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. © 2008 Springer Berlin Heidelberg.
CITATION STYLE
Morooka, K., Chen, X., Kurazume, R., Uchida, S., Hara, K., Iwashita, Y., & Hashizume, M. (2008). Real-time nonlinear FEM with neural network for simulating soft organ model deformation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5242 LNCS, pp. 742–749). Springer Verlag. https://doi.org/10.1007/978-3-540-85990-1_89
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