Automatic Detection and Segmentation of Robot-Assisted Surgical Motions. Medical Image Comuting and Computer-Assisted Intervention

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Automatic Detection and Segmentation of Robot-Assisted Surgical Motions. Medical Image Comuting and Computer-Assisted Intervention (2005)

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by Henry C. Lin , Izhak Shafran , Todd E. Murphy , Allison M. Okamura , David D. Yuh , Gregory D. Hager

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BibTeX

@MISC{Lin05automaticdetection,
    author = {Henry C. Lin and Izhak Shafran and Todd E. Murphy and Allison M. Okamura and David D. Yuh and Gregory D. Hager},
    title = {Automatic Detection and Segmentation of Robot-Assisted Surgical Motions. Medical Image Comuting and Computer-Assisted Intervention},
    year = {2005}
}

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Abstract

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 contextspecific 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. 1

Keyphrases

automatic detection robot-assisted surgical motion medical image comuting computer-assisted intervention surgical task surgical procedure document essential aspect surgical gesture recognition rate intuitive surgical da vinci system motion data contextspecific information motion enhancement raw motion data rich source key step surgical skill video data surgical training feedback robotic surgical system labelled sequence current technique

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