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The Koos grading scale is a classification system for vestibular schwannoma(VS) used to characterize the tumor and its effects on adjacent brainstructures. The Koos classification captures many of the characteristics oftreatment deci-sions and is often used to determine treatment plans. Althoughboth contrast-enhanced T1 (ceT1) scanning and high-resolution T2 (hrT2)scanning can be used for Koos Classification, hrT2 scanning is gaining interestbecause of its higher safety and cost-effectiveness. However, in the absence ofannotations for hrT2 scans, deep learning methods often inevitably suffer fromperformance deg-radation due to unsupervised learning. If ceT1 scans and theirannotations can be used for unsupervised learning of hrT2 scans, theperformance of Koos classifi-cation using unlabeled hrT2 scans will be greatlyimproved. In this regard, we propose an unsupervised cross-modality domainadaptation method based on im-age translation by transforming annotated ceT1scans into hrT2 modality and us-ing their annotations to achieve supervisedlearning of hrT2 modality. Then, the VS and 7 adjacent brain structures relatedto Koos classification in hrT2 scans were segmented. Finally, handcraftedfeatures are extracted from the segmenta-tion results, and Koos grade isclassified using a random forest classifier. The proposed method received rank1 on the Koos classification task of the Cross-Modality Domain Adaptation(crossMoDA 2022) challenge, with Macro-Averaged Mean Absolute Error (MA-MAE) of0.2148 for the validation set and 0.26 for the test set.

Koos Classification of Vestibular Schwannoma via Image Translation-Based Unsupervised Cross-Modality Domain Adaptation