  Robust cardiac image segmentation is still an open challenge due to the
inability of the existing methods to achieve satisfactory performance on unseen
data of different domains. Since the acquisition and annotation of medical data
are costly and time-consuming, recent work focuses on domain adaptation and
generalization to bridge the gap between data from different populations and
scanners. In this paper, we propose two data augmentation methods that focus on
improving the domain adaptation and generalization abilities of
state-to-the-art cardiac segmentation models. In particular, our "Resolution
Augmentation" method generates more diverse data by rescaling images to
different resolutions within a range spanning different scanner protocols.
Subsequently, our "Factor-based Augmentation" method generates more diverse
data by projecting the original samples onto disentangled latent spaces, and
combining the learned anatomy and modality factors from different domains. Our
extensive experiments demonstrate the importance of efficient adaptation
between seen and unseen domains, as well as model generalization ability, to
robust cardiac image segmentation.
