Journal of Frontiers of Computer Science and Technology ›› 2022, Vol. 16 ›› Issue (8): 1850-1864.DOI: 10.3778/j.issn.1673-9418.2203023

• Graphics and Image • Previous Articles     Next Articles

XR-MSF-Unet: Automatic Segmentation Model for COVID-19 Lung CT Images

XIE Juanying, ZHANG Kaiyun   

  1. School of Computer Science, Shaanxi Normal University, Xi’an 710119, China
  • Received:2022-02-14 Revised:2022-03-31 Online:2022-08-01 Published:2022-08-19
  • About author:XIE Juanying, born in 1971, Ph.D., professor, Ph.D. supervisor, senior member of CCF. Her research interests include machine learning, data mining, biomedical data analysis, etc.
    ZHANG Kaiyun, born in 1995, M.S. candidate, student member of CCF. Her research interests include machine learning and biomedical data analysis.
  • Supported by:
    the National Natural Science Foundation of China(62076159);the National Natural Science Foundation of China(12031010);the National Natural Science Foundation of China(61673251);the Fundamental Research Funds for the Central Universities of China(GK202105003);the Fundamental Research Funds for the Central Universities of China(GK201701006);the Graduate Innovation Fund of Shaanxi Normal University(2018TS078)


谢娟英, 张凯云   

  1. 陕西师范大学 计算机科学学院,西安 710119
  • 作者简介:谢娟英(1971—),女,陕西西安人,博士,教授,博士生导师,CCF高级会员,主要研究方向为机器学习、数据挖掘、生物医学数据分析等。
  • 基金资助:


The COVID-19 epidemic has threatened the human being. The automatic and accurate segmentation for the infected area of the COVID-19 CT images can help doctors to make correct diagnosis and treatment in time. However, it is very challenging to achieve perfect segmentation due to the diffuse infections of the COVID-19 to the patient lungs and irregular shapes of the infected areas and very similar infected areas to other lung tissues. To tackle these challenges, the XR-MSF-Unet model is proposed in this paper for segmenting the COVID-19 lung CT images of patients. The XR (X ResNet) convolution module is proposed in this model to replace the two-layer convolution operations of U-Net, so as to extract more informative features for achieving good segmentation results by multiple branches of XR. The plug and play attention mechanism module MSF (multi-scale features fusion module) is proposed in XR-MSF-Unet to fuse multi-scale features from different scales of reception fields, global, local and spatial features of CT images, so as to strengthen the detail segmentation effect of the model. Extensive experiments on the public COVID-19 CT images demonstrate that the proposed XR module can strengthen the capability of the XR-MSF-Unet model to extract effective features, and the MSF module plus XR module can effectively improve the segmentation capability of the XR-MSF-Unet model for the infected areas of the COVID-19 lung CT images. The proposed XR-MSF-Unet model obtains good segmentation results. Its segmentation perfor-mance is superior to that of the original U-Net model by 3.21, 5.96, 1.22 and 4.83 percentage points in terms of Dice, IOU, F1-Score and Sensitivity, and it defeats other same type of models, realizing automatic segmentation to the COVID-19 lung CT images.

Key words: COVID-19, CT image segmentation, U-Net, XR module, MSF module


新冠肺炎给人类带来极大威胁,自动精确分割新冠肺炎CT图像感染区域可以辅助医生进行诊断治疗,但新冠肺炎的弥漫性感染、感染区域形状多变、与其他肺部组织极易混淆等给CT图像分割带来挑战。为此,提出新冠肺炎肺部CT图像分割新模型XR-MSF-Unet,采用XR卷积模块代替U-Net的两层卷积,XR各分支的不同卷积核使模型能够提取更多有用特征;提出即插即用的融合多尺度特征的注意力模块MSF,融合不同感受野、全局、局部和空间特征,强化网络的细节分割效果。在COVID-19 CT公开数据集的实验表明:提出的XR模块能够增强模型的特征提取能力,提出的MSF模块结合XR模块,能够有效提高模型对新冠肺炎感染区域的分割效果;提出的XR-MSF-Unet模型取得了很好的分割效果,其Dice、IOU、F1-Score和Sensitivity指标分别比基模型U-Net的相应指标高出3.21、5.96、1.22和4.83个百分点,且优于同类模型的分割效果,实现了新冠肺炎肺部CT图像的自动有效分割。

关键词: 新冠肺炎, CT图像分割, U-Net, XR模块, MSF模块

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