Abstract
There are support systems for surgery using 3D object models of human organs such as surgical simulation and preoperative surgical planning. One of fundamental techniques in the support systems is to estimate the organ deformation in real-time. Finite element method (FEM) is one of well-known techniques for accurately simulating the physical behaviors of objects. However, FE analysis requires substantial computational expenses to obtain more realism simulation. To solve the problem, we have been constructing neural networks to estimate the nonlinear organ deformation. By using the training data generated by nonlinear FEM, the network learns the organ deformation when an external force acts on the organ surface. The computations in the network is the weighted sum of simple nonlinear functions. Therefore, our method achieves the real-time FE analysis while keeping the analysis accuracy. In this paper, we show the overview of our method and the experimental results obtained by our method.
