  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.
