Simulation of manual and automatic navigation of magnetically controlled wireless capsule endoscopy examination of human gastric

Xueshen Li, Yu Gan, David Duan, Xiao Yang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The magnetically controlled capsule endoscopy (MCCE) is an emerging modality for assessing gastrointestinal disorders due to its advantages. However, current assignments of MCCE rely on manual controlling and gastric landmarks, which are prone to omissions. We improve the scanning protocol of the MCCE in human gastric using both manual and automatic controlling methods. We design a quantitative scanning coverage ratio to measure the process of MCCE scanning within the human gastric. The proposed scanning coverage ratio is capable of guiding the manual and automatic scanning process of human gastric. Moreover, we design a deep reinforcement learning (DRL) controller for automatically navigating the capsule. Our DRL controller achieves a higher coverage ratio compared to previous research.

Original languageEnglish
Title of host publicationEndoscopic Microscopy XIX
EditorsGuillermo J. Tearney, Thomas D. Wang, Melissa J. Suter
ISBN (Electronic)9781510668997
DOIs
StatePublished - 2024
EventEndoscopic Microscopy XIX 2024 - San Francisco, United States
Duration: 27 Jan 202428 Jan 2024

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume12820
ISSN (Print)1605-7422

Conference

ConferenceEndoscopic Microscopy XIX 2024
Country/TerritoryUnited States
CitySan Francisco
Period27/01/2428/01/24

Keywords

  • Deep learning
  • Human gastric examination
  • Magnetically controlled capsule endoscopy
  • Reinforcement learning

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