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The purpose of this study is to apply and evaluate out-of-the-box deeplearning frameworks for the crossMoDA challenge. We use the CUT model, a modelfor unpaired image-to-image translation based on patchwise contrastive learningand adversarial learning, for domain adaptation from contrast-enhanced T1 MR tohigh-resolution T2 MR. As data augmentation, we generate additional images withvestibular schwannomas with lower signal intensity. For the segmentation task,we use the nnU-Net framework. Our final submission achieved mean Dice scores of0.8299 in the validation phase and 0.8253 in the test phase. Our method ranked3rd in the crossMoDA challenge.

Using Out-of-the-Box Frameworks for Contrastive Unpaired Image  Translation for Vestibular Schwannoma and Cochlea Segmentation: An approach  for the crossMoDA Challenge