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Conference Papers Year : 2020

A Deep Learning based Fast Signed Distance Map Generation


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.
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hal-02570026 , version 1 (11-05-2020)


  • HAL Id : hal-02570026 , version 1


Zihao Wang, Clair Vandersteen, Thomas Demarcy, Dan Gnansia, Charles Raffaelli, et al.. A Deep Learning based Fast Signed Distance Map Generation. MIDL 2020 - Medical Imaging with Deep Learning, Jul 2020, Montréal, Canada. ⟨hal-02570026⟩
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