13 March 2009 Recognition of surgical skills using hidden Markov models
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Proceedings Volume 7261, Medical Imaging 2009: Visualization, Image-Guided Procedures, and Modeling; 726125 (2009); doi: 10.1117/12.811140
Event: SPIE Medical Imaging, 2009, Lake Buena Vista (Orlando Area), Florida, United States
Abstract
Minimally invasive surgery is a highly complex medical discipline and can be regarded as a major breakthrough in surgical technique. A minimally invasive intervention requires enhanced motor skills to deal with difficulties like the complex hand-eye coordination and restricted mobility. To alleviate these constraints we propose to enhance the surgeon's capabilities by providing a context-aware assistance using augmented reality techniques. To recognize and analyze the current situation for context-aware assistance, we need intraoperative sensor data and a model of the intervention. Characteristics of a situation are the performed activity, the used instruments, the surgical objects and the anatomical structures. Important information about the surgical activity can be acquired by recognizing the surgical gesture performed. Surgical gestures in minimally invasive surgery like cutting, knot-tying or suturing are here referred to as surgical skills. We use the motion data from the endoscopic instruments to classify and analyze the performed skill and even use it for skill evaluation in a training scenario. The system uses Hidden Markov Models (HMM) to model and recognize a specific surgical skill like knot-tying or suturing with an average recognition rate of 92%.
© (2009) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Stefanie Speidel, Tom Zentek, Gunther Sudra, Tobias Gehrig, Beat Peter Müller-Stich, Carsten Gutt, Rüdiger Dillmann, "Recognition of surgical skills using hidden Markov models", Proc. SPIE 7261, Medical Imaging 2009: Visualization, Image-Guided Procedures, and Modeling, 726125 (13 March 2009); doi: 10.1117/12.811140; https://doi.org/10.1117/12.811140
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KEYWORDS
Surgery

Data modeling

Motion models

Detection and tracking algorithms

Expectation maximization algorithms

Visual process modeling

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