Application of DatasetGAN in medical imaging: preliminary studies

Zong Fan, Varun Kelkar, Mark A. Anastasio, Hua Li

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations


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.

Original languageEnglish
Title of host publicationMedical Imaging 2022
Subtitle of host publicationImage Processing
EditorsOlivier Colliot, Ivana Isgum, Bennett A. Landman, Murray H. Loew
ISBN (Electronic)9781510649392
StatePublished - 2022
EventMedical Imaging 2022: Image Processing - Virtual, Online
Duration: Mar 21 2021Mar 27 2021

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
ISSN (Print)1605-7422


ConferenceMedical Imaging 2022: Image Processing
CityVirtual, Online


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