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)


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.


Laparoscopic Cholecystectomy Hide Markov Model Dynamic Time Warping Laparoscopic Instrument Temporal Synchronization 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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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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