SlabGAN: A method for generating efficient 3D anisotropic medical volumes using generative adversarial networks

Jason L. Granstedt, Varun A. Kelkar, Weimin Zhou, Mark A. Anastasio

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Generative adversarial networks (GANs) have proven useful for several medical imaging tasks, including image reconstruction and stochastic object model generation. Thus far, most of the work with GANs has been constrained to twodimensional images. Considering that medical imaging data are often inherently three-dimensional (3D), a 3D GAN would be a more principled way to synthesize realistic volumes. Training a 3D GAN is both computationally and memory intensive. However, prior work has not considered the anisotropic nature of many medical imaging systems. In this paper, the SlabGAN is proposed to reduce the inefficiencies associated with training a 3D GAN. The SlabGAN uses the progressive GAN architecture extended to 3D, but removes the requirement of the three dimensions being equal sizes. This permits the generation of anisotropic 3D volumes with large x and y dimensions. The SlabGAN is trained on MRI brain images from the fastMRI dataset to generate images of dimension 256×256×16. The x and y dimensions of these images are comparable to previously published results while requiring significantly fewer computational resources to generate. The trained SlabGAN is applicable to tasks such as 3D medical image reconstruction and thin-slice MR super resolution.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2021
Subtitle of host publicationImage Processing
EditorsIvana Isgum, Bennett A. Landman
ISBN (Electronic)9781510640214
StatePublished - 2021
EventMedical Imaging 2021: Image Processing - Virtual, Online, United States
Duration: Feb 15 2021Feb 19 2021

Publication series

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


ConferenceMedical Imaging 2021: Image Processing
Country/TerritoryUnited States
CityVirtual, Online


  • 3D
  • Anisotropic
  • Deep learning
  • GAN
  • Generative adversarial network
  • Image synthesis
  • MRI

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Biomaterials
  • Radiology Nuclear Medicine and imaging


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