  Deep learning models tend to underperform in the presence of domain shifts.
Domain transfer has recently emerged as a promising approach wherein images
exhibiting a domain shift are transformed into other domains for augmentation
or adaptation. However, with the absence of paired and annotated images, models
merely learned by adversarial loss and cycle consistency loss could result in
poor consistency of anatomy structures during the translation. Additionally,
the complexity of learning multi-domain transfer could significantly increase
with the number of target domains and source images. In this paper, we propose
a multi-domain transfer network, named MDT-Net, to address the limitations
above through perceptual supervision. Specifically, our model consists of a
single encoder-decoder network and multiple domain-specific transfer modules to
disentangle feature representations of the anatomy content and domain variance.
Owing to this architecture, the model could significantly reduce the complexity
when the translation is conducted among multiple domains. To demonstrate the
performance of our method, we evaluate our model qualitatively and
quantitatively on RETOUCH, an OCT dataset comprising scans from three different
scanner devices (domains). Furthermore, we take the transfer results as
additional training data for fluid segmentation to prove the advantage of our
model indirectly, i.e., in the task of data adaptation and augmentation.
Experimental results show that our method could bring universal improvement in
these segmentation tasks, which demonstrates the effectiveness and efficiency
of MDT-Net in multi-domain transfer.
