Self-guided training for deep brain stimulation planning using objective assessment

  • Matthew S. Holden
  • Yulong Zhao
  • Claire Haegelen
  • Caroline Essert
  • Sara Fernandez-Vidal
  • Eric Bardinet
  • Tamas Ungi
  • Gabor Fichtinger
  • Pierre Jannin
Original Article



Deep brain stimulation (DBS) is an increasingly common treatment for neurodegenerative diseases. Neurosurgeons must have thorough procedural, anatomical, and functional knowledge to plan electrode trajectories and thus ensure treatment efficacy and patient safety. Developing this knowledge requires extensive training. We propose a training approach with objective assessment of neurosurgeon proficiency in DBS planning.


To assess proficiency, we propose analyzing both the viability of the planned trajectory and the manner in which the operator arrived at the trajectory. To improve understanding, we suggest a self-guided training course for DBS planning using real-time feedback. To validate the proposed measures of proficiency and training course, two experts and six novices followed the training course, and we monitored their proficiency measures throughout.


At baseline, experts planned higher quality trajectories and did so more efficiently. As novices progressed through the training course, their proficiency measures increased significantly, trending toward expert measures.


We developed and validated measures which reliably discriminate proficiency levels. These measures are integrated into a training course, which quantitatively improves trainee performance. The proposed training course can be used to improve trainees’ proficiency, and the quantitative measures allow trainees’ progress to be monitored.


Deep brain stimulation Objective skill assessment Simulation-based training 



Matthew S. Holden was supported by the Natural Sciences and Engineering Research Council (NSERC) Canada Graduate Scholarship (Grant No. CGSD3-460098-2014). Travel was supported by the Mitacs Globalink Research Award—Campus France and the Rennes Metropole Mobility Grant. Gabor Fichtinger was supported as a Cancer Care Ontario Research Chair in Cancer Imaging. This work was financially supported as a Collaborative Health Research Project (CHRP #127797), a joint initiative between the Natural Sciences and Engineering Research Council of Canada (NSERC) and the Canadian Institutes of Health Research (CIHR).

Compliance with ethical standards

Conflict of interest

All authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Informed consent

All participation was voluntary. Written informed consent was obtained from all participants in this study.


