A Deep Learning based Fast Signed Distance Map Generation

• URL: http://arxiv.org/abs/2005.12662v1
• Date: Tue, 26 May 2020 12:36:19 GMT
• Title: A Deep Learning based Fast Signed Distance Map Generation
• Authors: Zihao Wang, Clair Vandersteen, Thomas Demarcy, Dan Gnansia, Charles Raffaelli, Nicolas Guevara, Herv\'e Delingette
• Abstract summary: Signed distance map (SDM) is a common representation of surfaces in medical image analysis and machine learning. 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.
• Score: 4.298890193377769
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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