  The Koos grading scale is a classification system for vestibular schwannoma
(VS) used to characterize the tumor and its effects on adjacent brain
structures. The Koos classification captures many of the characteristics of
treatment deci-sions and is often used to determine treatment plans. Although
both contrast-enhanced T1 (ceT1) scanning and high-resolution T2 (hrT2)
scanning can be used for Koos Classification, hrT2 scanning is gaining interest
because of its higher safety and cost-effectiveness. However, in the absence of
annotations for hrT2 scans, deep learning methods often inevitably suffer from
performance deg-radation due to unsupervised learning. If ceT1 scans and their
annotations can be used for unsupervised learning of hrT2 scans, the
performance of Koos classifi-cation using unlabeled hrT2 scans will be greatly
improved. In this regard, we propose an unsupervised cross-modality domain
adaptation method based on im-age translation by transforming annotated ceT1
scans into hrT2 modality and us-ing their annotations to achieve supervised
learning of hrT2 modality. Then, the VS and 7 adjacent brain structures related
to Koos classification in hrT2 scans were segmented. Finally, handcrafted
features are extracted from the segmenta-tion results, and Koos grade is
classified using a random forest classifier. The proposed method received rank
1 on the Koos classification task of the Cross-Modality Domain Adaptation
(crossMoDA 2022) challenge, with Macro-Averaged Mean Absolute Error (MA-MAE) of
0.2148 for the validation set and 0.26 for the test set.
