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Domain adaptation has been widely adopted to transfer styles acrossmulti-vendors and multi-centers, as well as to complement the missingmodalities. In this challenge, we proposed an unsupervised domain adaptationframework for cross-modality vestibular schwannoma (VS) and cochleasegmentation and Koos grade prediction. We learn the shared representation fromboth ceT1 and hrT2 images and recover another modality from the latentrepresentation, and we also utilize proxy tasks of VS segmentation and brainparcellation to restrict the consistency of image structures in domainadaptation. After generating missing modalities, the nnU-Net model is utilizedfor VS and cochlea segmentation, while a semi-supervised contrastive learningpre-train approach is employed to improve the model performance for Koos gradeprediction. On CrossMoDA validation phase Leaderboard, our method received rank4 in task1 with a mean Dice score of 0.8394 and rank 2 in task2 withMacro-Average Mean Square Error of 0.3941. Our code is available athttps://github.com/fiy2W/cmda2022.superpolymerization.

Unsupervised Cross-Modality Domain Adaptation for Vestibular Schwannoma  Segmentation and Koos Grade Prediction based on Semi-Supervised Contrastive  Learning