  Deep learning models trained on medical images from a source domain (e.g.
imaging modality) often fail when deployed on images from a different target
domain, despite imaging common anatomical structures. Deep unsupervised domain
adaptation (UDA) aims to improve the performance of a deep neural network model
on a target domain, using solely unlabelled target domain data and labelled
source domain data. However, current state-of-the-art methods exhibit reduced
performance when target data is scarce. In this work, we introduce a new data
efficient UDA method for multi-domain medical image segmentation. The proposed
method combines a novel VAE-based feature prior matching, which is
data-efficient, and domain adversarial training to learn a shared
domain-invariant latent space which is exploited during segmentation. Our
method is evaluated on a public multi-modality cardiac image segmentation
dataset by adapting from the labelled source domain (3D MRI) to the unlabelled
target domain (3D CT). We show that by using only one single unlabelled 3D CT
scan, the proposed architecture outperforms the state-of-the-art in the same
setting. Finally, we perform ablation studies on prior matching and domain
adversarial training to shed light on the theoretical grounding of the proposed
method.
