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

Ken'ichi Morooka; Tomoyuki Taguchi; Xian Chen; Ryo Kurazume; Makoto Hashizume; Tsutomu Hasegawa

doi:10.1117/12.911171

17 February 2012 A method for constructing real-time FEM-based simulator of stomach behavior with large-scale deformation by neural networks

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

Author Affiliations +

Proceedings Volume 8316, Medical Imaging 2012: Image-Guided Procedures, Robotic Interventions, and Modeling; 83160J (2012) https://doi.org/10.1117/12.911171
Event: SPIE Medical Imaging, 2012, San Diego, California, United States

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.

Citation Download Citation

Ken'ichi Morooka, Tomoyuki Taguchi, Xian Chen, Ryo Kurazume, Makoto Hashizume, and Tsutomu Hasegawa "A method for constructing real-time FEM-based simulator of stomach behavior with large-scale deformation by neural networks", Proc. SPIE 8316, Medical Imaging 2012: Image-Guided Procedures, Robotic Interventions, and Modeling, 83160J (17 February 2012); https://doi.org/10.1117/12.911171

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