  Deep neural networks have shown exceptional learning capability and
generalizability in the source domain when massive labeled data is provided.
However, the well-trained models often fail in the target domain due to the
domain shift. Unsupervised domain adaptation aims to improve network
performance when applying robust models trained on medical images from source
domains to a new target domain. In this work, we present an approach based on
the Wasserstein distance guided disentangled representation to achieve 3D
multi-domain liver segmentation. Concretely, we embed images onto a shared
content space capturing shared feature-level information across domains and
domain-specific appearance spaces. The existing mutual information-based
representation learning approaches often fail to capture complete
representations in multi-domain medical imaging tasks. To mitigate these
issues, we utilize Wasserstein distance to learn more complete representation,
and introduces a content discriminator to further facilitate the representation
disentanglement. Experiments demonstrate that our method outperforms the
state-of-the-art on the multi-modality liver segmentation task.
