Computer Engineering and Applications ›› 2020, Vol. 56 ›› Issue (17): 203-209.DOI: 10.3778/j.issn.1002-8331.1911-0124

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Improved U-Net Network for Lung Nodule Segmentation

ZHONG Sihua, GUO Xingming, ZHENG Yineng   

  1. 1.Chongqing Engineering Research Center for Medical Electronic Technology, College of Bioengineering, Chongqing University, Chongqing 400044, China
    2.Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
  • Online:2020-09-01 Published:2020-08-31



  1. 1.重庆大学 生物工程学院 重庆市医疗电子技术工程研究中心,重庆 400044
    2.重庆医科大学附属第一医院 放射科,重庆 400016


In order to accurately segment lung nodules from CT images, an improved U-Net based method is proposed for lung nodule segmentation. The dense connection introduced into the network can not only strengthen the transmission and utilization of features, but also avoid the vanishing gradient problem. Meanwhile, an improved hybrid loss function is adopted to address class imbalance problem. The experimental results on the public LIDC-IDRI database show that the proposed method can achieve Dice similarity coefficient, precision and recall of 84.48%,85.35% and 83.81%, respectively. Compared with some segmentation methods, the proposed method can accurately segment lung nodules with good performance.

Key words: lung nodules, U-Net, dense connection, semantic segmentation



关键词: 肺结节, U-Net, 密集连接, 语义分割