TY - GEN
T1 - Application of DatasetGAN in medical imaging
T2 - Medical Imaging 2022: Image Processing
AU - Fan, Zong
AU - Kelkar, Varun
AU - Anastasio, Mark A.
AU - Li, Hua
N1 - Funding Information:
This work is original and has not been submitted for publication or presentation elsewhere. This work was supported in part by NIH awards R01EB020604, R01EB023045, R01NS102213, R01CA233873, Cancer Center at Illinois seed grant, Jump ARCHES Award, and DoD Award No. E01 W81XWH-21-1-0062.
Publisher Copyright:
© 2022 SPIE
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85131936304&partnerID=8YFLogxK
U2 - 10.1117/12.2611191
DO - 10.1117/12.2611191
M3 - Conference contribution
AN - SCOPUS:85131936304
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Medical Imaging 2022
A2 - Colliot, Olivier
A2 - Isgum, Ivana
A2 - Landman, Bennett A.
A2 - Loew, Murray H.
PB - SPIE
Y2 - 21 March 2021 through 27 March 2021
ER -