Annals of Computer Science and Information Systems, Volume 11

DOI: http://dx.doi.org/10.15439/2017F316

Citation: Proceedings of the 2017 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 11, pages 213–219 (2017)

Full text

Abstract. Neuro-endoscopy is a challenging minimally invasive neurosurgery that requires surgical skills to be acquired using training methods different from the existing apprenticeship model. There are various training systems developed for imparting fundamental technical skills in laparoscopy whereas limited systems for neuro-endoscopy. Neuro-Endo-Trainer was a box-trainer developed for endo-nasal transsphenoidal skills training with video based offline evaluation system. The objective of the current study was to develop a modified version (Neuro-Endo-Trainer-Online Assessment System (NET-OAS)) by providing a stand-alone system with online evaluation and real-time feedback. The validation study on a novice group of 15 participants shows the improvement in the technical skills for handling the neuro- endoscope and the tool while performing pick and place activity.

References

1. R. Abbott, “History of neuroendoscopy,” Neurosurgery Clinics of North America, vol. 15, no. 1, pp. 1–7, 2004.
2. M. Bridges and D. L. Diamond, “The financial impact of teaching surgical residents in the operating room,” The American Journal of Surgery, vol. 177, no. 1, pp. 28–32, 1999.
3. R. Hirayama, Y. Fujimoto, M. Umegaki, N. Kagawa, M. Kinoshita, N. Hashimoto, and T. Yoshimine, “Training to acquire psychomotor skills for endoscopic endonasal surgery using a personal webcam trainer: Clinical article,” Journal of neurosurgery, vol. 118, no. 5, pp. 1120–1126, 2013.
4. R. Singh, V. K. Srivastav, B. Baby, N. Damodaran, and A. Suri, “A novel electro-mechanical neuro-endoscopic box trainer,” in Industrial Instrumentation and Control (ICIC), 2015 International Conference on. IEEE, 2015, pp. 917–921.
5. G. Rosseau, J. Bailes, R. del Maestro, A. Cabral, N. Choudhury, O. Comas, P. Debergue, G. De Luca, J. Hovdebo, D. Jiang et al., “The development of a virtual simulator for training neurosurgeons to perform and perfect endoscopic endonasal transsphenoidal surgery,” Neurosurgery, vol. 73, pp. S85–S93, 2013.
6. S. Wolfsberger, M.-T. Forster, M. Donat, A. Neubauer, K. Bühler, R. Wegenkittl, T. Czech, J. A. Hainfellner, and E. Knosp, “Virtual endoscopy is a useful device for training and preoperative planning of transsphenoidal endoscopic pituitary surgery,” min-Minimally Invasive Neurosurgery, vol. 47, no. 04, pp. 214–220, 2004.
7. J. Fernandez-Miranda, J. Barges-Coll, D. Prevedello, J. Engh, C. Snyderman, R. Carrau, P. Gardner, and A. Kassam, “Animal model for endoscopic neurosurgical training: technical note,” min-Minimally Invasive Neurosurgery, vol. 53, no. 05/06, pp. 286–289, 2010.
8. R. Singh, B. Baby, N. Damodaran, V. Srivastav, A. Suri, S. Banerjee, S. Kumar, P. Kalra, S. Prasad, K. Paul et al., “Design and validation of an open-source, partial task trainer for endonasal neuro-endoscopic skills development: Indian experience,” World neurosurgery, vol. 86, pp. 259–269, 2016.
9. B. Baby, V. K. Srivastav, R. Singh, A. Suri, and S. Banerjee, “Neuro-endo-activity-tracker: An automatic activity detection application for neuro-endo-trainer: Neuro-endo-activity-tracker,” in Advances in Computing, Communications and Informatics (ICACCI), 2016 International Conference on. IEEE, 2016, pp. 987–993.
10. J. Dankelman, M. Chmarra, E. Verdaasdonk, L. Stassen, and C. Grimbergen, “Fundamental aspects of learning minimally invasive surgical skills,” Minimally Invasive Therapy & Allied Technologies, vol. 14, no. 4-5, pp. 247–256, 2005.
11. M. K. Chmarra, N. H. Bakker, C. A. Grimbergen, and J. Dankelman, “Trendo, a device for tracking minimally invasive surgical instruments in training setups,” Sensors and Actuators A: Physical, vol. 126, no. 2, pp. 328–334, 2006.
12. C. L. Y. W. D. Uecker and Y. Wang, “Image analysis for automated tracking in robot-assisted endoscopic surgery,” in Proc. 12th Int’l Conf. Pattern Recognition, 1994, pp. 88–92.
13. G.-Q. Wei, K. Arbter, and G. Hirzinger, “Real-time visual servoing for laparoscopic surgery. controlling robot motion with color image segmentation,” IEEE Engineering in Medicine and Biology Magazine, vol. 16, no. 1, pp. 40–45, 1997.
14. S.-K. Jun, M. S. Narayanan, P. Agarwal, A. Eddib, P. Singhal, S. Garimella, and V. Krovi, “Robotic minimally invasive surgical skill assessment based on automated video-analysis motion studies,” in Biomedical Robotics and Biomechatronics (BioRob), 2012 4th IEEE RAS & EMBS International Conference on. IEEE, 2012, pp. 25–31.
15. M. Allan, S. Thompson, M. J. Clarkson, S. Ourselin, D. J. Hawkes, J. Kelly, and D. Stoyanov, “2d-3d pose tracking of rigid instruments in minimally invasive surgery,” in International Conference on Information Processing in Computer-assisted Interventions. Springer, 2014, pp. 1–10.
16. Q. Zhang, L. Chen, Q. Tian, and B. Li, “Video-based analysis of motion skills in simulation-based surgical training,” in IS&T/SPIE Electronic Imaging. International Society for Optics and Photonics, 2013, pp. 86 670A–86 670A.
17. Z. Kalal, K. Mikolajczyk, and J. Matas, “Tracking-learning-detection,” IEEE transactions on pattern analysis and machine intelligence, vol. 34, no. 7, pp. 1409–1422, 2012.