发表机构
Nagoya University; Keio University School of Medicine; Juntendo University（名古屋大学; 庆应义塾大学医学院; 顺天堂大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对COVID-19诊断需求，提出含2D/3D混合特征提取流的CNN，在1288例CT数据集上实现83.3%的平均分类准确率，优于无混合流的CNN，可辅助缓解医疗人力短缺。
AI 中文摘要
本文提出一种基于COVID-19病例可能性的胸部CT体积自动分类方法。新型冠状病毒肺炎（COVID-19）在全球传播，导致大量感染者和死亡病例，COVID-19患者数量的突然增加造成医疗机构人力短缺。计算机辅助诊断（CAD）系统可提供快速、定量的诊断结果，用于COVID-19的CAD系统能实现高效诊断流程，有助于缓解此类人力短缺问题。本文提出一种用于COVID-19诊断辅助的胸部CT体积自动分类方法，构建了具有2D/3D混合特征提取流的COVID-19分类卷积神经网络（CNN），该混合流旨在有效提取胸部CT体积等各向异性体积的图像特征，从CT体积的三个相互垂直平面提取图像特征后进行组合以完成分类。使用包含1288个CT体积的数据集对所提方法的分类准确率进行评估，平均分类准确率为83.3%，该准确率高于不具备2D和3D混合特征提取流的分类CNN的准确率。
英文摘要
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.
CommentsOral Presentation in SPIE Medical Imaging 2022
Journal refProceedings of SPIE Medical Imaging 2022, Computer-Aided Diagnosis, Vol. 12033
