Abstract
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.
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Lin, H.C., Shafran, I., Murphy, T.E., Okamura, A.M., Yuh, D.D., Hager, G.D. (2005). Automatic Detection and Segmentation of Robot-Assisted Surgical Motions. In: Duncan, J.S., Gerig, G. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2005. MICCAI 2005. Lecture Notes in Computer Science, vol 3749. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11566465_99
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DOI: https://doi.org/10.1007/11566465_99
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