Fine-Grained Unsupervised Cross-Modality Domain Adaptation for
  Vestibular Schwannoma Segmentation
Luyi Han Tao Tan Ritse Mann
The domain adaptation approach has gained significant acceptance in
transferring styles across various vendors and centers, along with filling the
gaps in modalities. However, multi-center application faces the challenge of
the difficulty of domain adaptation due to their intra-domain differences. We
focus on introducing a fine-grained unsupervised framework for domain
adaptation to facilitate cross-modality segmentation of vestibular schwannoma
(VS) and cochlea. We propose to use a vector to control the generator to
synthesize a fake image with given features. And then, we can apply various
augmentations to the dataset by searching the feature dictionary. The diversity
augmentation can increase the performance and robustness of the segmentation
model. On the CrossMoDA validation phase Leaderboard, our method received a
mean Dice score of 0.765 and 0.836 on VS and cochlea, respectively.