A Deep Learning based Fast Signed Distance Map Generation

Jan 25, 2020 Blind Submission readers: everyone Show Bibtex
  • Track: short paper
  • Keywords: Signed Distance Map, Deep Learning
  • Abstract: 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.
  • Paper Type: both
  • Presentation Upload:  zip
  • Presentation Upload Agreement: I agree that my presentation material (videos and slides) will be made public.
0 Replies