  Most publicly available brain MRI datasets are very homogeneous in terms of
scanner and protocols, and it is difficult for models that learn from such data
to generalize to multi-center and multi-scanner data. We propose a novel data
augmentation approach with the aim of approximating the variability in terms of
intensities and contrasts present in real world clinical data. We use a
Gaussian Mixture Model based approach to change tissue intensities
individually, producing new contrasts while preserving anatomical information.
We train a deep learning model on a single scanner dataset and evaluate it on a
multi-center and multi-scanner dataset. The proposed approach improves the
generalization capability of the model to other scanners not present in the
training data.
