Application of DatasetGAN in medical imaging: preliminary studies

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

doi:10.1117/12.2611191

4 April 2022 Application of DatasetGAN in medical imaging: preliminary studies

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

Proceedings Volume 12032, Medical Imaging 2022: Image Processing; 120321O (2022) https://doi.org/10.1117/12.2611191
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Abstract

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.

Conference Presentation

Citation Download Citation

Zong Fan, Varun Kelkar, Mark A. Anastasio, and Hua Li "Application of DatasetGAN in medical imaging: preliminary studies", Proc. SPIE 12032, Medical Imaging 2022: Image Processing, 120321O (4 April 2022); https://doi.org/10.1117/12.2611191

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