Significant advances have been made towards building accu-
rate automatic segmentation systems for a variety of biomedical applica-
tions using machine learning. However, the performance of these systems
often degrades when they are applied on new data that differ from the
training data, for example, due to variations in imaging protocols. Man-
ually annotating new data for each test domain is not a feasible solution.
In this work we investigate unsupervised domain adaptation using ad-
versarial neural networks to train a segmentation method which is more
invariant to differences in the input data, and which does not require any
annotations on the test domain. Specifically, we learn domain-invariant
features by learning to counter an adversarial network, which attempts
to classify the domain of the input data by observing the activations of
the segmentation network. Furthermore, we propose a multi-connected
domain discriminator for improved adversarial training. Our system is
evaluated using two MR databases of subjects with traumatic brain in-
juries, acquired using different scanners and imaging protocols. Using
our unsupervised approach, we obtain segmentation accuracies which
are close to the upper bound of supervised domain adaptation.