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.
| Original language | English |
|---|---|
| Pages (from-to) | 742-749 |
| Number of pages | 8 |
| Journal | Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention |
| Volume | 11 |
| Issue number | Pt 2 |
| Publication status | Published - Jan 1 2008 |
All Science Journal Classification (ASJC) codes
- Medicine(all)
Cite this
Real-time nonlinear FEM with neural network for simulating soft organ model deformation. / Morooka, Ken'ichi; Chen, Xian; Kurazume, Ryo; Uchida, Seiichi; Hara, Kenji; Iwashita, Yumi; Hashizume, Makoto.
In: Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, Vol. 11, No. Pt 2, 01.01.2008, p. 742-749.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Real-time nonlinear FEM with neural network for simulating soft organ model deformation.
AU - Morooka, Ken'ichi
AU - Chen, Xian
AU - Kurazume, Ryo
AU - Uchida, Seiichi
AU - Hara, Kenji
AU - Iwashita, Yumi
AU - Hashizume, Makoto
PY - 2008/1/1
Y1 - 2008/1/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=82655161266&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=82655161266&partnerID=8YFLogxK
M3 - Article
C2 - 18982671
VL - 11
SP - 742
EP - 749
JO - Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
JF - Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
IS - Pt 2
ER -