Zhang Pengyi, Zhong Yunxin, Deng Yulin, Tang Xiaoying, Li Xiaoqiong
School of Life Science, Beijing Institute of Technology Beijing 100081 China.
Key Laboratory of Convergence Medical Engineering System and Healthcare TechnologyMinistry of Industry and Information Technology Beijing 100081 China.
IEEE Access. 2020 Nov 16;8:207736-207757. doi: 10.1109/ACCESS.2020.3038279. eCollection 2020.
Automated infection measurement and COVID-19 diagnosis based on Chest X-ray (CXR) imaging is important for faster examination, where infection segmentation is an essential step for assessment and quantification. However, due to the heterogeneity of X-ray imaging and the difficulty of annotating infected regions precisely, learning automated infection segmentation on CXRs remains a challenging task. We propose a novel approach, called DRR4Covid, to learn COVID-19 infection segmentation on CXRs from digitally reconstructed radiographs (DRRs). DRR4Covid consists of an infection-aware DRR generator, a segmentation network, and a domain adaptation module. Given a labeled Computed Tomography scan, the infection-aware DRR generator can produce infection-aware DRRs with pixel-level annotations of infected regions for training the segmentation network. The domain adaptation module is designed to enable the segmentation network trained on DRRs to generalize to CXRs. The statistical analyses made on experiment results have indicated that our infection-aware DRRs are significantly better than standard DRRs in learning COVID-19 infection segmentation (p < 0.05) and the domain adaptation module can improve the infection segmentation performance on CXRs significantly (p < 0.05). Without using any annotations of CXRs, our network has achieved a classification score of (Accuracy: 0.949, AUC: 0.987, F1-score: 0.947) and a segmentation score of (Accuracy: 0.956, AUC: 0.980, F1-score: 0.955) on a test set with 558 normal cases and 558 positive cases. Besides, by adjusting the strength of radiological signs of COVID-19 infection in infection-aware DRRs, we estimate the detection limit of X-ray imaging in detecting COVID-19 infection. The estimated detection limit, measured by the percent volume of the lung that is infected by COVID-19, is 19.43% ± 16.29%, and the estimated lower bound of infected voxel contribution rate for significant radiological signs of COVID-19 infection is 20.0%. Our codes are made publicly available at https://github.com/PengyiZhang/DRR4Covid.
基于胸部X光（CXR）成像的自动感染测量和新冠病毒肺炎（COVID-19）诊断对于更快地进行检查很重要，其中感染分割是评估和量化的关键步骤。然而，由于X光成像的异质性以及精确标注感染区域的难度，在CXR上学习自动感染分割仍然是一项具有挑战性的任务。我们提出了一种名为DRR4Covid的新方法，用于从数字重建射线照片（DRR）中学习CXR上的COVID-19感染分割。DRR4Covid由一个感染感知DRR生成器、一个分割网络和一个域适应模块组成。给定一个标记的计算机断层扫描，感染感知DRR生成器可以生成带有感染区域像素级标注的感染感知DRR，用于训练分割网络。域适应模块旨在使在DRR上训练的分割网络能够推广到CXR。对实验结果进行的统计分析表明，我们的感染感知DRR在学习COVID-19感染分割方面明显优于标准DRR（p < 0.05），并且域适应模块可以显著提高CXR上的感染分割性能（p < 0.05）。在不使用任何CXR标注的情况下，我们的网络在一个包含558例正常病例和558例阳性病例的测试集上实现了分类分数（准确率：0.949，AUC：0.987，F1分数：0.947）和分割分数（准确率：0.956，AUC：0.980，F1分数：0.955）。此外，通过调整感染感知DRR中COVID-19感染的放射学特征强度，我们估计了X光成像在检测COVID-19感染方面的检测限。以被COVID-19感染的肺体积百分比衡量的估计检测限为19.43% ± 16.29%，并且COVID-19感染显著放射学特征的感染体素贡献率的估计下限为20.0%。我们的代码可在https://github.com/PengyiZhang/DRR4Covid上公开获取。
