用2D与3D混合卷积网络，自动区分新冠肺部CT影像
Automated classification method of COVID-19 cases from chest CT volumes using 2D and 3D hybrid CNN for anisotropic volumes
- 设计2D/3D混合特征提取结构，从不同切面捕捉肺部CT特征
- 在1288例CT数据上达到83.3%平均分类准确率
- 适合医疗资源紧张时快速辅助新冠诊断，提升效率
本文提出一种基于似然性的新冠（COVID-19）胸部CT体积自动化分类方法。新冠疫情全球蔓延，导致大量感染者和死亡病例，短时间内患者激增造成医疗机构人力严重短缺。计算机辅助诊断（CAD）系统可提供快速、定量的诊断结果，优化诊断流程并缓解人力压力。本研究提出一种融合2D与3D混合特征提取流的新冠分类卷积神经网络（CNN），专门用于处理如胸部CT这类各向异性体积数据。该结构在CT体积的三个相互垂直平面上提取图像特征，并融合后进行分类。在包含1288个CT体积的数据集上评估，所提方法平均分类准确率达83.3%，优于未采用2D/3D混合特征提取的CNN模型。
原文摘要 · Abstract (English)
This paper proposes an automated classification method of chest CT volumes based on likelihood of COVID-19 cases. Novel coronavirus disease 2019 (COVID-19) spreads over the world, causing a large number of infected patients and deaths. Sudden increase in the number of COVID-19 patients causes a manpower shortage in medical institutions. Computer-aided diagnosis (CAD) system provides quick and quantitative diagnosis results. CAD system for COVID-19 enables efficient diagnosis workflow and contributes to reduce such manpower shortage. This paper proposes an automated classification method of chest CT volumes for COVID-19 diagnosis assistance. We propose a COVID-19 classification convolutional neural network (CNN) that has a 2D/3D hybrid feature extraction flows. The 2D/3D hybrid feature extraction flows are designed to effectively extract image features from anisotropic volumes such as chest CT volumes for diagnosis. The flows extract image features on three mutually perpendicular planes in CT volumes and then combine the features to perform classification. Classification accuracy of the proposed method was evaluated using a dataset that contains 1288 CT volumes. An averaged classification accuracy was 83.3%. The accuracy was higher than that of a classification CNN which does not have 2D and 3D hybrid feature extraction flows.
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