改进3D U-NET在CT影像分割中的应用研究

Research on Application of Improved 3D U-NET in CT Image Segmentation

对3D U-NET网络结构进行改进,提出一种CT影像中结节的自动分割方法。该项目在3D U-Net的基础上对其进行改进,改进的内容是卷积块操作采用3×3×3,Stride=1,padding=same的卷积,每个卷积后面相继增加Batch Normalization、Relu 和Dropout操作,池化被卷积操作代替,同时加入long skip connection长链接,实现浅层、低水平、粗粒度特征传递下去而不消失,提升网络对形状在10 mm以下但亮度高结节的轮廓表示能力,同时扩大了感受野、加速了网络的收敛。实现对CT影像的自动、准确描述。

The structure of 3D U-NET network is improved,and an automatic segmentation method of nodules in CT images is proposed.The project improves it on the basis of 3D U-NET.The improved content is that the convolution block operation adopts the convolution of 3×3×3,stripe=1 and padding=same.After each convolution,Batch Normalization,Relu and Dropout operations are added successively.Pooling is replaced by convolution operations.At the same time,long skip connection long links are added to realize the transmission of shallow layer,low level and coarse grained characteristics without disappearing,so as to improve the ability of the network to express the contour of nodules of shape less than 10 mm with high brightness,at the same time,it expands the receptive field and accelerates the convergence of the network.And then realize the automatic and accurate description of CT images.