  We present a novel deep learning approach to categorical segmentation of lung
CTs of COVID-19 patients. Specifically, we partition the scans into healthy
lung tissues, non-lung regions, and two different, yet visually similar,
pathological lung tissues, namely, ground-glass opacity and consolidation. This
is accomplished via a unique, end-to-end hierarchical network architecture and
ensemble learning, which contribute to the segmentation and provide a measure
for segmentation uncertainty. The proposed framework achieves competitive
results and outstanding generalization capabilities for three COVID-19
datasets. Our method is ranked second in a public Kaggle competition for
COVID-19 CT images segmentation. Moreover, segmentation uncertainty regions are
shown to correspond to the disagreements between the manual annotations of two
different radiologists. Finally, preliminary promising correspondence results
are shown for our private dataset when comparing the patients' COVID-19
severity scores (based on clinical measures), and the segmented lung
pathologies. Code and data are available at our repository:
https://github.com/talbenha/covid-seg
