Single shot state detection in simulation-based laparoscopy training

Kuo Shiuan Peng, Minsik Hong, Jerzy Rozenblit, Allan J. Hamilton

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

2 Scopus citations


A Single Shot State Detection (SSSD) method is proposed to support a laparoscopic surgery skills training system - Computer-Assisted Surgical Trainer (CAST). CAST actively assists a trainee with visual, audio, or force guidance during different surgical practice tasks. In each task, the guidance is provided according to the target object state, which is one of the key components of CAST. We propose SSSD using deep neural networks to detect object states in a single image. We first model semantic objects to recognize objects' state given a training task and then apply a deep learning algorithm, single shot detector (SSD), to detect the semantic objects. The contribution of this research is to present a unified object state model collaborating with a deep learning object detector, which can be applied to the surgical training simulator, as well as other visual sensing and automation systems.

Original languageEnglish (US)
Title of host publication2019 Spring Simulation Conference, SpringSim 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781510883888
StatePublished - Apr 2019
Event2019 Spring Simulation Conference, SpringSim 2019 - Tucson, United States
Duration: Apr 29 2019May 2 2019

Publication series

Name2019 Spring Simulation Conference, SpringSim 2019


Conference2019 Spring Simulation Conference, SpringSim 2019
Country/TerritoryUnited States


  • Laparoscopic surgery training
  • Object state detection
  • Semantic object

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Control and Optimization
  • Modeling and Simulation


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