Segmentation of Pulmonary Parenchyma from Pulmonary CT Based on ResU-Net++ Model

Abstract

Pulmonary parenchyma segmentation is an important basic step in CT detection, which requires high accuracy and speed. According to the characteristics of lung structure, we present a method for segmenting CT images of lung parenchyma based on the ResU-net++ neural network model. The model preserves the deep feature parameters extracted from the residual blocks while paying attention to preserving the feature parameters obtained by transferring the shallow convolution blocks. The experimental results show that compared with U-net and U-net++ models, the lung parenchyma segmentation results using this method are more accurate, the accuracy of lung parenchyma edge identification is significantly improved, and it is more suitable for the diverse structures at both ends of the lung.