Surgical Endoscopy

, Volume 27, Issue 3, pp 1029–1039

EVA: Laparoscopic Instrument Tracking Based on Endoscopic Video Analysis for Psychomotor Skills Assessment

  • Ignacio Oropesa
  • Patricia Sánchez-González
  • Magdalena K. Chmarra
  • Pablo Lamata
  • Álvaro Fernández
  • Juan A. Sánchez-Margallo
  • Frank Willem Jansen
  • Jenny Dankelman
  • Francisco M. Sánchez-Margallo
  • Enrique J. Gómez
New Technology

Abstract

Introduction

The EVA (Endoscopic Video Analysis) tracking system is a new system for extracting motions of laparoscopic instruments based on nonobtrusive video tracking. The feasibility of using EVA in laparoscopic settings has been tested in a box trainer setup.

Methods

EVA makes use of an algorithm that employs information of the laparoscopic instrument’s shaft edges in the image, the instrument’s insertion point, and the camera’s optical center to track the three-dimensional position of the instrument tip. A validation study of EVA comprised a comparison of the measurements achieved with EVA and the TrEndo tracking system. To this end, 42 participants (16 novices, 22 residents, and 4 experts) were asked to perform a peg transfer task in a box trainer. Ten motion-based metrics were used to assess their performance.

Results

Construct validation of the EVA has been obtained for seven motion-based metrics. Concurrent validation revealed that there is a strong correlation between the results obtained by EVA and the TrEndo for metrics, such as path length (ρ = 0.97), average speed (ρ = 0.94), or economy of volume (ρ = 0.85), proving the viability of EVA.

Conclusions

EVA has been successfully validated in a box trainer setup, showing the potential of endoscopic video analysis to assess laparoscopic psychomotor skills. The results encourage further implementation of video tracking in training setups and image-guided surgery.

