TY - GEN
T1 - Modeling and online recognition of surgical phases using hidden Markov models
AU - Blum, Tobias
AU - Padoy, Nicolas
AU - Feußner, Hubertus
AU - Navab, Nassir
PY - 2008
Y1 - 2008
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=77956014487&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-85990-1_75
DO - 10.1007/978-3-540-85990-1_75
M3 - Conference contribution
C2 - 18982657
AN - SCOPUS:77956014487
SN - 3540859896
SN - 9783540859895
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 627
EP - 635
BT - Medical Image Computing and Computer-Assisted Intervention - MICCAI 2008 - 11th International Conference, Proceedings
PB - Springer Verlag
T2 - 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2008
Y2 - 6 September 2008 through 10 September 2008
ER -