  Image normalization is a critical step in medical imaging. This step is often
done on a per-dataset basis, preventing current segmentation algorithms from
the full potential of exploiting jointly normalized information across multiple
datasets. To solve this problem, we propose an adversarial normalization
approach for image segmentation which learns common normalizing functions
across multiple datasets while retaining image realism. The adversarial
training provides an optimal normalizer that improves both the segmentation
accuracy and the discrimination of unrealistic normalizing functions. Our
contribution therefore leverages common imaging information from multiple
domains. The optimality of our common normalizer is evaluated by combining
brain images from both infants and adults. Results on the challenging iSEG and
MRBrainS datasets reveal the potential of our adversarial normalization
approach for segmentation, with Dice improvements of up to 59.6% over the
baseline.
