  Signed distance map (SDM) is a common representation of surfaces in medical
image analysis and machine learning. The computational complexity of SDM for 3D
parametric shapes is often a bottleneck in many applications, thus limiting
their interest. In this paper, we propose a learning based SDM generation
neural network which is demonstrated on a tridimensional cochlea shape model
parameterized by 4 shape parameters. The proposed SDM Neural Network generates
a cochlea signed distance map depending on four input parameters and we show
that the deep learning approach leads to a 60 fold improvement in the time of
computation compared to more classical SDM generation methods. Therefore, the
proposed approach achieves a good trade-off between accuracy and efficiency.
