, 132-3, 135-8.

  3. Ritter EM, Scott DJ. Design of a proficiency-based skills training curriculum for the fundamentals of laparoscopic surgery. Surg Innov. 2007; 14:107-12.

  4. Vassiliou MC, Ghitulescu GA, Feldman LS, et al. The MISTELS program to measure technical skill in laparoscopic surgery: evidence for reliability. Surg Endosc. 2006;20:744-7.

  5. Peters JH, Fried GM, Swanstrom LL, et al. Development and validation of a comprehensive program of education and assessment of the basic fundamentals of laparoscopic surgery. Surgery. 2004;135:21-7.

  6. Adrales GL, Chu UB, Hoskins JD, Witzke DB, Park AE. Development of a valid, cost-effective laparoscopic training program. Am J Surg. 2004; 187:157-63.

  7. Aggarwal R, Moorthy K, Darzi A. Laparoscopic skills training and assessment. Br J Surg. 2004;91:1549-58.

  8. Miyajima A, Hasegawa M, Takeda T, et al. How do young residents practice laparoscopic surgical skills? Urology. 2010;76:352-6.

  9. Martinez AM, Kalach AC, Espinoza DL. Millimetric laparoscopic surgery training on a physical trainer using rats. Surg Endosc. 2008;22:246-9.

  10. Martinez AM, Espinoza DL. Novel laparoscopic home trainer. Surg Laparosc Endosc Percutan Tech. 2007;17:300-2.

  11. Botden SM, Buzink SN, Schijven MP, Jakimowicz JJ. ProMIS augmented reality training of laparoscopic procedures face validity. Simul Healthc. 2008;3:97-102.

  12. Maciel A, Liu Y, Ahn W, Singh TP, Dunnican W, De S. Development of the VBLaSTTM: a virtual basic laparoscopic skill trainer. Int J Med Robot Comput Assist Surg. 2008;4:131-8.

  13. Pellen MG, Horgan LF, Barton JR, Attwood SE. Construct validity of the proMIS laparoscopic simulator. Surg Endosc. 2009;23:130-9.

  14. Van Sickle KR, McClusky DA 3rd, Gallagher AG, Smith CD. Construct validation of the proMIS simulator using a novel laparoscopic suturing task. Surg Endosc. 2005;19:1227-31.

  15. Broe D, Ridgway PF, Johnson S, Tierney S, Conlon KC. Construct validation of a novel hybrid surgical simulator. Surg Endosc. 2006;20:900-4.

  16. Botden SM, Jakimowicz JJ. What is going on in augmented reality simulation in laparoscopic surgery? Surg Endosc. 2009;23:1693-700.

  17. Hiemstra E, Terveer EM, Chmarra MK, Dankelman J, Jansen FW. Virtual reality in laparoscopic skills training: is haptic feedback replaceable? Minim Invasive Ther Allied Technol. 2011;20:179-84.

  18. Minor A, Lorias D, Ortiz S, Escamirosa F. Intelligent mechatronic system for automatically evaluating the training of the laparoscopic surgeon. In: Naik G, editor. Intelligent Mechatronics. London:InTech; 2011. p. 219-28.

  19. Sokollik C, Gross J, Buess G. New model for skills assessment and training progress in minimally invasive surgery. Surg Endosc. 2004; 18:495-500.

  20. Chmarra MK, Bakker NH, Grimbergen CA, Dankelman J. TrEndo, a device for tracking minimally invasive surgical instruments in training setups. Sensors Actuators Phys. 2006;126:328-34.

  21. Munz Y, Moorthy K, Dosis A, et al. The benefits of stereoscopic vision in robotic-assisted performance on bench models. Surg Endosc. 2004; 18:611-6.

  22. Wei GQ, Arbter K, Hirzinger G. Automatic Tracking of Laparoscopic Instruments by Color Coding. Berlin, Heidelberg: Springer; 1997. p. 357-66.

  23. Voros S, Haber GP, Menudet JF, Long JA, Cinquin P. ViKY robotic scope holder: initial clinical experience and preliminary results using instrument tracking. IEEE/ASME Trans Mechatronics. 2010;15:879-86.

  24. Voros S, Long JA, Cinquin P. Automatic detection of instruments in laparoscopic images: a first step towards high-level command of robotic endoscopic holders. Int J Rob Res. 2007;26:1173-90.

  25. Cano AM, Lamata P, Gayá F, Gómez EJ. New Methods for Video-Based Tracking of Laparoscopic Tools. Berlin, Heidelberg: In Springer; 2006. p. 142-9.

  26. Oropesa I, Sánchez-González P, Chmarra MK, et al. EVA: laparoscopic instrument tracking based on endoscopic video analysis for psychomotor skills assessment. Surg Endosc. 2013;27:1029-39.

  27. Allen BF, Kasper F, Nataneli G, Dutson E, Faloutsos P. Visual tracking of laparoscopic instruments in standard training environments. Stud Health Technol Inform. 2011;163:11-7.

  28. Brown DC. Close-range camera calibration. Photogramm Eng. 1971; 37:855-66.

  29. Zhang Z. A flexible new technique for camera calibration. IEEE Trans Pattern Anal Mach Intell. 2000;22:1330-4.

  30. Prokop RJ, Reeves AP. A survey of moment-based techniques for unoccluded object representation and recognition. CVGIP Graph Model Image Process. 1992;54:438-60.

  31. van den Boomgaard R, van Balen R. Methods for fast morphological image transforms using bitmapped binary images. CVGIP Graph Model Image Process. 1992;54:252-8.

  32. Hartley RI, Sturm P. Triangulation. Comput Vis Image Underst. 1997; 68:146-57.

  33. Hartley R, Gupta R, Chang T. Stereo from uncalibrated cameras. In: Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE Comput. Soc. Press. p. 761-4.

  34. Ulises P, Sohyung C, Shihab A. Volumetric calibration of stereo camera in visual servo based robot control. Int J Adv Robot Syst. 2009;6:5.

  35. Osborne JW, Overbay A. The power of outliers (and why researchers should always check for them). Res Eval. 2004;9:1-12.




2020     |     www.medigraphic.com