DC-CBAM-UNet++网络的肺结节图像分割方法

Lung Nodule Image Segmentation Method Based on DC-CBAM-UNet++Network

针对肺结节图像存在体积较小、形状不规则、边缘模糊,导致模型特征提取困难及分割精度不高等问题,提出一种基于UNet++结合空洞卷积与注意力机制的肺结节分割方法(DC-CBAM-UNet++)。该方法在传统UNet++网络基础上引入空洞卷积(DC-UNet++),并增加注意力机制加强特征图获得更多加权占比,使特征图获得更大的感受野。在LIDC肺结节公开数据集上的训练与验证结果表明,所提模型精确率、相似系数和交并比分别达到94.98%、90.86%、84.54%,证明了该方法的有效性,为分割肺结节图像提供了一种新方法。

To address the problems of small volume,irregular shape and blurred edges in lung nodule images,which lead to difficulty in fea⁃ture extraction and low segmentation accuracy,propose a lung nodule segmentation method(DC-CBAM-UNet++)based on UNet++com⁃bined with cavity convolution and attention mechanism.In order to obtain a larger sense field for the feature map,this method improves the tra⁃ditional UNet++network,introducing the null convolution(DC-UNet++)on the original basis,and also introducing the attention mechanism to enhance the feature map to obtain more weighted occupancy.Experiments were conducted using the LIDC lung nodule public dataset for training and validation,and the accuracy,similarity coefficient and cross-merge ratio reached 94.98%,90.86%and 84.54%,respectively,demonstrating the effectiveness of the method and providing a new method for segmenting pulmonary nodule images.