TY - GEN
T1 - Single shot state detection in simulation-based laparoscopy training
AU - Peng, Kuo Shiuan
AU - Hong, Minsik
AU - Rozenblit, Jerzy
AU - Hamilton, Allan J.
N1 - Publisher Copyright: © 2019 Society for Modeling & Simulation International (SCS).
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - Laparoscopic surgery training
KW - Object state detection
KW - Semantic object
UR - https://www.scopus.com/pages/publications/85073686183
UR - https://www.scopus.com/pages/publications/85073686183#tab=citedBy
U2 - 10.23919/SpringSim.2019.8732863
DO - 10.23919/SpringSim.2019.8732863
M3 - Conference contribution
T3 - Simulation Series
BT - Simulation Series
PB - The Society for Modeling and Simulation International
T2 - 2019 Modeling and Simulation in Medicine, MSM 2019, Part of the 2019 Spring Simulation Multi-Conference, SpringSim 2019
Y2 - 29 April 2019 through 2 May 2019
ER -