Lung Parenchyma Segmentation Based on Improved Unet Network

Abstract

Segmentation of lung parenchyma is an essential link in the diagnosis of lung diseases and also the premise of disease analysis. The accuracy of lung parenchyma segmentation affects the diagnosis and treatment of lung diseases. The size of input data will be reduced by using the traditional Unet network. In this paper, an improved Unet network structure is proposed to segment lung parenchyma automatically. In the process of convolution, the size of input data is kept constant by padding same and dropout layer is introduced into the network. We use cross entropy loss function to train the model for the first time. After the model converges, we use custom Dice loss function to fine tune to improve the accuracy. By calculating Jaccard coefficient and DSC coefficient, our lung parenchyma segmentation method has a very high accuracy, which is better than the earlier researchers' segmentation algorithm. The significance of this study is to provide pretreatment for the diagnosis and treatment of lung diseases.