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Modeling and Online Recognition of Surgical Phases Using Hidden Markov Models

  • Tobias Blum
  • Nicolas Padoy
  • Hubertus Feußner
  • Nassir Navab
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5242)

Abstract

The amount of signals that can be recorded during a surgery, like tracking data or state of instruments, is constantly growing. These signals can be used to better understand surgical workflow and to build surgical assist systems that are aware of the current state of a surgery. This is a crucial issue for designing future systems that provide context-sensitive information and user interfaces.

In this paper, Hidden Markov Models (HMM) are used to model a laparoscopic cholecystectomy. Seventeen signals, representing tool usage, from twelve surgeries are used to train the model. The use of a model merging approach is proposed to build the HMM topology and compared to other methods of initializing a HMM. The merging method allows building a model at a very fine level of detail that also reveals the workflow of a surgery in a human-understandable way. Results for detecting the current phase of a surgery and for predicting the remaining time of the procedure are presented.

Keywords

Laparoscopic Cholecystectomy Hide Markov Model Dynamic Time Warping Current Phase Observation Probability 
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 2008

Authors and Affiliations

  • Tobias Blum
    • 1
  • Nicolas Padoy
    • 1
    • 2
  • Hubertus Feußner
    • 3
  • Nassir Navab
    • 1
  1. 1.Computer Aided Medical Procedures (CAMP)Technische Universität MünchenGermany
  2. 2.LORIA-INRIA LorraineNancyFrance
  3. 3.Department of Surgery, Klinikum Rechts der IsarTechnische Universität MünchenGermany

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