论文标题
用于医学图像细分的卷积神经网络
Convolutional neural networks for medical image segmentation
论文作者
论文摘要
在本文中，我们研究了卷积神经网络（CNN）的一些基本方面，重点是医疗图像分割。首先，我们讨论了CNN体系结构，从而突出了数据的空间起源，体素分类和接受场。其次，我们讨论了输入输出对的采样，从而突出了体素分类，斑块大小和接受场之间的相互作用。最后，我们对CNN体系结构进行分类和细分的关键变化进行了历史概述，从而有见识三个关键的CNN体系结构之间的关系：FCN，U-NET和DeepMedic。
In this article, we look into some essential aspects of convolutional neural networks (CNNs) with the focus on medical image segmentation. First, we discuss the CNN architecture, thereby highlighting the spatial origin of the data, voxel-wise classification and the receptive field. Second, we discuss the sampling of input-output pairs, thereby highlighting the interaction between voxel-wise classification, patch size and the receptive field. Finally, we give a historical overview of crucial changes to CNN architectures for classification and segmentation, giving insights in the relation between three pivotal CNN architectures: FCN, U-Net and DeepMedic.
