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

• URL: http://arxiv.org/abs/2303.07674v1
• Date: Tue, 14 Mar 2023 07:25:38 GMT
• Title: Koos Classification of Vestibular Schwannoma via Image Translation-Based Unsupervised Cross-Modality Domain Adaptation
• Authors: Tao Yang and Lisheng Wang
• Abstract summary: We propose an unsupervised cross-modality domain adaptation method based on im-age translation. The proposed method received rank 1 on the Koos classification task of the Cross-Modality Domain Adaptation (crossMoDA 2022) challenge.
• Score: 5.81371357700742
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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