title: Real-time nonlinear FEM with neural network for simulating soft organ model deformation.
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. 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.
authors.affiliation: Digital Medicine Initiative, Kyushu University, Maidashi 3-1-1, Higashi-ku, Fukuoka 812-8582, Japan. morooka@digital.med.kyushu-u.ac.jp
authors.forename: Ken'ichi
authors.initials: K
authors.lastname: Morooka
authors.forename: Xian
authors.initials: X
authors.lastname: Chen
authors.forename: Ryo
authors.initials: R
authors.lastname: Kurazume
authors.forename: Seiichi
authors.initials: S
authors.lastname: Uchida
authors.forename: Kenji
authors.initials: K
authors.lastname: Hara
authors.forename: Yumi
authors.initials: Y
authors.lastname: Iwashita
authors.forename: Makoto
authors.initials: M
authors.lastname: Hashizume
mesh_terms: D019245:Compressive Strength / Q000502:physiology; D003198:Computer Simulation; D003238:Connective Tissue / Q000502:physiology*; D004548:Elasticity; D020342:Finite Element Analysis; D006244:Hardness; D006801:Humans; D008954:Models, Biological; D016571:Neural Networks, Computer; D013314:Stress, Mechanical; D014781:Viscera / Q000502:physiology*; D014783:Viscosity