Data-Derived Models for Segmentation with Application to Surgical Assessment and Training

Authors:
Balakrishnan Varadarajan;Carol Reiley;Henry Lin;Sanjeev Khudanpur;Gregory Hager
Affiliations:
Department of Electrical and Computer Engineering,;Department of Computer Science, Johns Hopkins University, Baltimore, USA 21218;Department of Computer Science, Johns Hopkins University, Baltimore, USA 21218;Department of Electrical and Computer Engineering, and Department of Computer Science, Johns Hopkins University, Baltimore, USA 21218;Department of Computer Science, Johns Hopkins University, Baltimore, USA 21218
Venue:
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part I
Year:
2009

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Abstract

This paper addresses automatic skill assessment in robotic minimally invasive surgery. Hidden Markov models (HMMs) are developed for individual surgical gestures (or surgemes ) that comprise a typical bench-top surgical training task. It is known that such HMMs can be used to recognize and segment surgemes in previously unseen trials [1]. Here, the topology of each surgeme HMM is designed in a data-driven manner, mixing trials from multiple surgeons with varying skill levels, resulting in HMM states that model skill-specific sub-gestures . The sequence of HMM states visited while performing a surgeme are therefore indicative of the surgeon's skill level. This expectation is confirmed by the average edit distance between the state-level "transcripts" of the same surgeme performed by two surgeons with different expertise levels. Some surgemes are further shown to be more indicative of skill than others.