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In this work, we propose a multi-view image translation framework, which cantranslate contrast-enhanced T1 (ceT1) MR imaging to high-resolution T2 (hrT2)MR imaging for unsupervised vestibular schwannoma and cochlea segmentation. Weadopt two image translation models in parallel that use a pixel-levelconsistent constraint and a patch-level contrastive constraint, respectively.Thereby, we can augment pseudo-hrT2 images reflecting different perspectives,which eventually lead to a high-performing segmentation model. Our experimentalresults on the CrossMoDA challenge show that the proposed method achievedenhanced performance on the vestibular schwannoma and cochlea segmentation.

Multi-view Cross-Modality MR Image Translation for Vestibular Schwannoma and Cochlea Segmentation