  Convolutional neural networks (CNNs) have been applied to various automatic
image segmentation tasks in medical image analysis, including brain MRI
segmentation. Generative adversarial networks have recently gained popularity
because of their power in generating images that are difficult to distinguish
from real images.
  In this study we use an adversarial training approach to improve CNN-based
brain MRI segmentation. To this end, we include an additional loss function
that motivates the network to generate segmentations that are difficult to
distinguish from manual segmentations. During training, this loss function is
optimised together with the conventional average per-voxel cross entropy loss.
  The results show improved segmentation performance using this adversarial
training procedure for segmentation of two different sets of images and using
two different network architectures, both visually and in terms of Dice
coefficients.
