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

Abstract

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 publicationSimulation Series
PublisherThe Society for Modeling and Simulation International
Edition5
ISBN (Electronic)9781510892521, 9781510892538, 9781510892545, 9781510892552, 9781510892569
DOIs
StatePublished - Jan 1 2019
Event2019 Modeling and Simulation in Medicine, MSM 2019, Part of the 2019 Spring Simulation Multi-Conference, SpringSim 2019 - Tucson, United States
Duration: Apr 29 2019May 2 2019

Publication series

NameSimulation Series
Number5
Volume51
ISSN (Print)0735-9276

Conference

Conference2019 Modeling and Simulation in Medicine, MSM 2019, Part of the 2019 Spring Simulation Multi-Conference, SpringSim 2019
CountryUnited States
CityTucson
Period4/29/195/2/19

Fingerprint

Laparoscopy
Semantics
Detectors
Surgery
Learning algorithms
Computer systems
Automation
Simulators
Deep learning

Keywords

  • Laparoscopic surgery training
  • Object state detection
  • Semantic object

ASJC Scopus subject areas

  • Computer Networks and Communications

Cite this

Peng, K. S., Hong, M., Rozenblit, J., & Hamilton, A. J. (2019). Single shot state detection in simulation-based laparoscopy training. In Simulation Series (5 ed.). (Simulation Series; Vol. 51, No. 5). The Society for Modeling and Simulation International. https://doi.org/10.23919/SpringSim.2019.8732863

Single shot state detection in simulation-based laparoscopy training. / Peng, Kuo Shiuan; Hong, Minsik; Rozenblit, Jerzy; Hamilton, Allan J.

Simulation Series. 5. ed. The Society for Modeling and Simulation International, 2019. (Simulation Series; Vol. 51, No. 5).

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

Peng, KS, Hong, M, Rozenblit, J & Hamilton, AJ 2019, Single shot state detection in simulation-based laparoscopy training. in Simulation Series. 5 edn, Simulation Series, no. 5, vol. 51, The Society for Modeling and Simulation International, 2019 Modeling and Simulation in Medicine, MSM 2019, Part of the 2019 Spring Simulation Multi-Conference, SpringSim 2019, Tucson, United States, 4/29/19. https://doi.org/10.23919/SpringSim.2019.8732863
Peng KS, Hong M, Rozenblit J, Hamilton AJ. Single shot state detection in simulation-based laparoscopy training. In Simulation Series. 5 ed. The Society for Modeling and Simulation International. 2019. (Simulation Series; 5). https://doi.org/10.23919/SpringSim.2019.8732863
Peng, Kuo Shiuan ; Hong, Minsik ; Rozenblit, Jerzy ; Hamilton, Allan J. / Single shot state detection in simulation-based laparoscopy training. Simulation Series. 5. ed. The Society for Modeling and Simulation International, 2019. (Simulation Series; 5).
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