Zhang XiaoQing, Wang GuangYu, Zhao Shu-Guang
Taizhou Institute of Science and Technology, Nanjing University of Science and Technology No.8, Meilan East Road Taizhou China.
College of Information Science and Technology, Donghua University Shanghai China.
Int J Imaging Syst Technol. 2021 Sep;31(3):1071-1086. doi: 10.1002/ima.22611. Epub 2021 Jun 4.
COVID-19 is a new type of respiratory infectious disease that poses a serious threat to the survival of human beings all over the world. Using artificial intelligence technology to analyze lung images of COVID-19 patients can achieve rapid and effective detection. This study proposes a COVSeg-NET model that can accurately segment ground glass opaque lesions in COVID-19 lung CT images. The COVSeg-NET model is based on the fully convolutional neural network model structure, which mainly includes convolutional layer, nonlinear unit activation function, maximum pooling layer, batch normalization layer, merge layer, flattening layer, sigmoid layer, and so forth. Through experiments and evaluation results, it can be seen that the dice coefficient, sensitivity, and specificity of the COVSeg-NET model are 0.561, 0.447, and 0.996 respectively, which are more advanced than other deep learning methods. The COVSeg-NET model can use a smaller training set and shorter test time to obtain better segmentation results.
新型冠状病毒肺炎（COVID-19）是一种新型呼吸道传染病，对全球人类的生存构成严重威胁。利用人工智能技术分析COVID-19患者的肺部图像可以实现快速有效的检测。本研究提出了一种COVSeg-NET模型，该模型可以准确分割COVID-19肺部CT图像中的磨玻璃样模糊病变。COVSeg-NET模型基于全卷积神经网络模型结构，主要包括卷积层、非线性单元激活函数、最大池化层、批归一化层、合并层、展平层、 sigmoid层等。通过实验和评估结果可以看出，COVSeg-NET模型的骰子系数、灵敏度和特异性分别为0.561、0.447和0.996，比其他深度学习方法更先进。COVSeg-NET模型可以使用较小的训练集和更短的测试时间来获得更好的分割结果。
