  Recent research on COVID-19 suggests that CT imaging provides useful
information to assess disease progression and assist diagnosis, in addition to
help understanding the disease. There is an increasing number of studies that
propose to use deep learning to provide fast and accurate quantification of
COVID-19 using chest CT scans. The main tasks of interest are the automatic
segmentation of lung and lung lesions in chest CT scans of confirmed or
suspected COVID-19 patients. In this study, we compare twelve deep learning
algorithms using a multi-center dataset, including both open-source and
in-house developed algorithms. Results show that ensembling different methods
can boost the overall test set performance for lung segmentation, binary lesion
segmentation and multiclass lesion segmentation, resulting in mean Dice scores
of 0.982, 0.724 and 0.469, respectively. The resulting binary lesions were
segmented with a mean absolute volume error of 91.3 ml. In general, the task of
distinguishing different lesion types was more difficult, with a mean absolute
volume difference of 152 ml and mean Dice scores of 0.369 and 0.523 for
consolidation and ground glass opacity, respectively. All methods perform
binary lesion segmentation with an average volume error that is better than
visual assessment by human raters, suggesting these methods are mature enough
for a large-scale evaluation for use in clinical practice.
