Lung cancer has become one of the life-threatening killers. Lung disease need to be assisted by CT images taken doctor's diagnosis, and the segmented CT image of the lung parenchyma is the first step to help doctor diagnosis. For the problem of accurately segmenting the lung parenchyma, this paper proposes a segmentation method based on the combination of VGG-16 and dilated convolution. First of all, we use the first three parts of VGG-16 network structure to convolution and pooling the input image. Secondly, using multiple sets of dilated convolutions make the network has a large enough receptive field. Finally, the multi-scale convolution features are fused, and each pixel is predicted using MLP to segment the parenchymal region. Experimental results were produced over state of the art on 137 images which key metrics Dice similarity coefficient (DSC) is 0.9867. Experimental results show that this method can effectively segment the lung parenchymal area, and compared to other conventional methods better.

肺癌已经成为威胁生命的杀手之一。肺部疾病需要医生诊断的CT图像辅助，而肺实质的CT图像分割是帮助医生诊断的第一步。针对准确分割肺实质的问题，提出了一种基于VGG-16和扩张卷积相结合的分割方法。首先，我们使用VGG-16网络结构的前三部分来卷积和合并输入图像。其次，使用多组扩张卷积使网络具有足够大的接收场。最后，融合多尺度卷积特征，并使用MLP预测每个像素以分割实质区域。在137张图像上产生了最新技术的实验结果，关键指标Dice相似系数（DSC）为0.9867。实验结果表明，该方法可以有效分割肺实质区域，与其他常规方法相比效果更好。