  Automatic segmentation of vestibular schwannoma (VS) and cochlea from
magnetic resonance imaging can facilitate VS treatment planning. Unsupervised
segmentation methods have shown promising results without requiring the
time-consuming and laborious manual labeling process. In this paper, we present
an approach for VS and cochlea segmentation in an unsupervised domain
adaptation setting. Specifically, we first develop a cross-site cross-modality
unpaired image translation strategy to enrich the diversity of the synthesized
data. Then, we devise a rule-based offline augmentation technique to further
minimize the domain gap. Lastly, we adopt a self-configuring segmentation
framework empowered by self-training to obtain the final results. On the
CrossMoDA 2022 validation leaderboard, our method has achieved competitive VS
and cochlea segmentation performance with mean Dice scores of 0.8178 $\pm$
0.0803 and 0.8433 $\pm$ 0.0293, respectively.
