Generative Adversarial Networks (GAN) have many potential medical imaging
applications, including data augmentation, domain adaptation, and model
explanation. Due to the limited embedded memory of Graphical Processing Units
(GPUs), most current 3D GAN models are trained on low-resolution medical
images. In this work, we propose a novel end-to-end GAN architecture that can
generate high-resolution 3D images. We achieve this goal by separating training
and inference. During training, we adopt a hierarchical structure that
simultaneously generates a low-resolution version of the image and a randomly
selected sub-volume of the high-resolution image. The hierarchical design has
two advantages: First, the memory demand for training on high-resolution images
is amortized among subvolumes. Furthermore, anchoring the high-resolution
subvolumes to a single low-resolution image ensures anatomical consistency
between subvolumes. During inference, our model can directly generate full
high-resolution images. We also incorporate an encoder with a similar
hierarchical structure into the model to extract features from the images.
Experiments on 3D thorax CT and brain MRI demonstrate that our approach
outperforms state of the art in image generation and clinical-relevant feature
extraction.Comment: 12 pages, 9 figures. Under revie