A method for constructing real-time FEM-based simulator of stomach behavior with large-scale deformation by neural networks

Ken'ikchi Morooka, Tomoyuki Taguchi, Xian Chen, Ryo Kurazume, Makoto Hashizume, Tsutomu Hasegawa

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

3 Citations (Scopus)

Abstract

This paper presents a method for simulating the behavior of stomach with large-scale deformation. This simulator is generated by the real-time FEM-based analysis by using a neural network. There are various deformation patterns of hollow organs by changing both its shape and volume. In this case, one network can not learn the stomach deformation with a huge number of its deformation pattern. To overcome the problem, we propose a method of constructing the simulator composed of multiple neural networks by 1)partitioning a training dataset into several subsets, and 2)selecting the data included in each subset. From our experimental results, we can conclude that our method can speed up the training process of a neural network while keeping acceptable accuracy.

Original languageEnglish
Title of host publicationMedical Imaging 2012
Subtitle of host publicationImage-Guided Procedures, Robotic Interventions, and Modeling
DOIs
Publication statusPublished - 2012
EventMedical Imaging 2012: Image-Guided Procedures, Robotic Interventions, and Modeling - San Diego, CA, United States
Duration: Feb 5 2012Feb 7 2012

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume8316
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2012: Image-Guided Procedures, Robotic Interventions, and Modeling
CountryUnited States
CitySan Diego, CA
Period2/5/122/7/12

Keywords

  • large dataset division
  • neural network
  • real-time FEM analysis
  • tissue deformation

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging

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