  Training segmentation networks requires large annotated datasets, but manual
annotation is time consuming and costly. We here investigate if the combination
of a noise-to-image GAN and an image-to-image GAN can be used to synthesize
realistic brain tumor images as well as the corresponding tumor annotations
(labels), to substantially increase the number of training images. The
noise-to-image GAN is used to synthesize new label images, while the
image-to-image GAN generates the corresponding MR image from the label image.
Our results indicate that the two GANs can synthesize label images and MR
images that look realistic, and that adding synthetic images improves the
segmentation performance, although the effect is small.
