  The insertion of deep learning in medical image analysis had lead to the
development of state-of-the art strategies in several applications such a
disease classification, as well as abnormality detection and segmentation.
However, even the most advanced methods require a huge and diverse amount of
data to generalize. Because in realistic clinical scenarios, data acquisition
and annotation is expensive, deep learning models trained on small and
unrepresentative data tend to outperform when deployed in data that differs
from the one used for training (e.g data from different scanners). In this
work, we proposed a domain adaptation methodology to alleviate this problem in
segmentation models. Our approach takes advantage of the properties of
adversarial domain adaptation and consistency training to achieve more robust
adaptation. Using two datasets with white matter hyperintensities (WMH)
annotations, we demonstrated that the proposed method improves model
generalization even in corner cases where individual strategies tend to fail.
