In a broad range of fields it may be desirable to reuse a supervised classification algorithm and apply it to a new data set.
There has recently been great progress in automatic segmentation of medical images with deep learning algorithms.
Generalization of voxelwise classifiers is hampered by differences between MRI-scanners, e. g. different acquisition protocols and field strengths.
Due to this acquisition related variation, classifiers trained on data from a specific scanner fail or under-perform when applied to data that was acquired differently.
Automatic segmentation in MR brain images is important for quantitative analysis in large-scale studies with images acquired at all ages.