  1. 1.
    Krack P, Hariz MI, Baunez C, Guridi J, Obeso JA (2010) Deep brain stimulation: from neurology to psychiatry? Trends Neurosci 33(10):474–484CrossRefPubMedGoogle Scholar
  2. 2.
    York MK, Wilde EA, Simpson R, Jankovic J (2009) Relationship between neuropsychological outcome and DBS surgical trajectory and electrode location. J Neurol Sci 287(1–2):159–171CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Voges J, Waerzeggers Y, Maarouf M, Lehrke R, Koulousakis A, Lenartz D, Sturm V (2006) Deep-brain stimulation: long-term analysis of complications caused by hardware and surgery-experiences from a single centre. J Neurol Neurosurg Psychiatry 77(7):868–72CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Rogers DA, Regehr G, Howdieshell TR, Yeh KA, Palm E (2000) The impact of external feedback on computer-assisted learning for surgical technical skill training. Am J Surg 179(4):341–343CrossRefPubMedGoogle Scholar
  5. 5.
    Guo T, Finnis KW, Parrent AG, Peters TM (2006) Visualization and navigation system development and application for stereotactic deep-brain neurosurgeries. Comput Aided Surg 11(5):231–239CrossRefPubMedGoogle Scholar
  6. 6.
    Miocinovic S, Noecker AM, Maks CB, Butson CR, McIntyre CC (2007) In: Sakas DE, Simpson BA (eds) Operative neuromodulation: volume 2: neural networks surgery. Springer, Vienna, pp 561–567Google Scholar
  7. 7.
    D‘Haese P-F, Pallavaram S, Li R, Remple MS, Kao C, Neimat JS, Konrad PE, Dawant BM (2012) CranialVault and its CRAVE tools: a clinical computer assistance system for deep brain stimulation (DBS) therapy. Med Image Anal 16(3):744–753CrossRefPubMedGoogle Scholar
  8. 8.
    D‘Albis T, Haegelen C, Essert C, Fernandez-Vidal S, Lalys F, Jannin P (2014) PyDBS: an automated image processing workflow for deep brain stimulation surgery. Int J Comput Assist Radiol Surg 10(2):117–128CrossRefPubMedGoogle Scholar
  9. 9.
    Heuer GG, Zaghloul KA, Jaggi JL, Baltuch GH (2008) Use of an integrated platform system in the placement of deep brain stimulators. Neurosurgery 62(3 Suppl 1):245–247PubMedGoogle Scholar
  10. 10.
    Brunenberg EJL, Vilanova A, Visser-Vandewalle V, Temel Y, Ackermans L, Platel B, ter Haar Romeny BM (2007) Automatic trajectory planning for deep brain stimulation: a feasibility study. In: Ayache N, Ourselin S, Maeder A (eds) Medical image computing and computer-assisted intervention—MICCAI 2007: 10th international conference, Brisbane, October 29–November 2, proceedings, part I. Springer, Berlin, pp 584–592CrossRefGoogle Scholar
  11. 11.
    Tirelli P, De Momi E, Borghese NA, Ferrigno G (2009) An intelligent atlas-based planning system for keyhole neurosurgery. Int J Comput Assist Radiol Surg 4(1):85–86Google Scholar
  12. 12.
    Essert C, Haegelen C, Lalys F, Abadie A, Jannin P (2011) Automatic computation of electrode trajectories for deep brain stimulation: a hybrid symbolic and numerical approach. Int J Comput Assist Radiol Surg 7(4):517–532CrossRefPubMedGoogle Scholar
  13. 13.
    Beriault S, Al Subaie F, Collins DL, Sadikot AF, Pike GB (2012) A multi-modal approach to computer-assisted deep brain stimulation trajectory planning. Int J Comput Assist Radiol Surg 7(5):687–704CrossRefPubMedGoogle Scholar
  14. 14.
    Shamir RR, Joskowicz L, Tamir I, Dabool E, Pertman L, Ben-Ami A, Shoshan Y (2012) Reduced risk trajectory planning in image-guided keyhole neurosurgery. Med Phys 39(5):2885–2895CrossRefPubMedGoogle Scholar
  15. 15.
    Liu Y, Konrad PE, Neimat JS, Tatter SB, Yu H, Datteri RD, Landman BA, Noble JH, Pallavaram S, Dawant BM, D’Haese P-F (2014) Multisurgeon, multisite validation of a trajectory planning algorithm for deep brain stimulation procedures. IEEE Trans Biomed Eng 61(9):2479–2487CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Essert C, Fernandez-Vidal S, Capobianco A, Haegelen C, Karachi C, Bardinet E, Marchal M, Jannin P (2015) Statistical study of parameters for deep brain stimulation automatic preoperative planning of electrodes trajectories. Int J Comput Assist Radiol Surg 10(12):1973–1983CrossRefPubMedGoogle Scholar
  17. 17.
    Navkar NV, Tsekos NV, Stafford JR, Weinberg JS, Deng Z (2010) Visualization and planning of neurosurgical interventions with straight access. In: Navab N, Jannin P (eds) Information processing in computer-assisted interventions: first international conference, IPCAI 2010, Geneva, Switzerland, June 23, 2010. Proceedings. Springer, Berlin, pp 1–11Google Scholar
  18. 18.
    Alaraj A, Lemole MG, Finkle JH, Yudkowsky R, Wallace A, Luciano C, Banerjee PP, Rizzi SH, Charbel FT (2011) Virtual reality training in neurosurgery: review of current status and future applications. Surg Neurol Int 2:52CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Neubauer A, Wolfsberger S, Forster MT, Mroz L, Wegenkittl R, Buhler K (2005) Advanced virtual endoscopic pituitary surgery. IEEE Trans Vis Comput Graph 11(5):497–507CrossRefPubMedGoogle Scholar
  20. 20.
    Kockro RA, Stadie A, Schwandt E, Reisch R, Charalampaki C, Ng I, Yeo TT, Hwang P, Serra L, Perneczky A (2007) A collaborative virtual reality environment for neurosurgical planning and training. Neurosurgery 61(5 Suppl 2):379–391PubMedGoogle Scholar
  21. 21.
    John NW, Phillips NI, Cenydd LA, Coope D, Carleton-Bland N, Kamaly-Asl I, Gray WP (2015) A tablet-based virtual environment for neurosurgery training. Presence 24(2):155–162CrossRefGoogle Scholar
  22. 22.
    Allen B, Nistor V, Dutson E, Carman G, Lewis C, Faloutsos P (2010) Support vector machines improve the accuracy of evaluation for the performance of laparoscopic training tasks. Surg Endosc 24(1):170–178CrossRefPubMedGoogle Scholar
  23. 23.
    Ungi T, Sargent D, Moult E, Lasso A, Pinter C, McGraw RC, Fichtinger G (2012) Perk Tutor: an open-source training platform for ultrasound-guided needle insertions. IEEE Trans Biomed Eng 59(12):3475–3481CrossRefPubMedGoogle Scholar
  24. 24.
    Venkatesh V, Morris MG, Davis GB, Davis FD (2003) User acceptance of information technology: toward a unified view. MIS Q 27(3):425–478CrossRefGoogle Scholar

Copyright information

© CARS 2018

Authors and Affiliations

  1. 1.Laboratory for Percutaneous Surgery, School of ComputingQueen’s UniversityKingstonCanada
  2. 2.Equipe MediCIS, Faculté de MédicineUniversité de Rennes 1RennesFrance
  3. 3.Centre Hospitalier Universitaire de RennesRennesFrance
  4. 4.ICubeUniversité de StrasbourgStrasbourgFrance
  5. 5.ICM CENIRHôpital Pitié-SalpêtrièreParisFrance

Personalised recommendations