Workflow mining for visualization and analysis of surgeries
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Modeling the workflow of a surgery is a topic of growing interest. Workflow models can be used to analyze statistical properties of a surgery, for intuitive visualization, evaluation and other applications. In most cases, workflow models are created manually, which is a time consuming process that might suffer from a personal bias. In this work, an approach for automatic workflow mining is presented.
Materials and methods
Ten process logs, each describing a single instance of a laparoscopic cholecystectomy, are used to build a Hidden Markov Model (HMM). Using a merging approach, models at different levels of detail are generated. These embody statistical information concerning aspects like duration of actions or tool usage during the surgery.
A Graphical User Interface (GUI) is presented, that uses a graph representation of the HMM to intuitively visualize surgical workflow. It allows changing the level of detail by expanding and merging nodes. The GUI can also be used to compare videos of surgeries which are synchronized to the model.
The proposed method allows automatic generation and visualization of a statistical model describing the workflow of a surgery.
KeywordsWorkflow mining Surgical workflow analysis Information visualization Cholecystectomy Hidden Markov models
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- 2.Maruster L, van der Aalst WMP, Weijters AJMM, van der Bosch A, Daelemans W (2002) Automated discovery of Workflow Models from hospital data. In: Proceedings of the ECAI Workshop on Knowledge Discovery and Spatial Data. pp 183–190Google Scholar
- 4.Neumuth T, Trantakis C, Eckhardt F, Dengl M, Meixensberger J, Burgert O (2007) Supporting the analysis of intervention courses with surgical process models on the example of 14 microsurgical lumbar discectomies. Int J Comput Assist Radiol Surg 2(Suppl 1): 436–438Google Scholar
- 5.Neumuth T, Schumann S, Strauß G, Jannin P, Meixensberger J, Dietz A, Lemke HU, Burgert O (2006) Visualization options for surgical workflows. Int J Comput Assist Radiol Surg 1(suppl 1): 438–440Google Scholar
- 6.Megali G, Sinigaglia S, Tonet O, Cavallo F, Dario P (2006) Understanding expertise in surgical gesture by means of Hidden Markov Models. International conference on biomedical robotics and biomechatronics (BioRob), Tuscany, Italy, pp 625–630Google Scholar
- 7.Leong JJH, Nicolaou M, Atallah L, Mylonas GP, Darzi AW, Yang GZ (2006) HMM assessment of quality of movement trajectory in laparoscopic surgery. In: Proceedings of medical image computing and computer-assisted intervention (MICCAI), Copenhagen, Denmark, pp 752–759Google Scholar
- 9.Ohnuma K, Masamune K, Yoshimitsu K, Sadahiro T, Vain J, Fukui Y, Miyawaki F (2006) Timed-automata-based model for laparoscopic surgery and intraoperative motion recognition of a surgeon as the interface connecting the signal scenario and the real operating rool. Int J Comput Assist Radiol Surg 1(suppl 1): 442–445Google Scholar
- 10.Ahmadi A, Sielhorst T, Stauder R, Horn M, Feußner H, Navab N (2006) Recovery of surgical workflow without explicit models. In: Proceedings of medical image computing and computer-assisted intervention (MICCAI), Copenhagen, Denmark, pp 420–428Google Scholar
- 11.Padoy N, Horn M, Feußner H, Berger MO, Navab N (2007) Recovery of surgical workflow: a model-based approach. Int J Comput Assist Radiol Surg 2(suppl 1): 481–482Google Scholar
- 13.Stolcke A, Omohundro S (1994) Inducing probabilistic grammars by Bayesian model merging. Grammatical inference and applications. In: Proceedings of the second international colloquium on grammatical inference, Alicante, Spain, pp 106–118Google Scholar
- 14.Stolcke A, Omohundro S (1994) Best-first model merging for hidden markov model induction. Technical report TR-94–403, ICSI, Berkeley, CAGoogle Scholar
- 15.Ellson J, Ganser E, Koutsofis L, North SC, Woodhull G (2001) GraphViz—open source graph drawing tools. Graph drawing: 9th international symposium, Vienna, Austria, pp 483–484Google Scholar