Real-Time Nonlinear FEM with Neural Network for Simulating Soft Organ Model Deformation

Authors:
Ken'Ichi Morooka;Xian Chen;Ryo Kurazume;Seiichi Uchida;Kenji Hara;Yumi Iwashita;Makoto Hashizume
Affiliations:
Digital Medicine Initiative, Kyushu University,;Digital Medicine Initiative, Kyushu University,;Graduate School of Infomation Science and Electrical Engineering, Kyushu University,;Graduate School of Infomation Science and Electrical Engineering, Kyushu University,;Faculty of Design, Kyushu University, Fukuoka, Japan 812-8582;Graduate School of Infomation Science and Electrical Engineering, Kyushu University,;Digital Medicine Initiative, Kyushu University,
Venue:
MICCAI '08 Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, Part II
Year:
2008

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Abstract

This paper presents a new method for simulating the deformation of organ models by using a neural network. The proposed method is based on the idea proposed by Chen et al. [2] that a deformed model can be estimated from the superposition of basic deformation modes. The neural network finds a relationship between external forces and the models deformed by the forces. The experimental results show that the trained network can achieve a real-time simulation while keeping the acceptable accuracy compared with the nonlinear FEM computation.