  As the world progresses in technology and health, awareness of disease by
revealing asymptomatic signs improves. It is important to detect and treat
tumors in early stage as it can be life-threatening. Computer-aided
technologies are used to overcome lingering limitations facing disease
diagnosis, while brain tumor segmentation remains a difficult process,
especially when multi-modality data is involved. This is mainly attributed to
ineffective training due to lack of data and corresponding labelling. This work
investigates the feasibility of employing deep-fake image generation for
effective brain tumor segmentation. To this end, a Generative Adversarial
Network was used for image-to-image translation for increasing dataset size,
followed by image segmentation using a U-Net-based convolutional neural network
trained with deepfake images. Performance of the proposed approach is compared
with ground truth of four publicly available datasets. Results show improved
performance in terms of image segmentation quality metrics, and could
potentially assist when training with limited data.
