  For proper generalization performance of convolutional neural networks (CNNs)
in medical image segmentation, the learnt features should be invariant under
particular non-linear shape variations of the input. To induce invariance in
CNNs to such transformations, we propose Probabilistic Augmentation of Data
using Diffeomorphic Image Transformation (PADDIT) -- a systematic framework for
generating realistic transformations that can be used to augment data for
training CNNs. We show that CNNs trained with PADDIT outperforms CNNs trained
without augmentation and with generic augmentation in segmenting white matter
hyperintensities from T1 and FLAIR brain MRI scans.
