  Scarcity of annotated images hampers the building of automated solution for
reliable COVID-19 diagnosis and evaluation from CT. To alleviate the burden of
data annotation, we herein present a label-free approach for segmenting
COVID-19 lesions in CT via pixel-level anomaly modeling that mines out the
relevant knowledge from normal CT lung scans. Our modeling is inspired by the
observation that the parts of tracheae and vessels, which lay in the
high-intensity range where lesions belong to, exhibit strong patterns. To
facilitate the learning of such patterns at a pixel level, we synthesize
`lesions' using a set of surprisingly simple operations and insert the
synthesized `lesions' into normal CT lung scans to form training pairs, from
which we learn a normalcy-converting network (NormNet) that turns an 'abnormal'
image back to normal. Our experiments on three different datasets validate the
effectiveness of NormNet, which conspicuously outperforms a variety of
unsupervised anomaly detection (UAD) methods.
