Synthetic Training with Generative Adversarial Networks for Segmentation of Microscopies

Medical imaging is often burdened with small available annotated data. In case of supervised deep learning algorithms a large amount of data is needed. One common strategy is to augment the given dataset for increasing the amount of training data. Recent researches show that the generation of synthetic images is a possible strategy to expand datasets. Especially, generative adversarial networks (GAN)s are promising candidates for generating new annotated training images. This work combines recent architectures of Generative Adversarial Networks in one pipeline to generate medical original and segmented image pairs for semantic segmentation. Results of training a U-Net with incorporated synthetic images as addition to common data augmentation are showing a performance boost compared to training without synthetic images from 77.99% to 80.23% average Jaccard Index.