  Generative adversarial networks (GANs) have been widely investigated for many
potential applications in medical imaging. DatasetGAN is a recently proposed
framework based on modern GANs that can synthesize high-quality segmented
images while requiring only a small set of annotated training images. The
synthesized annotated images could be potentially employed for many medical
imaging applications, where images with segmentation information are required.
However, to the best of our knowledge, there are no published studies focusing
on its applications to medical imaging. In this work, preliminary studies were
conducted to investigate the utility of DatasetGAN in medical imaging. Three
improvements were proposed to the original DatasetGAN framework, considering
the unique characteristics of medical images. The synthesized segmented images
by DatasetGAN were visually evaluated. The trained DatasetGAN was further
analyzed by evaluating the performance of a pre-defined image segmentation
technique, which was trained by the use of the synthesized datasets. The
effectiveness, concerns, and potential usage of DatasetGAN were discussed.