Keywords

Minimally invasive surgery EVA TrEndo Training Motion analysis Video 

References

  1. 1.
    Cuschieri A (2005) Laparoscopic surgery: current status, issues and future developments. Surgeon 3:125–130PubMedCrossRefGoogle Scholar
  2. 2.
    Aggarwal R, Moorthy K, Darzi A (2004) Laparoscopic skills training and assessment. Br J Surg 91:1549–1558PubMedCrossRefGoogle Scholar
  3. 3.
    Harden RM, Stevenson M, Downie WW, Wilson GM (1975) Assessment of clinical competence using objective structured examination. Br Med J 1:447–451PubMedCrossRefGoogle Scholar
  4. 4.
    van Sickle KR, Ritter EM, McClusky DA 3rd, Lederman A, Baghai M, Gallagher AG, Smith CD (2007) Attempted establishment of proficiency levels for laparoscopic performance on a national scale using simulation: the results from the 2004 SAGES minimally invasive surgical trainer-virtual reality (MIST-VR) learning center study. Surg Endosc 21:5–10PubMedCrossRefGoogle Scholar
  5. 5.
    Usón J, Sánchez-Margallo FM, Pascual S, Climent S (2010) Formación en Cirugía Laparoscópica Paso a Paso, 4th edn. Minimally Invasive Surgery Centre Jesús Usón, CáceresGoogle Scholar
  6. 6.
    Sánchez-González P, Oropesa I, Romero V, Fernández A, Albacete A, Asenjo E, Noguera J, Sánchez-Margallo FM, Burgos D, Gómez EJ (2010) TELMA: technology enhanced learning environment for minimally Invasive surgery. Procedia Comp Sci 3:316–321CrossRefGoogle Scholar
  7. 7.
    Oropesa I, Sánchez-González P, Lamata P, Chmarra MK, Pagador JB, Sánchez-Margallo JA, Sánchez-Margallo FM, Gómez EJ (2011) Methods and tools for objective assessment of psychomotor skills in laparoscopic surgery. J Surg Res 171:e81–e95. doi:10.1016/j.jss.2011.06.034 PubMedCrossRefGoogle Scholar
  8. 8.
    Fried GM, Feldman LS (2008) Objective assessment of technical performance. World J Surg 32:156–160PubMedCrossRefGoogle Scholar
  9. 9.
    Chmarra MK, Bakker NH, Grimbergen CA, Dankelman J (2006) TrEndo, a device for tracking minimally invasive surgical instruments in training setups. Sens Actuat A-Physical 126:328–334CrossRefGoogle Scholar
  10. 10.
    Rosen J, Brown JD, Barreca M, Chang L, Hannaford B, Sinanan M (2002) The Blue DRAGON–a system for monitoring the kinematics and the dynamics of endoscopic tools in minimally invasive surgery for objective laparoscopic skill assessment. Stud Health Technol Inform 85:412–418PubMedGoogle Scholar
  11. 11.
    Sokollik C, Gross J, Buess G (2004) New model for skills assessment and training progress in minimally invasive surgery. Surg Endosc 18:495–500PubMedCrossRefGoogle Scholar
  12. 12.
    Datta V, Mackay S, Mandalia M, Darzi A (2001) The use of electromagnetic motion tracking analysis to objectively measure open surgical skill in the laboratory-based model. J Am Coll Surg 193:479–485PubMedCrossRefGoogle Scholar
  13. 13.
    Peters TM (2006) Image-guidance for surgical procedures. Phys Med Biol 51:R505–R540. doi:10.1088/0031-9155/51/14/R01 PubMedCrossRefGoogle Scholar
  14. 14.
    Sánchez-González P, Cano AM, Oropesa I, Sánchez-Margallo FM, del Pozo F, Lamata P, Gómez EJ (2011) Laparoscopic video analysis for training and image guided surgery. Minim Invasive Ther Allied Technol. doi:10.3109/13645706.2010.541921 Google Scholar
  15. 15.
    Sánchez-Margallo JA, Sánchez-Margallo FM, Pagador JB, Gómez-Aguilera EJ, Sánchez-González P, Usón J, Moreno J (2010) Video-based assistance system for training in minimally invasive surgery. Minim Invasive Ther Allied Technol 20:197–205PubMedCrossRefGoogle Scholar
  16. 16.
    van Sickle KR, McClusky DA 3rd, Gallagher AG, Smith CD (2005) Construct validation of the ProMIS simulator using a novel laparoscopic suturing task. Surg Endosc 19:1227–1231PubMedCrossRefGoogle Scholar
  17. 17.
    Pellen MG, Horgan LF, Barton JR, Attwood SE (2009) Construct validity of the ProMIS laparoscopic simulator. Surg Endosc 23:130–139PubMedCrossRefGoogle Scholar
  18. 18.
    Ritter EM, Kindelan TW, Michael C, Pimentel EA, Bowyer MW (2007) Concurrent validity of augmented reality metrics applied to the fundamentals of laparoscopic surgery (FLS). Surg Endosc 21:1441–1445PubMedCrossRefGoogle Scholar
  19. 19.
    Krupa A, Gangloff J, Doignon C, de Mathelin MF, Morel G, Leroy J, Soler L, Marescaux J (2003) Autonomous 3-D positioning of surgical instruments in robotized laparoscopic surgery using visual “servoing.”. IEEE T Robotic Autom 9:842–853CrossRefGoogle Scholar
  20. 20.
    Allen BF, Kasper F, Nataneli G, Dutson E, Faloutsos P (2011) Visual tracking of laparoscopic instruments in standard training environments. Stud Health Technol Inform 163:11–17PubMedGoogle Scholar
  21. 21.
    Speidel S, Delles M, Gutt C, Dillmann R (2006) Tracking of instruments in minimally invasive surgery for surgical skill analysis. Med Imag Augment Real 4091:148–155CrossRefGoogle Scholar
