论文标题
基于深度学习的快速签名距离地图生成
A Deep Learning based Fast Signed Distance Map Generation
论文作者
论文摘要
签名的距离图（SDM）是医学图像分析和机器学习中表面的常见表示。 3D参数形状的SDM的计算复杂性通常在许多应用中是瓶颈，因此限制了它们的兴趣。在本文中，我们提出了一个基于学习的SDM生成神经网络，该网络在通过4个形状参数参数化的三维耳蜗形状模型上进行了证明。所提出的SDM神经网络根据四个输入参数生成耳蜗签名的距离图，我们表明，与更经典的SDM生成方法相比，深度学习方法在计算时间内会提高60倍。因此，拟议的方法在准确性和效率之间取得了良好的权衡。
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.
