String Motif-Based Description of Tool Motion for Detecting Skill and Gestures in Robotic Surgery
The growing availability of data from robotic and laparoscopic surgery has created new opportunities to investigate the modeling and assessment of surgical technical performance and skill. However, previously published methods for modeling and assessment have not proven to scale well to large and diverse data sets. In this paper, we describe a new approach for simultaneous detection of gestures and skill that can be generalized to different surgical tasks. It consists of two parts: (1) descriptive curve coding (DCC), which transforms the surgical tool motion trajectory into a coded string using accumulated Frenet frames, and (2) common string model (CSM), a classification model using a similarity metric computed from longest common string motifs. We apply DCC-CSM method to detect surgical gestures and skill levels in two kinematic datasets (collected from the da Vinci surgical robot). DCC-CSM method classifies gestures and skill with 87.81% and 91.12% accuracy, respectively.
Keywordssurgical motion descriptive models gesture and skill classification geometry descriptive curve coding robotic surgery
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- 4.Ahmidi, N., Hager, G.D., Ishii, L., Gallia, G.L., Ishii, M.: Robotic Path Planning for Surgeon Skill Evaluation in Minimally-Invasive Sinus Surgery. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part I. LNCS, vol. 7510, pp. 471–478. Springer, Heidelberg (2012)CrossRefGoogle Scholar
- 5.Zappella, L., Béjar, B., Hager, G., Vidal, R.: Surgical gesture classification from video and kinematic data. Medical Image Analysis (2013)Google Scholar
- 6.Tao, L., Elhamifar, E., Khudanpur, S., Hager, G.D., Vidal, R.: Sparse Hidden Markov Models for Surgical Gesture Classification and Skill Evaluation. In: Abolmaesumi, P., Joskowicz, L., Navab, N., Jannin, P. (eds.) IPCAI 2012. LNCS, vol. 7330, pp. 167–177. Springer, Heidelberg (2012)CrossRefGoogle Scholar
- 8.Dosis, A., Bello, F., Gillies, D., Undre, S., Aggarwal, R., Darzi, A.: Laparoscopic task recognition using hidden Markov models. Studies in Health Technology and Informatics 111, 115–122 (2005)Google Scholar
- 9.Varadarajan, B.: Learning and inference algorithms for dynamical system models of dexterous motion. PhD thesis, Johns Hopkins University (2011)Google Scholar