  22. 22.
    Tonet O, Ramesh TU, Megali G, Dario P (2006) Tracking endoscopic instruments without localizer: image analysis-based approach. Stud Health Technol Inform 119:544–549PubMedGoogle Scholar
  23. 23.
    Bouarfa L, Akman O, Schneider A, Jonker PP, Dankelman J (2011) In vivo real-time tracking of surgical instruments in endoscopic video. Minim Invasive Ther Allied Technol DOI: 10.3109/13645706.2011.580764
  24. 24.
    Voros S, Long J, Cinquin P (2006) Automatic localization of laparoscopic instruments for the visual servoing of an endoscopic camera holder. Med Image Comput Comput Assist Interv 4190:535–542Google Scholar
  25. 25.
    Climent J, Marés P (2004) Automatic instrument localization in laparoscopic surgery. Electron Lett Comput Vis Image Anal 4:21–31Google Scholar
  26. 26.
    McKenna SJ, Charif HN, Frank T (2005) Towards video understanding for laparoscopic surgery: instrument tracking. Image and Vision Computing New Zealand Conference, Dunedin, New ZealandGoogle Scholar
  27. 27.
    Doignon C, Nageotte F, Maurin B, Krupa A (2008) Pose estimation and feature tracking for robot assisted surgery with medical imaging. In: Kragic D, Kyrik V (eds) Unifying perspectives in computational and robot vision, vol 8. Springer, New York, pp 79–101CrossRefGoogle Scholar
  28. 28.
    Cano AM, Lamata P, Gayá F, del Pozo F, Gómez EJ (2006) New methods for video-based tracking of laparoscopic tools. In: Harders M, Székely G (eds) ISBMS 2006, LNCS, vol 4072. Springer, Heidelberg, pp 142–149Google Scholar
  29. 29.
    Cano AM, Sánchez-González P, Sánchez-Margallo FM, Oropesa I, del Pozo F, Gómez EJ (2008) Video-endoscopic image analysis for 3D reconstruction of the surgical scene. In: Sloten JV, Verdonck P, Nyssen M, Haueisen J (eds) 4th European Conference of the International Federation for Medical and Biological Engineering, IFMBE Proceedings vol 22. pp 923–926Google Scholar
  30. 30.
    Wolf R, Duchateau J, Cinquin P, Voros S (2011) 3D tracking of laparoscopic instruments using statistical and geometric modeling. In: Fichtinger G, Martel A, Peters T (eds) Medical image computing and computer-assisted intervention – MICCAI 2011, LNCS, vol 6891. pp 203–210Google Scholar
  31. 31.
    Satava RM, Cuschieri A, Hamdorf J (2003) Metrics for objective assessment. Surg Endosc 17:220–226PubMedCrossRefGoogle Scholar
  32. 32.
    Chmarra MK, Jansen FW, Grimbergen CA, Dankelman J (2008) Retracting and seeking movements during laparoscopic goal-oriented movements. Is the shortest path length optimal? Surg Endosc 22:943–949PubMedCrossRefGoogle Scholar
  33. 33.
    Chmarra MK, Dankelman J, van den Dobbelsteen JJ, Jansen FW (2008) Force feedback and basic laparoscopic skills. Surg Endosc 22:2140–2148PubMedCrossRefGoogle Scholar
  34. 34.
    Chmarra MK, Klein S, de Winter JCF, Jansen FW, Dankelman J (2010) Objective classification of residents based on their psychomotor laparoscopic skills. Surg Endosc 24:1031–1039PubMedCrossRefGoogle Scholar
  35. 35.
    Hiemstra E, Chmarra MK, Dankelman J, Jansen FW (2011) Intracorporeal suturing: economy of instrument movements using a box trainer model. J Minim Invasive Gynecol 18:494–499PubMedCrossRefGoogle Scholar
  36. 36.
    Cano AM, Vara I, Sánchez-González P, Gómez EJ (2008) Laparoscopic image analysis for automatic tracking of surgical tools. In: Proceedings of computer assisted radiology and surgery (CARS 2008), vol 3. p S279Google Scholar
  37. 37.
    Brown DC (1971) Close-range camera calibration. Photogramm Eng 37:855–866Google Scholar
  38. 38.
    Bouguet JY (2010) Camera Calibration Toolbox for Matlab - Calibrating a stereo system, stereo image rectification and 3D stereo triangulation (2010). Pasadena, CA (USA): California Institute of Technology; 2010 Available at: http://www.vision.caltech.edu/bouguetj/calib_doc/htmls/example5.html. Accessed 27 Oct 2011
  39. 39.
    Gonzales RC, Woods RE (2002) Digital Image Processing, 2nd edn. Prentice-Hall, Englewood Cliffs, NJGoogle Scholar
  40. 40.
    Jain AK, Dubes RC (1981) Algorithms for clustering data. Prentice-Hall, Upper Saddle River, NJGoogle Scholar
  41. 41.
    Aggarwal R, Grantcharov T, Moorthy K, Milland T, Papasavas P, Dosis A, Bello F, Darzi A (2007) An evaluation of the feasibility, validity, and reliability of laparoscopic skills assessment in the operating room. Ann Surg 245:992–999PubMedCrossRefGoogle Scholar
  42. 42.
    Maithel S, Sierra R, Korndorffer J, Neumann P, Dawson S, Callery M, Jones D, Scott D (2006) Construct and face validity of MIST-VR, Endotower, and CELTS. Surg Endosc 20:104–112PubMedCrossRefGoogle Scholar
  43. 43.
