论文标题
在胸部X光片上对COVID-19空域疾病的自动检测和量化：一种新的方法，使用对基于CT基于CT的地面真实的数字重建X光片（DRR）训练的CNN实现放射科医生级的性能
Automated detection and quantification of COVID-19 airspace disease on chest radiographs: A novel approach achieving radiologist-level performance using a CNN trained on digital reconstructed radiographs (DRRs) from CT-based ground-truth
论文作者
论文摘要
目的：要利用源自上等模态（CT）的空域疾病（AD）的体积定量，该模态（CT）作为地面真理，将投影到数字重建的X光片（DRR）上介绍至：1）训练卷积神经网络以对配对CXRS上的空域疾病量化空间疾病； 2）将经过DRR培训的CNN与确认的COVID患者的CXR评估中的专家读者进行比较。 材料和方法：从2020年3月3日至5月至5月在美国东北部的一家三级医院开始，我们回顾性地选择了86名Covid-19患者（带有RT-PCR阳性）的队列，他们在48小时内接受了胸部CT和CXR。 COVID-19相关AD（POV）的地面真实体积百分比是通过在CT上的手动AD分段建立的。将最终的3D口罩投影到2D前后数字重建的X光片（DRR）中，以计算基于区域的AD百分比（POA）。卷积神经网络（CNN）经过由COVID-19和非covid-19患者的大规模CT数据集产生的DRR图像训练，自动分割肺，AD和CXR上的POA量化POA。通过计算相关性和平均绝对误差，将CNN POA结果与由两名专家读者和POV地面真相对CXR进行了量化的POA进行了比较。 结果：Bootstrap平均误差（MAE）和POA和POV之间的相关性为11.98％[11.05％-12.47％]和0.77 [0.70-0.82]的专家读者的平均值为9.56％-9.56％-9.78％[8.83％-10.22％]和0.78-0.81 [8.83％-10.22％]和0.78-0.81 [0.78-0.81 [0.73-0] [0.73-0.85]。 结论：我们使用CT衍生的空域定量对DRR进行了训练的CNN，在COVID-19的RT-PCR阳性的患者中，在CXR上实现了PRICE Sadiogist在COXR上的准确性水平。
Purpose: To leverage volumetric quantification of airspace disease (AD) derived from a superior modality (CT) serving as ground truth, projected onto digitally reconstructed radiographs (DRRs) to: 1) train a convolutional neural network to quantify airspace disease on paired CXRs; and 2) compare the DRR-trained CNN to expert human readers in the CXR evaluation of patients with confirmed COVID-19. Materials and Methods: We retrospectively selected a cohort of 86 COVID-19 patients (with positive RT-PCR), from March-May 2020 at a tertiary hospital in the northeastern USA, who underwent chest CT and CXR within 48 hrs. The ground truth volumetric percentage of COVID-19 related AD (POv) was established by manual AD segmentation on CT. The resulting 3D masks were projected into 2D anterior-posterior digitally reconstructed radiographs (DRR) to compute area-based AD percentage (POa). A convolutional neural network (CNN) was trained with DRR images generated from a larger-scale CT dataset of COVID-19 and non-COVID-19 patients, automatically segmenting lungs, AD and quantifying POa on CXR. CNN POa results were compared to POa quantified on CXR by two expert readers and to the POv ground-truth, by computing correlations and mean absolute errors. Results: Bootstrap mean absolute error (MAE) and correlations between POa and POv were 11.98% [11.05%-12.47%] and 0.77 [0.70-0.82] for average of expert readers, and 9.56%-9.78% [8.83%-10.22%] and 0.78-0.81 [0.73-0.85] for the CNN, respectively. Conclusion: Our CNN trained with DRR using CT-derived airspace quantification achieved expert radiologist level of accuracy in the quantification of airspace disease on CXR, in patients with positive RT-PCR for COVID-19.
