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

Synthesizing brain tumor images and annotations by combining progressive growing GAN and SPADE