3D (c)GAN for Whole Body MR Synthesis

Abstract

Synthesis of images has recently seen many works that produce high-quality real world images. In the domain of medical imaging the application of deep generative models especially Generative Adversarial Networks (GANs) can be applied to many different tasks. Under the premise of the generation of high-quality images that match the distribution of the original data, the synthesized data can be used to increase the size of small datasets, or in combination with conditioning on meta data, to increase the size of underrepresented classes in the dataset. In this work we propose a model that generates 3D medical images. The model can easily be conditioned on meta data, for example available patient information. We evaluate the quality of the generated images and compare our model against the 3D-StyleGAN model which is also designed for 3D medical image synthesis.