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Using Out-of-the-Box Frameworks for Unpaired Image Translation and Image Segmentation for the crossMoDA Challenge

10/02/2021

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by Jae Won Choi, et al.

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The purpose of this study is to apply and evaluate out-of-the-box deep learning frameworks for the crossMoDA challenge. We use the CUT model for domain adaptation from contrast-enhanced T1 MR to high-resolution T2 MR. As data augmentation, we generated additional images with vestibular schwannomas with lower signal intensity. For the segmentation task, we use the nnU-Net framework. Our final submission achieved a mean Dice score of 0.8299 (0.0465) in the validation phase.

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