Application of three-dimensional orthogonal neural network to craniomaxillary reconstruction
The purpose of this investigation is to establish a practical method to predict and create surface a profile of bone defects by a well-trained 3-D orthogonal neural network. First, the coordinates of the skeletal positions around the boundary of bone defects are input into the 3-D orthogonal neural network to train it to learn the scattering characteristic. The 3-D orthogonal neural network avoids local minima and converges rapidly. After the neural network has been well trained, the mathematic model of the bone defect surface is generated, and the pixel positions are derived. Herein, to verify its performance the proposed method is applied on a patient with a craniofacial defect.
orthogonal neural network x-ray computed tomography rapid prototyping backpropagation network