  Segmentation of COVID-19 lesions from chest CT scans is of great importance
for better diagnosing the disease and investigating its extent. However, manual
segmentation can be very time consuming and subjective, given the lesions'
large variation in shape, size and position. On the other hand, we still lack
large manually segmented datasets that could be used for training machine
learning-based models for fully automatic segmentation. In this work, we
propose a new interactive and user-friendly tool for COVID-19 lesion
segmentation, which works by alternating automatic steps (based on level-set
segmentation and statistical shape modeling) with manual correction steps. The
present software was tested by two different expertise groups: one group of
three radiologists and one of three users with an engineering background.
Promising segmentation results were obtained by both groups, which achieved
satisfactory agreement both between- and within-group. Moreover, our
interactive tool was shown to significantly speed up the lesion segmentation
process, when compared to fully manual segmentation. Finally, we investigated
inter-observer variability and how it is strongly influenced by several
subjective factors, showing the importance for AI researchers and clinical
doctors to be aware of the uncertainty in lesion segmentation results.
