  GANs are able to model accurately the distribution of complex,
high-dimensional datasets, e.g. images. This makes high-quality GANs useful for
unsupervised anomaly detection in medical imaging. However, differences in
training datasets such as output image dimensionality and appearance of
semantically meaningful features mean that GAN models from the natural image
domain may not work `out-of-the-box' for medical imaging, necessitating
re-implementation and re-evaluation. In this work we adapt and evaluate three
GAN models to the task of modelling 3D healthy image patches for pulmonary CT.
To the best of our knowledge, this is the first time that such an evaluation
has been performed. The DCGAN, styleGAN and the bigGAN architectures were
investigated due to their ubiquity and high performance in natural image
processing. We train different variants of these methods and assess their
performance using the FID score. In addition, the quality of the generated
images was evaluated by a human observer study, the ability of the networks to
model 3D domain-specific features was investigated, and the structure of the
GAN latent spaces was analysed. Results show that the 3D styleGAN produces
realistic-looking images with meaningful 3D structure, but suffer from mode
collapse which must be addressed during training to obtain samples diversity.
Conversely, the 3D DCGAN models show a greater capacity for image variability,
but at the cost of poor-quality images. The 3D bigGAN models provide an
intermediate level of image quality, but most accurately model the distribution
of selected semantically meaningful features. The results suggest that future
development is required to realise a 3D GAN with sufficient capacity for
patch-based lung CT anomaly detection and we offer recommendations for future
areas of research, such as experimenting with other architectures and
incorporation of position-encoding.
