Modeling and Online Recognition of Surgical Phases Using Hidden Markov Models

Authors:
Tobias Blum;Nicolas Padoy;Hubertus Feußner;Nassir Navab
Affiliations:
Computer Aided Medical Procedures (CAMP), Technische Universität München, Germany;Computer Aided Medical Procedures (CAMP), Technische Universität München, Germany and LORIA-INRIA Lorraine, Nancy, France;Department of Surgery, Klinikum Rechts der Isar, Technische Universität München, Germany;Computer Aided Medical Procedures (CAMP), Technische Universität München, Germany
Venue:
MICCAI '08 Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, Part II
Year:
2008

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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.