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International Journal of Biomedical Imaging
Volume 2017 (2017), Article ID 1985796, 17 pages
Research Article

Phase Segmentation Methods for an Automatic Surgical Workflow Analysis

1Graduate School of Information Science and Engineering, Ritsumeikan University, Kusatsu, Shiga, Japan
2College of Information Science and Engineering, Ritsumeikan University, Kusatsu, Shiga, Japan

Correspondence should be addressed to Dinh Tuan Tran

Received 28 October 2016; Accepted 5 January 2017; Published 19 March 2017

Academic Editor: Jingbing Li

Copyright © 2017 Dinh Tuan Tran et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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 general working contexts, including medical staff, equipment, and materials. We have an awareness of such working contexts by using multiple synchronized cameras to capture the surgical workflow. Further, we validate the robustness of our methods by conducting experiments involving up to 12 phases of surgical workflows with the average length of each surgical workflow being 12.8 minutes. The maximum average accuracy achieved after applying leave-one-out cross-validation was 84.4%, which we found to be a very promising result.