Brain tumor segmentation using synthetic MR images - A comparison of GANs and diffusion models.

ABSTRACT: Large annotated datasets are required for training deep learning models, but in medical imaging data sharing is often complicated due to ethics, anonymization and data protection legislation. Generative AI models, such as generative adversarial networks (GANs) and diffusion models, can today produce very realistic synthetic images, and can potentially facilitate data sharing. However, in order to share synthetic medical images it must first be demonstrated that they can be used for training different networks with acceptable performance. Here, we therefore comprehensively evaluate four GANs (progressive GAN, StyleGAN 1-3) and a diffusion model for the task of brain tumor segmentation (using two segmentation networks, U-Net and a Swin transformer). Our results show that segmentation network

SUBMITTER: Usman Akbar M 

PROVIDER: S-EPMC10904731 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature