  Image normalization is a building block in medical image analysis.
Conventional approaches are customarily utilized on a per-dataset basis. This
strategy, however, prevents the current normalization algorithms from fully
exploiting the complex joint information available across multiple datasets.
Consequently, ignoring such joint information has a direct impact on the
performance of segmentation algorithms. This paper proposes to revisit the
conventional image normalization approach by instead learning a common
normalizing function across multiple datasets. Jointly normalizing multiple
datasets is shown to yield consistent normalized images as well as an improved
image segmentation. To do so, a fully automated adversarial and task-driven
normalization approach is employed as it facilitates the training of realistic
and interpretable images while keeping performance on-par with the
state-of-the-art. The adversarial training of our network aims at finding the
optimal transfer function to improve both the segmentation accuracy and the
generation of realistic images. We evaluated the performance of our normalizer
on both infant and adult brains images from the iSEG, MRBrainS and ABIDE
datasets. Results reveal the potential of our normalization approach for
segmentation, with Dice improvements of up to 57.5% over our baseline. Our
method can also enhance data availability by increasing the number of samples
available when learning from multiple imaging domains.
