Medical (CT) image generation with style
Arjun Krishna, Klaus D. Mueller · 2019
We propose the use of a conditional generative adversarial network (cGAN) to generate anatomically accurate full-sized CT images. Our approach is motivated by the recently discovered concept of style transfer and proposes to mix style and content of two separate CT images for generating a new image. We argue that by using these losses in a style transfer based architecture along with a cGAN, we can increase the size of clinically accurate, annotated datasets by multiple folds. Our framework can generate full-sized images with novel anatomy at spatial high resolution for all organs and only requires limited annotated input data of a few patients. The expanded datasets our framework generates can then be utilized within the many deep learning architectures designed for various processing tasks in medical imaging.