  The destitution of image data and corresponding expert annotations limit the
training capacities of AI diagnostic models and potentially inhibit their
performance. To address such a problem of data and label scarcity, generative
models have been developed to augment the training datasets. Previously
proposed generative models usually require manually adjusted annotations (e.g.,
segmentation masks) or need pre-labeling. However, studies have found that
these pre-labeling based methods can induce hallucinating artifacts, which
might mislead the downstream clinical tasks, while manual adjustment could be
onerous and subjective. To avoid manual adjustment and pre-labeling, we propose
a novel controllable and simultaneous synthesizer (dubbed CS$^2$) in this study
to generate both realistic images and corresponding annotations at the same
time. Our CS$^2$ model is trained and validated using high resolution CT (HRCT)
data collected from COVID-19 patients to realize an efficient infections
segmentation with minimal human intervention. Our contributions include 1) a
conditional image synthesis network that receives both style information from
reference CT images and structural information from unsupervised segmentation
masks, and 2) a corresponding segmentation mask synthesis network to
automatically segment these synthesized images simultaneously. Our experimental
studies on HRCT scans collected from COVID-19 patients demonstrate that our
CS$^2$ model can lead to realistic synthesized datasets and promising
segmentation results of COVID infections compared to the state-of-the-art
nnUNet trained and fine-tuned in a fully supervised manner.
