| 改进U-Net的新冠肺炎图像分割方法 |
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| 引用本文： | 宋瑶,刘俊. 改进U-Net的新冠肺炎图像分割方法[J]. 计算机工程与应用, 2021, 57(19): 243-251. DOI: 10.3778/j.issn.1002-8331.2010-0207 |
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| 作者姓名： | 宋瑶 刘俊 |
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| 作者单位： | 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 4300652.武汉科技大学 计算机科学与技术学院,武汉 430065 |
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| 摘 要： | 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...
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| 关 键 词： | 新型冠状病毒肺炎(COVID-19) U-Net 语义分割 |
Improved U-Net Network for COVID-19 Image Segmentation |
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| SONG Yao,LIU Jun. Improved U-Net Network for COVID-19 Image Segmentation[J]. Computer Engineering and Applications, 2021, 57(19): 243-251. DOI: 10.3778/j.issn.1002-8331.2010-0207 |
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| Authors: | SONG Yao LIU Jun |
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| Affiliation: | 1.Hubei Key Laboratory of Intelligent Information Processing and Real-Time Industrial Systems（Wuhan University ofScience and Technology）, Wuhan 430065, China2.School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan 430065, China |
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| Abstract: | 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. |
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| Keywords: | COVID-19 U-Net semantic segmentation |
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