Coronavirus disease 2019 (COVID-19) has been the main agenda of the whole world, since it came into sight in December 2019 as it has significantly affected the world economy and healthcare system. Given the effects of COVID-19 on pulmonary tissues, chest radiographic imaging has become a necessity for screening and monitoring the disease. Numerous studies have proposed Deep Learning approaches for the automatic diagnosis of COVID-19. Although these methods achieved astonishing performance in detection, they have used limited chest X-ray (CXR) repositories for evaluation, usually with a few hundred COVID-19 CXR images only. Thus, such data scarcity prevents reliable evaluation with the potential of overfitting. In addition, most studies showed no or limited capability in infection localization and severity grading of COVID-19 pneumonia. In this study, we address this urgent need by proposing a systematic and unified approach for lung segmentation and COVID-19 localization with infection quantification from CXR images. To accomplish this, we have constructed the largest benchmark dataset with 33,920 CXR images, including 11,956 COVID-19 samples, where the annotation of ground-truth lung segmentation masks is performed on CXRs by a novel human-machine collaborative approach. An extensive set of experiments was performed using the state-of-the-art segmentation networks, U-Net, U-Net++, and Feature Pyramid Networks (FPN). The developed network, after an extensive iterative process, reached a superior performance for lung region segmentation with Intersection over Union (IoU) of 96.11% and Dice Similarity Coefficient (DSC) of 97.99%. Furthermore, COVID-19 infections of various shapes and types were reliably localized with 83.05% IoU and 88.21% DSC. Finally, the proposed approach has achieved an outstanding COVID-19 detection performance with both sensitivity and specificity values above 99%.
翻译:97. 2019年科罗纳病毒疾病(COVID-19)是全世界的主要议程,自2019年12月出现以来,它一直是全世界的主要议程,因为它对世界经济和保健系统产生了重大影响。鉴于COVID-19对肺组织的影响,胸射线成像已成为检查和监测该疾病的必要手段。许多研究提出了对COVID-19进行自动诊断的深学习方法。虽然这些方法在检测中取得了惊人的性能,但它们使用了有限的胸X光(CXR)存储库用于评估,通常只有几百个COVID-19 CXR图像。因此,这种数据缺乏阻碍了对广泛适应潜力的可靠评估。此外,大多数研究表明,在CVID-19肺炎的局部传播和严重程度定级方面,没有或能力有限。 在这项研究中,我们提出了系统统一的方法来诊断COVID-19的局部化和COVID-19的局部化方法,从CXR图像中获得了惊人的检测,我们用33,920 CXR图像构建了最大的基准数据集,包括11,956 COVI-19的中央数据网络和中央介质介质的局部数据网络进行了广泛的地平段。