  Domain Adaptation is a technique to address the lack of massive amounts of
labeled data in unseen environments. Unsupervised domain adaptation is proposed
to adapt a model to new modalities using solely labeled source data and
unlabeled target domain data. Though many image-spaces domain adaptation
methods have been proposed to capture pixel-level domain-shift, such techniques
may fail to maintain high-level semantic information for the segmentation task.
For the case of biomedical images, fine details such as blood vessels can be
lost during the image transformation operations between domains. In this work,
we propose a model that adapts between domains using cycle-consistent loss
while maintaining edge details of the original images by enforcing an
edge-based loss during the adaptation process. We demonstrate the effectiveness
of our algorithm by comparing it to other approaches on two eye fundus vessels
segmentation datasets. We achieve 1.1 to 9.2 increment in DICE score compared
to the SOTA and ~5.2 increments compared to a vanilla CycleGAN implementation.
