  Automatic methods to segment the vestibular schwannoma (VS) tumors and the
cochlea from magnetic resonance imaging (MRI) are critical to VS treatment
planning. Although supervised methods have achieved satisfactory performance in
VS segmentation, they require full annotations by experts, which is laborious
and time-consuming. In this work, we aim to tackle the VS and cochlea
segmentation problem in an unsupervised domain adaptation setting. Our proposed
method leverages both the image-level domain alignment to minimize the domain
divergence and semi-supervised training to further boost the performance.
Furthermore, we propose to fuse the labels predicted from multiple models via
noisy label correction. Our results on the challenge validation leaderboard
showed that our unsupervised method has achieved promising VS and cochlea
segmentation performance with mean dice score of 0.8261 $\pm$ 0.0416; The mean
dice value for the tumor is 0.8302 $\pm$ 0.0772. This is comparable to the
weakly-supervised based method.
