Disentangled Representations for Domain-generalized Cardiac Segmentation
This work addresses the challenge of domain shift in medical imaging for cardiac segmentation, which is incremental as it builds on existing methods to enhance generalization.
The paper tackles the problem of robust cardiac image segmentation across unseen domains by proposing two data augmentation methods, Resolution Augmentation and Factor-based Augmentation, which improve domain adaptation and generalization in state-of-the-art models, though no concrete performance numbers are provided in the abstract.
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.