  Local deformations in medical modalities are common phenomena due to a
multitude of factors such as metallic implants or limited field of views in
magnetic resonance imaging (MRI). Completion of the missing or distorted
regions is of special interest for automatic image analysis frameworks to
enhance post-processing tasks such as segmentation or classification. In this
work, we propose a new generative framework for medical image inpainting,
titled ipA-MedGAN. It bypasses the limitations of previous frameworks by
enabling inpainting of arbitrary shaped regions without a prior localization of
the regions of interest. Thorough qualitative and quantitative comparisons with
other inpainting and translational approaches have illustrated the superior
performance of the proposed framework for the task of brain MR inpainting.
