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

Development and evaluation of a 3D annotation software for interactive COVID-19 lesion segmentation in chest CT