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

• URL: http://arxiv.org/abs/2110.01607v1
• Date: Sat, 2 Oct 2021 08:04:46 GMT
• Title: Using Out-of-the-Box Frameworks for Unpaired Image Translation and Image Segmentation for the crossMoDA Challenge
• Authors: Jae Won Choi
• Abstract summary: We use the CUT model for domain adaptation from contrast-enhanced T1 MR to high-resolution T2 MR. For the segmentation task, we use the nnU-Net framework.
• Score: 0.6396288020763143
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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