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.
@article{arxiv.2005.12662,
title = {A Deep Learning based Fast Signed Distance Map Generation},
author = {Zihao Wang and Clair Vandersteen and Thomas Demarcy and Dan Gnansia and Charles Raffaelli and Nicolas Guevara and Hervé Delingette},
journal= {arXiv preprint arXiv:2005.12662},
year = {2022}
}