  Whole gland (WG) segmentation of the prostate plays a crucial role in
detection, staging and treatment planning of prostate cancer (PCa). Despite
promise shown by deep learning (DL) methods, they rely on the availability of a
considerable amount of annotated data. Augmentation techniques such as
translation and rotation of images present an alternative to increase data
availability. Nevertheless, the amount of information provided by the
transformed data is limited due to the correlation between the generated data
and the original. Based on the recent success of generative adversarial
networks (GAN) in producing synthetic images for other domains as well as in
the medical domain, we present a pipeline to generate WG segmentation masks and
synthesize T2-weighted MRI of the prostate based on a publicly available
multi-center dataset. Following, we use the generated data as a form of data
augmentation. Results show an improvement in the quality of the WG segmentation
when compared to standard augmentation techniques.

Image and Video Processing;Computer Vision and Pattern Recognition;Machine Learning