Recovery of Surgical Workflow Without Explicit Models

  • Seyed-Ahmad Ahmadi
  • Tobias Sielhorst
  • Ralf Stauder
  • Martin Horn
  • Hubertus Feussner
  • Nassir Navab
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4190)


Workflow recovery is crucial for designing context-sensitive service systems in future operating rooms. Abstract knowledge about actions which are being performed is particularly valuable in the OR. This knowledge can be used for many applications such as optimizing the workflow, recovering average workflows for guiding and evaluating training surgeons, automatic report generation and ultimately for monitoring in a context aware operating room.

This paper describes a novel way for automatic recovery of the surgical workflow. Our algorithms perform this task without an implicit or explicit model of the surgery. This is achieved by the synchronization of multidimensional state vectors of signals recorded in different operations of the same type. We use an enhanced version of the dynamic time warp algorithm to calculate the temporal registration. The algorithms have been tested on 17 signals of six different surgeries of the same type. The results on this dataset are very promising because the algorithms register the steps in the surgery correctly up to seconds, which is our sampling rate. Our software visualizes the temporal registration by displaying the videos of different surgeries of the same type with varying duration precisely synchronized to each other. The synchronized videos of one surgery are either slowed down or speeded up in order to show the same steps as the ones presented in the videos of the other surgery.


Hide Markov Model Explicit Model Dynamic Time Warping Functional Endoscopic Sinus Surgery Warp Path 


  1. 1.
    Herfarth, C.: ’lean’ surgery through changes in surgical workflow. British Journal of Surgery 90, 513–514 (2003)CrossRefGoogle Scholar
  2. 2.
    Cleary, K., Chung, H.Y., Mun, S.K.: Or 2020: The operating room of the future. Laparoendoscopic and Advanced Surgical Techniques 15, 495–500 (2005)CrossRefGoogle Scholar
  3. 3.
    Riedl, S.: Modern operating room management in the workflow of surgery. spectrum of tasks and challenges of the future. Der Chirurg 73, 105–110 (2002)CrossRefGoogle Scholar
  4. 4.
    Aggarwal, R., Undre, S., Moorthy, K., Vincent, C., Darzi, A.: The simulated operating theatre: comprehensive training for surgical teams. Qual Saf Health Care 13, 27–32 (2004)CrossRefGoogle Scholar
  5. 5.
    Rosen, J., Solazzo, M., Hannaford, B., Sinanan, M.: Task decomposition of laparoscopic surgery for objective evaluation of surgical residents learning curve using hidden markov model. Comput Aided Surg. 7, 49–61 (2002)CrossRefGoogle Scholar
  6. 6.
    Lin, H.C., Shafran, I., Murphy, T.E., Okamura, A.M., Yuh, D.D., Hager, G.D.: Automatic detection and segmentation of robot-assisted surgical motions. In: Duncan, J.S., Gerig, G. (eds.) MICCAI 2005. LNCS, vol. 3749, pp. 802–810. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  7. 7.
    Strauss, G., Fischer, M., Meixensberger, J., et al: Workflow analysis to assess the efficiency of intraoperative technology using the example of functional endoscopic sinus surgery. In: HNO (2005)Google Scholar
  8. 8.
    Sielhorst, T., Blum, T., Navab, N.: Synchronizing 3d movements for quantitative comparison and simultaneous visualization of actions. In: Proc. IEEE and ACM International on Mixed and Augmented Reality (ISMAR) (2005)Google Scholar
  9. 9.
    Sakoe, H., Chiba, S.: Dynamic programming algorithm optimization for spoken word recognition. IEEE Trans. Acoust. Speech Signal Process. 26, 43–49 (1978)MATHCrossRefGoogle Scholar
  10. 10.
    Kassidas, A., MacGregor, J.F., Taylor, P.A.: Synchronization of batch trajectories using dynamic time warping. AIChE Journal 44, 864–875 (1998)CrossRefGoogle Scholar
  11. 11.
    Li, H., Greenspan, M.: Multi-scale gesture recognition from time-varying contours. In: Tenth IEEE International Conference on Computer Vision (ICCV), vol. 1, pp. 236–243 (2005)Google Scholar
  12. 12.
    Bobick, A.F., Wilson, A.D.: A state-based approach to the representation and recognition of gesture. IEEE Transactions on Pattern Analysis and Machine Intelligence 19, 1325–1337 (2005)CrossRefGoogle Scholar
  13. 13.
    Bellman, R.E., Dreyfus, S.E.: Applied Dynamic Programming. Princeton University Press, Princeton (1962)MATHGoogle Scholar
  14. 14.
    Wang, K., Gasser, T.: Alignment of curves by dynamic time warping. Annals of Statistics 25, 1251–1276 (1997)MATHCrossRefMathSciNetGoogle Scholar
  15. 15.
    Gasser, T., Wang, K.: Synchronizing sample curves nonparametrically. Annals of Statistics 27, 439–460 (1999)MATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Seyed-Ahmad Ahmadi
    • 1
  • Tobias Sielhorst
    • 1
  • Ralf Stauder
    • 1
  • Martin Horn
    • 1
  • Hubertus Feussner
    • 2
  • Nassir Navab
    • 1
  1. 1.Chair for Computer Aided Medical Procedures (CAMP)TU MunichGermany
  2. 2.Chirurgische Klinik und Poliklinik, Klinikum Rechts der IsarTU MunichGermany

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