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Erschienen in:
2005 | OriginalPaper | Buchkapitel
Automatic Detection and Segmentation of Robot-Assisted Surgical Motions
verfasst von : Henry C. Lin, Izhak Shafran, Todd E. Murphy, Allison M. Okamura, David D. Yuh, Gregory D. Hager
Erschienen in: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2005
Verlag: Springer Berlin Heidelberg
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Robotic surgical systems such as Intuitive Surgical’s da Vinci system provide a rich source of motion and video data from surgical procedures. In principle, this data can be used to evaluate surgical skill, provide surgical training feedback, or document essential aspects of a procedure. If processed online, the data can be used to provide context-specific information or motion enhancements to the surgeon. However, in every case, the key step is to relate recorded motion data to a model of the procedure being performed. This paper examines our progress at developing techniques for “parsing” raw motion data from a surgical task into a labelled sequence of surgical gestures. Our current techniques have achieved > 90% fully automated recognition rates on 15 datasets.