Learning Transferable Object-Centric Diffeomorphic Transformations for
  Data Augmentation in Medical Image Segmentation
Nilesh Kumar Prashnna K. Gyawali Sandesh Ghimire Linwei Wang
Obtaining labelled data in medical image segmentation is challenging due to
the need for pixel-level annotations by experts. Recent works have shown that
augmenting the object of interest with deformable transformations can help
mitigate this challenge. However, these transformations have been learned
globally for the image, limiting their transferability across datasets or
applicability in problems where image alignment is difficult. While
object-centric augmentations provide a great opportunity to overcome these
issues, existing works are only focused on position and random transformations
without considering shape variations of the objects. To this end, we propose a
novel object-centric data augmentation model that is able to learn the shape
variations for the objects of interest and augment the object in place without
modifying the rest of the image. We demonstrated its effectiveness in improving
kidney tumour segmentation when leveraging shape variations learned both from
within the same dataset and transferred from external datasets.