A Deep Learning based Fast Signed Distance Map Generation
This work addresses efficiency issues in medical image analysis and machine learning applications, though it is incremental as it applies an existing deep learning approach to a specific domain problem.
The paper tackles the computational bottleneck of generating signed distance maps (SDMs) for 3D parametric shapes by proposing a deep learning-based neural network, achieving a 60-fold improvement in computation time compared to classical methods.
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.