  The recent achievements of Deep Learning rely on the test data being similar
in distribution to the training data. In an ideal case, Deep Learning models
would achieve Out-of-Distribution (OoD) Generalization, i.e. reliably make
predictions on out-of-distribution data. Yet in practice, models usually fail
to generalize well when facing a shift in distribution. Several methods were
thereby designed to improve the robustness of the features learned by a model
through Regularization- or Domain-Prediction-based schemes. Segmenting medical
images such as MRIs of the hippocampus is essential for the diagnosis and
treatment of neuropsychiatric disorders. But these brain images often suffer
from distribution shift due to the patient's age and various pathologies
affecting the shape of the organ. In this work, we evaluate OoD Generalization
solutions for the problem of hippocampus segmentation in MR data using both
fully- and semi-supervised training. We find that no method performs reliably
in all experiments. Only the V-REx loss stands out as it remains easy to tune,
while it outperforms a standard U-Net in most cases.
