Phase Segmentation Methods for an Automatic Surgical Workflow Analysis

@inproceedings{Tran2017PhaseSM,
  title={Phase Segmentation Methods for an Automatic Surgical Workflow Analysis},
  author={Dinh Tuan Tran and Ryuhei Sakurai and Hirotake Yamazoe and Joo-Ho Lee},
  booktitle={Int. J. Biomedical Imaging},
  year={2017}
}
In this paper, we present robust methods for automatically segmenting phases in a specified surgical workflow by using latent Dirichlet allocation (LDA) and hidden Markov model (HMM) approaches. More specifically, our goal is to output an appropriate phase label for each given time point of a surgical workflow in an operating room. The fundamental idea behind our work lies in constructing an HMM based on observed values obtained via an LDA topic model covering optical flow motion features of… CONTINUE READING
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An improvement of surgical phase detection using latent dirichlet allocation and hidden markov model

  • D. T. Tran, R. Sakurai, J.-H. Lee
  • Smart Innovation, Systems and Technologies, vol…
  • 2016
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