论文标题
DRR4COVID：从数字重建的X光片中学习自动化的Covid-19感染分割
DRR4Covid: Learning Automated COVID-19 Infection Segmentation from Digitally Reconstructed Radiographs
论文作者
论文摘要
基于胸部X射线（CXR）成像的自动感染测量和COVID-19诊断对于更快的检查很重要。我们提出了一种名为DRR4Covid的新方法，以从数字重建的X光片（DRR）中学习CXRS上的COVID-19诊断和感染分割。 DRR4COVID包括感染感知的DRR发生器，分类和/或分割网络以及域适应模块。感染感知的DRR发电机能够生产具有可调性的COVID-19感染的放射学迹象的DRR，并产生像素级感染注释，使DRR与DRR相匹配。引入了域的适应模块，以通过未标记的真实CXR和将DRR进行训练网络和CXR之间的域差异，并将其标记为DRR。我们通过使用基于最大平均差异（MMD）和一个基于FCN的Heverse的最大平均差异（MMD）和一个基于fcn的Heverser和A A Serififation Heverers和A A Semerer Networder提供了简单但有效的DRR4COVID实现。广泛的实验结果证实了我们方法的功效。具体而言，通过准确性，AUC和F1得分来量化性能，我们的网络无需使用CXRS的任何注释，在794个正常情况和794个正常情况和794个正常情况的测试集中，分类得分（0.954，0.989，0.953）和分段分数（0.957，0.981，0.956）。此外，我们通过调节合成DRR中COVID-19感染的放射学迹象的强度来估计X射线图像在检测COVID-19感染中的敏感性。肺中感染体素比例的估计检测极限为19.43％，对于COVID-19的显着放射学迹象，感染体素的贡献率的估计下限为20.0％。我们的代码将在https://github.com/pengyizhang/drr4covid上公开提供。
Automated infection measurement and COVID-19 diagnosis based on Chest X-ray (CXR) imaging is important for faster examination. We propose a novel approach, called DRR4Covid, to learn automated COVID-19 diagnosis and infection segmentation on CXRs from digitally reconstructed radiographs (DRRs). DRR4Covid comprises of an infection-aware DRR generator, a classification and/or segmentation network, and a domain adaptation module. The infection-aware DRR generator is able to produce DRRs with adjustable strength of radiological signs of COVID-19 infection, and generate pixel-level infection annotations that match the DRRs precisely. The domain adaptation module is introduced to reduce the domain discrepancy between DRRs and CXRs by training networks on unlabeled real CXRs and labeled DRRs together.We provide a simple but effective implementation of DRR4Covid by using a domain adaptation module based on Maximum Mean Discrepancy (MMD), and a FCN-based network with a classification header and a segmentation header. Extensive experiment results have confirmed the efficacy of our method; specifically, quantifying the performance by accuracy, AUC and F1-score, our network without using any annotations from CXRs has achieved a classification score of (0.954, 0.989, 0.953) and a segmentation score of (0.957, 0.981, 0.956) on a test set with 794 normal cases and 794 positive cases. Besides, we estimate the sensitive of X-ray images in detecting COVID-19 infection by adjusting the strength of radiological signs of COVID-19 infection in synthetic DRRs. The estimated detection limit of the proportion of infected voxels in the lungs is 19.43%, and the estimated lower bound of the contribution rate of infected voxels is 20.0% for significant radiological signs of COVID-19 infection. Our codes will be made publicly available at https://github.com/PengyiZhang/DRR4Covid.
