  Single domain generalization aims to address the challenge of
out-of-distribution generalization problem with only one source domain
available. Feature distanglement is a classic solution to this purpose, where
the extracted task-related feature is presumed to be resilient to domain shift.
However, the absence of references from other domains in a single-domain
scenario poses significant uncertainty in feature disentanglement
(ill-posedness). In this paper, we propose a new framework, named
\textit{Domain Game}, to perform better feature distangling for medical image
segmentation, based on the observation that diagnostic relevant features are
more sensitive to geometric transformations, whilist domain-specific features
probably will remain invariant to such operations. In domain game, a set of
randomly transformed images derived from a singular source image is
strategically encoded into two separate feature sets to represent diagnostic
features and domain-specific features, respectively, and we apply forces to
pull or repel them in the feature space, accordingly. Results from cross-site
test domain evaluation showcase approximately an ~11.8% performance boost in
prostate segmentation and around ~10.5% in brain tumor segmentation compared to
the second-best method.
