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Proceedings Paper

A method for constructing real-time FEM-based simulator of stomach behavior with large-scale deformation by neural networks
Author(s): Ken'ichi Morooka; Tomoyuki Taguchi; Xian Chen; Ryo Kurazume; Makoto Hashizume; Tsutomu Hasegawa
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Paper 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.4 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.

Paper Details

Date Published: 17 February 2012
PDF: 6 pages
Proc. SPIE 8316, Medical Imaging 2012: Image-Guided Procedures, Robotic Interventions, and Modeling, 83160J (17 February 2012); doi: 10.1117/12.911171
Show Author Affiliations
Ken'ichi Morooka, Kyushu Univ. (Japan)
Tomoyuki Taguchi, Kyushu Univ. (Japan)
Xian Chen, Yamaguchi Univ. (Japan)
Ryo Kurazume, Kyushu Univ. (Japan)
Makoto Hashizume, Kyushu Univ. (Japan)
Tsutomu Hasegawa, Kyushu Univ. (Japan)


Published in SPIE Proceedings Vol. 8316:
Medical Imaging 2012: Image-Guided Procedures, Robotic Interventions, and Modeling
David R. Holmes; Kenneth H. Wong, Editor(s)

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