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A Deep Learning based Fast Signed Distance Map Generation

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.
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Contributor : Zihao Wang Connect in order to contact the contributor
Submitted on : Monday, May 11, 2020 - 5:04:48 PM
Last modification on : Sunday, May 1, 2022 - 3:16:56 AM


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  • 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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