The automatic assignment of a severity score to the CT scans of patients affected by COVID-19 pneumonia could reduce the workload in radiology departments. This study aims at exploiting Artificial intelligence (AI) for the identification, segmentation and quantification of COVID-19 pulmonary lesions. We investigated the effects of using multiple datasets, heterogeneously populated and annotated according to different criteria. We developed an automated analysis pipeline, the LungQuant system, based on a cascade of two U-nets. The first one (U-net_1) is devoted to the identification of the lung parenchyma, the second one (U-net_2) acts on a bounding box enclosing the segmented lungs to identify the areas affected by COVID-19 lesions. Different public datasets were used to train the U-nets and to evaluate their segmentation performances, which have been quantified in terms of the Dice index. The accuracy in predicting the CT-Severity Score (CT-SS) of the LungQuant system has been also evaluated. Both Dice and accuracy showed a dependency on the quality of annotations of the available data samples. On an independent and publicly available benchmark dataset, the Dice values measured between the masks predicted by LungQuant system and the reference ones were 0.95$\pm$0.01 and 0.66$\pm$0.13 for the segmentation of lungs and COVID-19 lesions, respectively. The accuracy of 90% in the identification of the CT-SS on this benchmark dataset was achieved. We analysed the impact of using data samples with different annotation criteria in training an AI-based quantification system for pulmonary involvement in COVID-19 pneumonia. In terms of the Dice index, the U-net segmentation quality strongly depends on the quality of the lesion annotations. Nevertheless, the CT-SS can be accurately predicted on independent validation sets, demonstrating the satisfactory generalization ability of the LungQuant.

中文翻译：

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通过两个U-网络的级联来量化COVID-19肺炎的肺部受累情况：使用不同的注释标准对多个数据集进行训练和评估

将严重度评分自动分配给受COVID-19肺炎影响的患者的CT扫描可以减少放射科的工作量。这项研究旨在利用人工智能（AI）对COVID-19肺部病变进行识别，分割和量化。我们调查了使用多个数据集（根据不同标准进行异构填充和注释）的效果。我们基于两个U网络的级联，开发了一个自动分析管道LungQuant系统。第一个（U-net_1）专用于肺实质的识别，第二个（U-net_2）作用于包围分割的肺部的边界框上，以识别受COVID-19病变影响的区域。使用了不同的公共数据集来训练U网络并评估其细分效果，其中已根据Dice索引进行了量化。还评估了预测LungQuant系统的CT严重度评分（CT-SS）的准确性。骰子和准确性都显示出对可用数据样本注释质量的依赖性。在一个独立且可公开获得的基准数据集上，由LungQuant系统预测的口罩与参考口罩之间测得的Dice值分别为针对肺和COVID-19病变的分割的0.95 $ \ pm $ 0.01和0.66 $ \ pm $ 0.13。在该基准数据集上识别CT-SS的准确率达到了90％。我们分析了使用具有不同注释标准的数据样本在训练基于AI的COVID-19肺炎受累量化系统的培训中所产生的影响。就骰子索引而言，U-net分割质量在很大程度上取决于病变注释的质量。但是，可以在独立的验证集上准确预测CT-SS，这证明了LungQuant具有令人满意的泛化能力。