Abstract: Lung cancer is one of the cancers with the highest mortality in the world. As an important basis for early diagnosis of lung cancer, accurate segmentation of pulmonary nodules is particularly important. In order to help doctors diagnose lung lesions, an improved UNet lung nodule segmentation method is proposed. First, Efficient Channel Attention for Deep Convolutional Neural Networks(EcaNet) is introduced in the feature extraction part, which improves the UNet segmentation effect and makes it have good generalization ability. At the same time, in order to reduce the number of parameters of the model and improve the segmentation performance of the algorithm, a feature fusion model of depthwise separable convolution is proposed, which replaces the traditional convolution operation with depthwise separable convolution to complete feature fusion. According to the image characteristics of pulmonary nodules, the Dice Loss and the weighted cross entropy (WCE) are combined as a new loss function. To verify the effectiveness of the proposed algorithm Eca-UNet, our evaluated on the LIDC-IDRI public dataset of lung nodules. The results show that the DICE similarity coefficient and MIOU of the Eca-UNet algorithm are 10.47% and 7.34% higher than that of the UNet segmentation algorithm, respectively. At the same time, the training speed has increased by 10.10%, and the prediction speed has increased by 11.56%.

Key words: image segmentation, CT images of pulmonary nodules, attention mechanism, UNet, residual network