  Unsupervised domain adaptation is useful in medical image segmentation.
Particularly, when ground truths of the target images are not available, domain
adaptation can train a target-specific model by utilizing the existing labeled
images from other modalities. Most of the reported works mapped images of both
the source and target domains into a common latent feature space, and then
reduced their discrepancy either implicitly with adversarial training or
explicitly by directly minimizing a discrepancy metric. In this work, we
propose a new framework, where the latent features of both domains are driven
towards a common and parameterized variational form, whose conditional
distribution given the image is Gaussian. This is achieved by two networks
based on variational auto-encoders (VAEs) and a regularization for this
variational approximation. Both of the VAEs, each for one domain, contain a
segmentation module, where the source segmentation is trained in a supervised
manner, while the target one is trained unsupervisedly. We validated the
proposed domain adaptation method using two cardiac segmentation tasks, i.e.,
the cross-modality (CT and MR) whole heart segmentation and the cross-sequence
cardiac MR segmentation. Results show that the proposed method achieved better
accuracies compared to two state-of-the-art approaches and demonstrated good
potential for cardiac segmentation. Furthermore, the proposed explicit
regularization was shown to be effective and efficient in narrowing down the
distribution gap between domains, which is useful for unsupervised domain
adaptation. Our code and data has been released via
https://zmiclab.github.io/projects.html.
