This paper presents a new neural network methodology for modelling of soft tissue
deformation for surgical simulation. The proposed methodology formulates soft tissue
deformation and its dynamics as the neural propagation and dynamics of cellular neural
networks for real-time, realistic, and stable simulation of soft tissue deformation.
It develops two cellular neural network models; based on the bioelectric propagation
of biological tissues and principles of continuum mechanics, one cellular neural network
model is developed for propagation and distribution of mechanical load in soft tissues;
based on non-rigid mechanics of motion in continuum mechanics, the other cellular
neural network model is developed for governing model dynamics of soft tissue deformation.
The proposed methodology not only has computational advantage due to the collective
and simultaneous activities of neural cells to satisfy the real-time computational
requirement of surgical simulation, but also it achieves physical realism of soft
tissue deformation according to the bioelectric propagation manner of mechanical load
via dynamic neural activities. Furthermore, the proposed methodology also provides
stable model dynamics for soft tissue deformation via the nonlinear property of the
cellular neural network. Interactive soft tissue deformation with haptic feedback
is achieved via a haptic device. Simulations and experimental results show the proposed
methodology exhibits the nonlinear force-displacement relationship and associated
nonlinear deformation of soft tissues. Furthermore, not only isotropic and homogeneous
but also anisotropic and heterogeneous materials can be modelled via a simple modification
of electrical conductivity. values of mass points.