Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (19): 243-251.DOI: 10.3778/j.issn.1002-8331.2010-0207

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Improved U-Net Network for COVID-19 Image Segmentation

SONG Yao, LIU Jun   

  1. 1.Hubei Key Laboratory of Intelligent Information Processing and Real-Time Industrial Systems(Wuhan University ofScience and Technology), Wuhan 430065, China
    2.School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan 430065, China
  • Online:2021-10-01 Published:2021-09-29



  1. 1.智能信息处理与实时工业系统湖北省重点实验室(武汉科技大学),武汉 430065
    2.武汉科技大学 计算机科学与技术学院,武汉 430065


The novel corona virus pneumonia(COVID-19) pandemic is spreading globally. Computerized Tomography(CT) imaging technology plays a vital role in the fight against global COVID-19. When diagnosing new coronary pneumonia, it will be helpful if the new coronary pneumonia focus area can be automatically and accurately segmented from the CT image, the doctor makes a more accurate and quick diagnosis. Aiming at the segmentation problem of new coronary pneumonia lesions, an automatic segmentation method based on the improved U-Net model is proposed. The EfficientNet-B0 network pre-trained on ImageNet is used in the encoder to extract features of effective information. In the decoder, the traditional up-sampling operation is replaced with a DUpsampling structure, in order to fully obtain the detailed feature information of the lesion edge, and finally the accuracy of the segmentation is improved through the integration of model snapshots. The experimental results on the public data set show that the accuracy, recall and Dice coefficients of the proposed algorithm are 84.24%, 80.43% and 85.12%, respectively. Compared with other segmentation networks, this method can effectively segment the neo-coronary pneumonia lesion area and has good segmentation performance.

Key words: COVID-19, U-Net, semantic segmentation


新型冠状病毒肺炎(COVID-19)大流行疾病正在全球范围内蔓延。计算机断层扫描(CT)影像技术,在抗击全球 COVID-19 的斗争中起着至关重要的作用,诊断新冠肺炎时,如果能够从CT图像中自动准确分割出新冠肺炎病灶区域,将有助于医生进行更准确和快速的诊断。针对新冠肺炎病灶分割问题,提出基于U-Net改进模型的自动分割方法。在编码器中运用了在 ImageNet 上预训练好的 EfficientNet-B0网络,对有效信息进行特征提取。在解码器中将传统的上采样操作换成DUpsampling结构,以此来充分获取病灶边缘的细节特征信息,最后通过模型快照的集成提高分割的精度。在公开数据集上的实验结果表明,所提算法的准确率、召回率和Dice系数分别为84.24%、80.43%和85.12%,与其他的语义分割算法相比,该方法能有效分割新冠肺炎病灶区域,具有良好的分割性能。

关键词: 新型冠状病毒肺炎(COVID-19), U-Net, 语义分割