Abstract

Info

Title:: Lung field segmentation using U-Net based on atrous spatial pyramid pooling

Author(s):: XIA Wenjing1; ZHOU Lazhen1; CHEN Hongchi1; LI Fangzuo1; 2; WU Ting3; ZHANG Xiang1; 2; 1. School of Medical Information Engineering, Gannan Medical University, Ganzhou 341000, China 2. Key Laboratory of Biomaterials and Biofabrication in Tissue Engineering, Gannan Medical University, Ganzhou 341000, China 3. The First Affiliated Hospital of Gannan Medical University, Ganzhou 341000, China

Keywords:: Keywords: chest X-ray image lung field segmentation U-Net atrous spatial pyramid pooling

Abstract:: Abstract: Objective The auto-segmentation of lung fields in chest X-ray images is a critical step in the screening and diagnosis of related diseases. In order to meet the requirements of computer-aided diagnosis system, a U-Net network based on atrous spatial pyramid pooling is proposed to automatically segment lung fields in chest X-ray images. Methods Atrous spatial pyramid pooling was introduced between encoding and decoding to enlarge the receptive field, and the image context was obtained at multiple scales to segment lung fields in chest radiographs. Montgomery chest X-ray set and the Shenzhen chest X-ray set were used for validation. The performance of atrous spatial pyramid pooling based U-Net in lung field segmentation was evaluated by the commonly used evaluation criteria for medical image segmentation (accuracy, Dice similarity coefficient and intersection over union). Results The validation showed that the accuracy, Dice similarity coefficient, and intersection over union were 98.29%, 96.61%, and 93.47%, respectively. Conclusion Compared with other methods, U-Net based on atrous spatial pyramid pooling for lung field segmentation learns more edge segmentation features, and achieves better segmentation results.