    Chmarra MK, Kolkman W, Jansen FW, Grimbergen CA, Dankelman J (2007) The influence of experience and camera holding on laparoscopic instrument movements with the TrEndo tracking system. Surg Endosc 21:2069–2075PubMedCrossRefGoogle Scholar
  44. 44.
    Yamaguchi S, Konishi K, Yasunaga T, Yoshida D, Kinjo N, Kobayashi K, Ieiri S, Okazaki K, Nakashima H, Tanoue K (2007) Construct validity for eye–hand coordination skill on a virtual reality laparoscopic surgical simulator. Surg Endosc 21:2253–2257PubMedCrossRefGoogle Scholar
  45. 45.
    Verdaasdonk EGG, Stassen LPS, Schijven MP, Dankelman J (2007) Construct validity and assessment of the learning curve for the SIMENDO endoscopic simulator. Surg Endosc 21:1406–1412PubMedCrossRefGoogle Scholar
  46. 46.
    Larsen CR, Grantcharov T, Aggarwal R, Tully A, Sørensen JL, Dalsgaard T, Ottesen B (2006) Objective assessment of gynecologic laparoscopic skills using the LapSimGyn virtual reality simulator. Surg Endosc 20:1460–1466PubMedCrossRefGoogle Scholar
  47. 47.
    Stylopoulos N, Cotin S, Maithel S, Ottensmeyer M, Jackson P, Bardsley R, Neumann P, Rattner D, Dawson S (2004) Computer-enhanced laparoscopic training system (CELTS): bridging the gap. Surg Endosc 18:782–789PubMedCrossRefGoogle Scholar
  48. 48.
    Egi H, Okajima M, Yoshimitsu M, Ikeda S, Miyata Y, Masugami H, Kawahara T, Kurita Y, Kaneko M, Asahara T (2008) Objective assessment of endoscopic surgical skills by analyzing direction-dependent dexterity using the hiroshima university endoscopic surgical assessment device (HUESAD). Surg Today 38:705–710PubMedCrossRefGoogle Scholar
  49. 49.
    Megali G, Sinigaglia S, Tonet O, Cavallo F, Dario P (2006) Understanding expertise in surgical gesture by means of Hidden Markov Models. In: Proc IEEE RAS EMBS Int Conf Biomed Robot Biomechatron 2006:625–630Google Scholar
  50. 50.
    Bell JA (1998) Royal air force selection procedures. Ann R Coll Surg Engl 70:270–275Google Scholar
  51. 51.
    Van de Loo RPJM (1998) Selection of surgical trainees in The Netherlands. Ann R Coll Surg Engl 70:277–279Google Scholar
  52. 52.
    Gilligan JH, Treasure T, Watts C (1996) Incorporating psychometric measures in selecting and developing surgeons. J Manag Med 10:5–16PubMedCrossRefGoogle Scholar
  53. 53.
    Lamata P, Gomez EJ, Bello F, Kneebone RL, Aggarwal R, Lamata F (2006) Conceptual framework for laparoscopic VR simulators. IEEE Comput Graph Appl 26:69–79PubMedCrossRefGoogle Scholar
  54. 54.
    Kalman RE (1960) A new approach to linear filtering and prediction problems. Trans ASME, J Basic Eng 82:35–45CrossRefGoogle Scholar
  55. 55.
    Sinha SN, Frahm JM, Pollefeys M, Gene Y (2011) Feature tracking and matching in video using programmable graphics hardware. Mach Vision Appl 22:207–217CrossRefGoogle Scholar
  56. 56.
    Rosen J, Brown JD, Chang L, Sinanan MN, Hannaford B (2006) Generalized approach for modeling minimally invasive surgery as a stochastic process using a discrete Markov model. IEEE Trans Biomed Eng 53:399–413PubMedCrossRefGoogle Scholar
  57. 57.
    Bann SD, Khan MS, Darzi AW (2003) Measurement of surgical dexterity using motion analysis of simple bench tasks. World J Surg 27:390–394PubMedCrossRefGoogle Scholar
  58. 58.
    Sherman V, Feldman L, Stanbridge D, Kazmi R, Fried G (2005) Assessing the learning curve for the acquisition of laparoscopic skills on a virtual reality simulator. Surg Endosc 19:678–682PubMedCrossRefGoogle Scholar
  59. 59.
    van Dongen KW, Tournoij E, van der Zee DC, Schijven MP, Broeders IAMJ (2007) Construct validity of the LapSim: can the LapSim virtual reality simulator distinguish between novices and experts? Surg Endosc 21:1413–1417PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Ignacio Oropesa
    • 1
    • 2
  • Patricia Sánchez-González
    • 1
    • 2
  • Magdalena K. Chmarra
    • 3
    • 6
  • Pablo Lamata
    • 1
  • Álvaro Fernández
    • 1
    • 2
  • Juan A. Sánchez-Margallo
    • 5
  • Frank Willem Jansen
    • 3
    • 4
  • Jenny Dankelman
    • 3
  • Francisco M. Sánchez-Margallo
    • 5
  • Enrique J. Gómez
    • 1
    • 2
  1. 1.Bioengineering and Telemedicine Centre (GBT), ETSI TelecomunicaciónUniversidad Politécnica de Madrid (UPM)MadridSpain
  2. 2.Networking Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN)SaragossaSpain
  3. 3.Department of BioMechanical Engineering, Faculty of Mechanical, Maritime and Materials Engineering (3mE)Delft University of TechnologyDelftThe Netherlands
  4. 4.Department of GynecologyLeiden University Medical CenterLeidenThe Netherlands
  5. 5.Jesús Usón Minimally Invasive Surgery CentreCáceresSpain
  6. 6.Department of Circulation and Medical Imaging, Faculty of MedicineNorwegian University of Science and Technology (NTNU)TrondheimNorway

Personalised recommendations