Phase Segmentation Methods for an Automatic Surgical Workflow Analysis
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 surgic...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2017-01-01
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| Series: | International Journal of Biomedical Imaging |
| Online Access: | http://dx.doi.org/10.1155/2017/1985796 |
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| _version_ | 1832563327016894464 |
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| author | Dinh Tuan Tran Ryuhei Sakurai Hirotake Yamazoe Joo-Ho Lee |
| author_facet | Dinh Tuan Tran Ryuhei Sakurai Hirotake Yamazoe Joo-Ho Lee |
| author_sort | Dinh Tuan Tran |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-f0fe6216a70f4214a49a502dad27388e |
| institution | Kabale University |
| issn | 1687-4188 1687-4196 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Biomedical Imaging |
| spelling | doaj-art-f0fe6216a70f4214a49a502dad27388e2025-02-03T01:20:29ZengWileyInternational Journal of Biomedical Imaging1687-41881687-41962017-01-01201710.1155/2017/19857961985796Phase Segmentation Methods for an Automatic Surgical Workflow AnalysisDinh Tuan Tran0Ryuhei Sakurai1Hirotake Yamazoe2Joo-Ho Lee3Graduate School of Information Science and Engineering, Ritsumeikan University, Kusatsu, Shiga, JapanCollege of Information Science and Engineering, Ritsumeikan University, Kusatsu, Shiga, JapanCollege of Information Science and Engineering, Ritsumeikan University, Kusatsu, Shiga, JapanCollege of Information Science and Engineering, Ritsumeikan University, Kusatsu, Shiga, JapanIn 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.http://dx.doi.org/10.1155/2017/1985796 |
| spellingShingle | Dinh Tuan Tran Ryuhei Sakurai Hirotake Yamazoe Joo-Ho Lee Phase Segmentation Methods for an Automatic Surgical Workflow Analysis International Journal of Biomedical Imaging |
| title | Phase Segmentation Methods for an Automatic Surgical Workflow Analysis |
| title_full | Phase Segmentation Methods for an Automatic Surgical Workflow Analysis |
| title_fullStr | Phase Segmentation Methods for an Automatic Surgical Workflow Analysis |
| title_full_unstemmed | Phase Segmentation Methods for an Automatic Surgical Workflow Analysis |
| title_short | Phase Segmentation Methods for an Automatic Surgical Workflow Analysis |
| title_sort | phase segmentation methods for an automatic surgical workflow analysis |
| url | http://dx.doi.org/10.1155/2017/1985796 |
| work_keys_str_mv | AT dinhtuantran phasesegmentationmethodsforanautomaticsurgicalworkflowanalysis AT ryuheisakurai phasesegmentationmethodsforanautomaticsurgicalworkflowanalysis AT hirotakeyamazoe phasesegmentationmethodsforanautomaticsurgicalworkflowanalysis AT jooholee phasesegmentationmethodsforanautomaticsurgicalworkflowanalysis |