Abstract

Info

Title:: Airway segmentation method based on coordinate information and multi-scale parallel network

Author(s):: LIU Weipeng1; 2; 3; LI Jian1; 2; QI Yedong2; 3; REN Ziwen2; 3; WANG Yuan2; 3; 1. School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300401, China 2. State Key Lab of Reliability and Intelligence of Electrical Equipment, Tianjin 300401, China 3. School of Artificial Intelligence, Hebei University of Technology, Tianjin 300401, China

Keywords:: Keywords: airway segmentation coordinate information multi-scale feature aggregation parallel network

Abstract:: Abstract: An airway segmentation method based on coordinate information and multi-scale parallel network is proposed to solve the problem of insufficient accuracy of airway model in surgical navigation. Airway features at different scales are learned separately by a parallel network to address the feature conflict arising from airways of different sizes. Then, a coordinate guided up-sampling module is designed to utilize coordinate information from shallow features for guiding reconstruction of deeper features, thus restricting the spatial location of the target and improving the model accuracy. Finally, a channel guided multi-scale feature aggregation module is constructed to capture semantic details across multiple scales and investigate channel relationships between features at different scales. The proposed method and other models are trained and tested on two public datasets, namely LIDC-IDRI and EXACT09. Experimental results show that the proposed method achieves an average Dice coefficient of 93.20% which is 2.61% higher than 3D U-Net, a false positive rate of only 0.012%, a tree length detection rate of 88.59%, and a branch detection rate of 97.42%, demonstrating that the method can be applied to lung disease diagnosis or navigation bronchoscopy.