Modeling and online recognition of surgical phases using hidden Markov models

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5242 LNCS, Issue PART 2, 2008, Pages 627-635

  Blum, Tobias ^a   Padoy, Nicolas ^a,b   Feußner, Hubertus ^c   Navab, Nassir ^a  

^a TECHNICAL UNIVERSITY OF MUNICH   (Germany)

^b LORIA   (France)

^c TECHNICAL UNIVERSITY OF MUNICH   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

HIDDEN MARKOV MODELS; MEDICAL COMPUTING; MEDICAL IMAGING; MERGING; TOPOLOGY; TRANSPLANTATION (SURGICAL); USER INTERFACES;

CONTEXT SENSITIVE; CURRENT PHASE; LAPAROSCOPIC CHOLECYSTECTOMY; LEVEL OF DETAIL; MODEL MERGING; ON-LINE RECOGNITION; SURGICAL WORKFLOW; TRACKING DATA;

SURGICAL EQUIPMENT;

ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; CHOLECYSTECTOMY; COMPUTER ASSISTED SURGERY; COMPUTER INTERFACE; DECISION SUPPORT SYSTEM; GALLBLADDER; HUMAN; METHODOLOGY; ONLINE SYSTEM; PROBABILITY;

ARTIFICIAL INTELLIGENCE; CHOLECYSTECTOMY, LAPAROSCOPIC; DECISION SUPPORT TECHNIQUES; GALLBLADDER; HUMANS; MARKOV CHAINS; ONLINE SYSTEMS; PATTERN RECOGNITION, AUTOMATED; SURGERY, COMPUTER-ASSISTED; USER-COMPUTER INTERFACE;

EID: 77956014487     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-85990-1_75     Document Type: Conference Paper

References (13)

1. Plsek, P.: Complexity and the adoption of innovation in health care. In: Accelerating Quality Improvement in Health Care (2003)
2. 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)
3. Neumuth, T., Strauß, G., Meixensberger, J., Lemke, H., Burgert, O.: Acquisition of process descriptions from surgical interventions. In: Database and Expert Systems Applications, pp. 602-611 (2006)
4. Blum, T., Padoy, N., Feußner, H., Navab, N.: Workflow mining for visualization and analysis of surgeries. In: Computer Assisted Radiology and Surgery (2008)
5. 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)
6. Rosen, J., Brown, J., Chang, L., Sinanan, M., Hannaford, B.: Generalized Approach for Modeling Minimally Invasive Surgery as a Stochastic Process Using a Discrete Markov Model. IEEE Trans. on Biomed. Eng. 53(3), 399-413 (2006)
7. Lin, H., Shafran, I., Yuh, D., Hager, G.: Towards automatic skill evaluation: Detection and segmentation of robot-assisted surgical motions. Computer Aided Surgery 11(5), 220-230 (2006)
8. Bhatia, B., Oates, T., Xiao, Y., Hu, P.: Real-Time Identification of Operating Room State from Video. Innovative Applications of Artificial Intelligence, 1761-1766 (2007)
9. James, A., Vieira, D., Lo, B., Darzi, A., Yang, G.Z.: Eye-Gaze Driven Surgical Workflow Segmentation. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007, Part II. LNCS, vol. 4792, pp. 110-117. Springer, Heidelberg (2007)
10. Padoy, N., Blum, T., Essa, I., Feußner, H., Berger, M.O., Navab, N.: A boosted segmentation method for surgical workflow analysis. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007, Part I. LNCS, vol. 4791, pp. 102-109. Springer, Heidelberg (2007)
11. Padoy, N., Blum, T., Feußner, H., Berger, M.-O., Navab, N.: On-line recognition of surgical activity for monitoring in the operating room. In: Innovative Applications of Artificial Intelligence (2008)
12. Stolcke, A., Omohundro, S.: Inducing probabilistic grammars by Bayesian model merging. In: Grammatical Inference and Applications, pp. 106-118 (1994)
13. Stolcke, A., Omohundro, S.: Best-first model merging for hidden markov model induction. Technical Report TR-94-003, ICSI, Berkeley, CA (1994)