In the field of medical image segmentation the segmentation of lung parenchyma plays a crucial role in lung nodule detection. Pursuing higher accuracy while considering the number of model parameters has been one of the research hotspots. In this regard a new three-layer dense convolutional neural network DA-UNet was proposed. Firstly a dense convolution module was used instead of the normal 3 × 3 convolution used in traditional U-Net which enhances the feature extraction capability of the network by taking advantage of the reuse feature of dense convolution. Furthermore the network was pruned without affecting the accuracy of the segmentation network too much reducing the number of up and down samples and reducing unnecessary computational power consumption. In addition the Attention Gate was used to enhance the effect of information fusion between high and low levels in the jumping connection and atrous spatial pyramid pooling was used. The model incorporated feature information at different scales to further enhance task-related regional features in the images effectively reducing noise interference and improving network segmentation accuracy. It was demonstrated experimentally that the number of parameters of the three-time up-and-down sampling improved model was only about 75. 2% of those of the traditional four-time up-and-down sampling but the segmentation effect was not much affected. The LUNA competition lung image data set was used for segmentation verification. The experimental results showed that the accuracy reached 0. 991 and the IoU was 0. 961. Compared with the traditional UNet the evaluation index IoU was improved by 2. 9%. In the liver images of the generalization experiment the IoU of DA-UNet was stable at about 0. 929 while that of U-Net was stable at about 0. 838. These results prove that the improved U-Net has better segmentation results.