A method for constructing real-time FEM-based simulator of stomach behavior with large-scale deformation by neural networks
Ken’ichi Morooka, Tomoyuki Taguchi, Chen Xian, Ryo Kurazume, Makoto Hashizume, Tsutomu Hasegawa · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2012
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.