A Segmentation Model of Lung Parenchyma in Chest CT Based on ResUnet

Abstract

Segmentation of the lung parenchymal region in chest CT is an essential part of the automatic diagnosis of lung diseases. Therefore, the quality of the segmentation directly affects the results of the automatic diagnosis. This paper proposes a model for lung parenchymal segmentation in chest CT based on ResUnet. It introduces the residual learning unit to transfer low-level information and enhances the connection between layers using skip connections based on the U-Net architecture. Then, it achieves full feature extraction through down-convolution and up-sampling and uses image enhancement and data augmentation to preprocess the data set. Through experiment, the proposed segmentation model has better results than the IoU and Dice of other models and can better segment the lung parenchyma in chest CT.