Image-based object state modeling of a transfer task in simulated surgical training

Kuo Shiuan Peng, Minsik Hong, Jerzy Rozenblit

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper proposes a real-time, image-based training scenario comprehension method. This method aims to support the visual and haptic guidance system for laparoscopic surgery skill training. The target task of the proposed approach is a simulation model of a peg transfer task, which is one of the hands-on exam topics in the Fundamentals of Laparoscopic Surgery certification. In this paper, a simulation process of an image-based object state modeling method is proposed. It generates a system object state of the transfer task to support the guidance system. A rule-based, intelligent system is used to discern the object state without the aid of any object template or model. This is the novelty of the proposed method.

Original languageEnglish (US)
Title of host publicationSimulation Series
EditorsJerzy W. Rozenblit, Johannes Sametinger
PublisherThe Society for Modeling and Simulation International
Pages58-69
Number of pages12
Edition6
ISBN (Electronic)9781510838253
StatePublished - 2017
Event4th Modeling and Simulation in Medicine Symposium, MSM 2017, Part of the 2017 Spring Simulation Multi-Conference, SpringSim 2017 - Virginia Beach, United States
Duration: Apr 23 2017Apr 26 2017

Publication series

NameSimulation Series
Number6
Volume49
ISSN (Print)0735-9276

Conference

Conference4th Modeling and Simulation in Medicine Symposium, MSM 2017, Part of the 2017 Spring Simulation Multi-Conference, SpringSim 2017
CountryUnited States
CityVirginia Beach
Period4/23/174/26/17

Keywords

  • Image understanding
  • Laparoscopy
  • Medical simulation
  • Simulation-based surgical training

ASJC Scopus subject areas

  • Computer Networks and Communications

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