  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. In the MICCAI 2021 crossMoDA challenge, our results on
the final evaluation leaderboard showed that our proposed method has achieved
promising segmentation performance with mean dice score of 79.9% and 82.5% and
ASSD of 1.29 mm and 0.18 mm for VS tumor and cochlea, respectively. The cochlea
ASSD achieved by our method has outperformed all other competing methods as
well as the supervised nnU-Net.
