  A deep learning model trained on some labeled data from a certain source
domain generally performs poorly on data from different target domains due to
domain shifts. Unsupervised domain adaptation methods address this problem by
alleviating the domain shift between the labeled source data and the unlabeled
target data. In this work, we achieve cross-modality domain adaptation, i.e.
between CT and MRI images, via disentangled representations. Compared to
learning a one-to-one mapping as the state-of-art CycleGAN, our model recovers
a many-to-many mapping between domains to capture the complex cross-domain
relations. It preserves semantic feature-level information by finding a shared
content space instead of a direct pixelwise style transfer. Domain adaptation
is achieved in two steps. First, images from each domain are embedded into two
spaces, a shared domain-invariant content space and a domain-specific style
space. Next, the representation in the content space is extracted to perform a
task. We validated our method on a cross-modality liver segmentation task, to
train a liver segmentation model on CT images that also performs well on MRI.
Our method achieved Dice Similarity Coefficient (DSC) of 0.81, outperforming a
CycleGAN-based method of 0.72. Moreover, our model achieved good generalization
to joint-domain learning, in which unpaired data from different modalities are
jointly learned to improve the segmentation performance on each individual
modality. Lastly, under a multi-modal target domain with significant diversity,
our approach exhibited the potential for diverse image generation and remained
effective with DSC of 0.74 on multi-phasic MRI while the CycleGAN-based method
performed poorly with a DSC of only 0.52.
