  Limited amount of labelled training data are a common problem in medical
imaging. This makes it difficult to train a well-generalised model and
therefore often leads to failure in unknown domains. Hippocampus segmentation
from magnetic resonance imaging (MRI) scans is critical for the diagnosis and
treatment of neuropsychatric disorders. Domain differences in contrast or shape
can significantly affect segmentation. We address this issue by disentangling a
T1-weighted MRI image into its content and domain. This separation enables us
to perform a domain transfer and thus convert data from new sources into the
training domain. This step thus simplifies the segmentation problem, resulting
in higher quality segmentations. We achieve the disentanglement with the
proposed novel methodology 'Content Domain Disentanglement GAN', and we propose
to retrain the UNet on the transformed outputs to deal with GAN-specific
artefacts. With these changes, we are able to improve performance on unseen
domains by 6-13% and outperform state-of-the-art domain transfer methods.
