基于深度特征聚合网络的医学图像分割

利用卷积神经网络(CNN)进行医学图像分割时,通常将分割问题抽象为特征表示和参数优化问题,但在上采样和下采样过程中容易丢失特征信息,导致分割效果不理想.设计包含三级特征表示层和特征聚合模块的深度特征聚合网络结构DFA-Net.通过三级特征表示层提取基础特征同时聚合中间特征和深层特征,从而以聚合深层特征弥补CNN上采样与...

Medical Image Segmentation Based on Deep Feature Aggregation Network

Medical image segmentation using Convolutional Neural Network(CNN)usually simplifies the segmentation problem into the feature representation and parameter optimization problems.However,feature information is easily lost in the process of up sampling and down sampling,which leads to unsatisfactory segmentation effect.To solve the problem,this paper designs a deep feature aggregation network structure,DFA-Net,which includes the three-level feature representation layer and feature aggregation module.The three-level feature representation layer is used to extract basic features and aggregate middle features and deep features,so as to use the aggregated deep features to make up for the feature loss in CNN up sampling and down sampling.The feature aggregation module is used to aggregate and activate shallow features and deep features,and perform fine adjustment on them according to their complementary information.Experimental results on open datasets of brain images and fundus images show that DFA-Net can make full use of the information complementarity between deep features and shallow features to deal with isolated pixels in segmentation results and avoid the information loss caused by up sampling and down sampling.Its segmentation accuracy is higher than that of U-net,Unet++,SegNet,LadderNet and other methods.