  We demonstrate that AI models can accurately segment liver tumors without the
need for manual annotation by using synthetic tumors in CT scans. Our synthetic
tumors have two intriguing advantages: (I) realistic in shape and texture,
which even medical professionals can confuse with real tumors; (II) effective
for training AI models, which can perform liver tumor segmentation similarly to
the model trained on real tumors -- this result is exciting because no existing
work, using synthetic tumors only, has thus far reached a similar or even close
performance to real tumors. This result also implies that manual efforts for
annotating tumors voxel by voxel (which took years to create) can be
significantly reduced in the future. Moreover, our synthetic tumors can
automatically generate many examples of small (or even tiny) synthetic tumors
and have the potential to improve the success rate of detecting small liver
tumors, which is critical for detecting the early stages of cancer. In addition
to enriching the training data, our synthesizing strategy also enables us to
rigorously assess the AI robustness.
