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

doi:10.1117/12.911171

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 proceeding › Conference contribution

6 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 language	English
Title of host publication	Medical Imaging 2012
Subtitle of host publication	Image-Guided Procedures, Robotic Interventions, and Modeling
DOIs	https://doi.org/10.1117/12.911171
Publication status	Published - 2012
Event	Medical Imaging 2012: Image-Guided Procedures, Robotic Interventions, and Modeling - San Diego, CA, United States Duration: Feb 5 2012 → Feb 7 2012

Publication series

Name	Progress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume	8316
ISSN (Print)	1605-7422

Other

Other	Medical Imaging 2012: Image-Guided Procedures, Robotic Interventions, and Modeling
Country/Territory	United States
City	San Diego, CA
Period	2/5/12 → 2/7/12

All Science Journal Classification (ASJC) codes

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

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10.1117/12.911171

Cite this

Morooka, K., Taguchi, T., Chen, X., Kurazume, R., Hashizume, M., & Hasegawa, T. (2012). A method for constructing real-time FEM-based simulator of stomach behavior with large-scale deformation by neural networks. In Medical Imaging 2012: Image-Guided Procedures, Robotic Interventions, and Modeling [83160J] (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 8316). https://doi.org/10.1117/12.911171

A method for constructing real-time FEM-based simulator of stomach behavior with large-scale deformation by neural networks. / Morooka, Ken'ikchi; Taguchi, Tomoyuki; Chen, Xian et al.
Medical Imaging 2012: Image-Guided Procedures, Robotic Interventions, and Modeling. 2012. 83160J (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 8316).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Morooka, K, Taguchi, T, Chen, X, Kurazume, R, Hashizume, M & Hasegawa, T 2012, A method for constructing real-time FEM-based simulator of stomach behavior with large-scale deformation by neural networks. in Medical Imaging 2012: Image-Guided Procedures, Robotic Interventions, and Modeling., 83160J, Progress in Biomedical Optics and Imaging - Proceedings of SPIE, vol. 8316, Medical Imaging 2012: Image-Guided Procedures, Robotic Interventions, and Modeling, San Diego, CA, United States, 2/5/12. https://doi.org/10.1117/12.911171

Morooka K, Taguchi T, Chen X, Kurazume R, Hashizume M, Hasegawa T. A method for constructing real-time FEM-based simulator of stomach behavior with large-scale deformation by neural networks. In Medical Imaging 2012: Image-Guided Procedures, Robotic Interventions, and Modeling. 2012. 83160J. (Progress in Biomedical Optics and Imaging - Proceedings of SPIE). doi: 10.1117/12.911171

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AB - 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.

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

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