Abstract

Info

Title:: Pulmonary nodule segmentation using multi-branch U-Net based on feature enhancement

Author(s):: WEN Fan; YANG Ping; ZHANG Xin; TIAN Ji; WANG Jinhua; Smart City College, Beijing Union University, Beijing 100101, China

Keywords:: Keywords: pulmonary nodule 3D U-Net Transformer multi-scale residual block coordinate attention

Abstract:: Abstract: To address the problem of the inaccurate segmentation of pulmonary nodules caused by large scale differences, unclear boundary texture and serious background interference, a multi-branch U-Net based on feature enhancement is designed for pulmonary nodules segmentation. The method uses Transformer to extract structural features of pulmonary nodules and surrounding tissues from a global perspective, and shallow 3D U-Net to extract the texture features. The extracted both structural and texture features are used for feature enhancement. In addition, a multi-scale residual block and 3D coordinate attention module are designed to modify 3D U-Net for obtaining multi-scale information of pulmonary nodules with enhanced features. Based on 3D U-Net decoder, the deep semantic information is reused for accomplishing the segmentation of pulmonary nodules. The verification on LIDC-IDRI dataset shows that the proposed model has a precision, sensitivity and Dice similarity coefficient of 90.04%, 86.64% and 88.80%, respectively, exhibiting superior comprehensive segmentation performance as compared with other algorithms.