A Boosted Segmentation Method for Surgical Workflow Analysis

  • N. Padoy
  • T. Blum
  • I. Essa
  • Hubertus Feussner
  • M. -O. Berger
  • Nassir Navab
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4791)

Abstract

As demands on hospital efficiency increase, there is a stronger need for automatic analysis, recovery, and modification of surgical workflows. Even though most of the previous work has dealt with higher level and hospital-wide workflow including issues like document management, workflow is also an important issue within the surgery room. Its study has a high potential, e.g., for building context-sensitive operating rooms, evaluating and training surgical staff, optimizing surgeries and generating automatic reports.

In this paper we propose an approach to segment the surgical workflow into phases based on temporal synchronization of multidimensional state vectors. Our method is evaluated on the example of laparoscopic cholecystectomy with state vectors representing tool usage during the surgeries. The discriminative power of each instrument in regard to each phase is estimated using AdaBoost. A boosted version of the Dynamic Time Warping (DTW) algorithm is used to create a surgical reference model and to segment a newly observed surgery. Full cross-validation on ten surgeries is performed and the method is compared to standard DTW and to Hidden Markov Models.

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References

  1. 1.
    Dazzi, L., Fassino, C., Saracco, R., Quaglini, S., Stefanelli, M.: A patient workflow management system built on guidelines. In: AMIA 1997, pp. 146–150 (1997)Google Scholar
  2. 2.
    Herfarth, C.: ’lean’ surgery through changes in surgical workflow. British Journal of Surgery 90(5), 513–514 (2003)CrossRefGoogle Scholar
  3. 3.
    Cleary, K., Chung, H.Y., Mun, S.K.: Or 2020: The operating room of the future. Laparoendoscopic and Advanced Surgical Techniques 15(5), 495–500 (2005)CrossRefGoogle Scholar
  4. 4.
    Jannin, P., Raimbault, M., Morandi, X., Gibaud, B.: Modeling surgical procedures for multimodal image-guided neurosurgery. In: Niessen, W.J., Viergever, M.A. (eds.) MICCAI 2001. LNCS, vol. 2208, pp. 565–572. Springer, Heidelberg (2001)Google Scholar
  5. 5.
    Raimbault, M., Morandi, X., Jannin, P.: Towards models of surgical procedures: analyzing a database of neurosurgical cases. In: Med. Imaging, SPIE, pp. 97–104 (2005)Google Scholar
  6. 6.
    Neumuth, T., Strauß, G., Meixensberger, J., Lemke, H.U., Burgert, O.: Acquisition of process descriptions from surgical interventions. In: Bressan, S., Küng, J., Wagner, R. (eds.) DEXA 2006. LNCS, vol. 4080, pp. 602–611. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  7. 7.
    Lin, H.C., Shafran, I., Murphy, T.E., Okamura, A.M., Gregory, D., Hager, D.D.Y.: 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
  8. 8.
    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(1), 49–61 (2002)CrossRefGoogle Scholar
  9. 9.
    Leong, J., Nicolaou, M., Atallah, L., Mylonas, G., Darzi, A., Yang, G.Z.: HMM Assessment of Quality of Movement Trajectory in Laparoscopic Surgery. In: Larsen, R., Nielsen, M., Sporring, J. (eds.) MICCAI 2006. LNCS, vol. 4190, pp. 752–759. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  10. 10.
    Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. In: Vitányi, P.M.B. (ed.) EuroCOLT 1995. LNCS, vol. 904, pp. 23–37. Springer, Heidelberg (1995)Google Scholar
  11. 11.
    Viola, P., Jones, M.J.: Robust real-time face detection. IJCV 57(2), 137–154 (2004)CrossRefGoogle Scholar
  12. 12.
    Ahmadi, S.A., Sielhorst, T., Stauder, R., Horn, M., Feussner, H., Navab, N.: Recovery of surgical workflow without explicit models. In: Larsen, R., Nielsen, M., Sporring, J. (eds.) MICCAI 2006. LNCS, vol. 4190, pp. 420–428. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  13. 13.
    Sakoe, H., Chiba, S.: Dynamic programming algorithm optimization for spoken word recognition. IEEE Trans. Acoust. Speech Signal Process. 26(1), 43–49 (1978)MATHCrossRefGoogle Scholar
  14. 14.
    Darrell, T., Essa, I.A., Pentland, A.: Task-specific gesture analysis in real-time using interpolated views. IEEE Trans. PAMI 18(12), 1236–1242 (1996)Google Scholar
  15. 15.
    Kassidas, A., MacGregor, J.F., Taylor, P.A.: Synchronization of batch trajectories using dynamic time warping. AIChE Journal 44(4), 864–875 (1998)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • N. Padoy
    • 1
    • 2
  • T. Blum
    • 1
  • I. Essa
    • 3
  • Hubertus Feussner
    • 4
  • M. -O. Berger
    • 2
  • Nassir Navab
    • 1
  1. 1.Chair for Computer Aided Medical Procedures (CAMP), TU MunichGermany
  2. 2.LORIA-INRIA Lorraine, NancyFrance
  3. 3.College of Computing, Georgia Institute of Technology, AtlantaUSA
  4. 4.Chirurgische Klinik und Poliklinik, Klinikum Rechts der Isar, TU MunichGermany

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