S033 - A Deep Learning based Fast Signed Distance Map Generation

Zihao Wang, Clair Vandersteen, Thomas Demarcy, Dan Gnansia, Charles Raffaelli, Nicolas Guevara, Hervé Delingette

Show abstract - Show schedule - PDF - Reviews - Teaser

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.
Hide abstract

Poster Session #3 - 9:30 - 11:00 UTC-4 (Tuesday)
Hide schedule

Access paper channel

Short paper

Can't display slides, your browser doesn't support embedding PDFs. You can still download the slides:

Download slides