  With the widespread success of deep learning in biomedical image
segmentation, domain shift becomes a critical and challenging problem, as the
gap between two domains can severely affect model performance when deployed to
unseen data with heterogeneous features. To alleviate this problem, we present
a novel unsupervised domain adaptation network, for generalizing models learned
from the labeled source domain to the unlabeled target domain for
cross-modality biomedical image segmentation. Specifically, our approach
consists of two key modules, a conditional domain discriminator~(CDD) and a
category-centric prototype aligner~(CCPA). The CDD, extended from conditional
domain adversarial networks in classifier tasks, is effective and robust in
handling complex cross-modality biomedical images. The CCPA, improved from the
graph-induced prototype alignment mechanism in cross-domain object detection,
can exploit precise instance-level features through an elaborate prototype
representation. In addition, it can address the negative effect of class
imbalance via entropy-based loss. Extensive experiments on a public benchmark
for the cardiac substructure segmentation task demonstrate that our method
significantly improves performance on the target domain.
