Unsupervised Domain Adaptation in Semantic Segmentation Based on Pixel Alignment and Self-Training

• URL: http://arxiv.org/abs/2109.14219v1
• Date: Wed, 29 Sep 2021 06:56:57 GMT
• Title: Unsupervised Domain Adaptation in Semantic Segmentation Based on Pixel Alignment and Self-Training
• Authors: Hexin Dong, Fei Yu, Jie Zhao, Bin Dong and Li Zhang
• Abstract summary: Pixel alignment transfers ceT1 scans to hrT2 modality, helping to reduce domain shift in the training segmentation model. Self-training adapts the decision boundary of the segmentation network to fit the distribution of hrT2 scans.
• Score: 13.63879014979211
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper proposes an unsupervised cross-modality domain adaptation approach based on pixel alignment and self-training. Pixel alignment transfers ceT1 scans to hrT2 modality, helping to reduce domain shift in the training segmentation model. Self-training adapts the decision boundary of the segmentation network to fit the distribution of hrT2 scans. Experiment results show that PAST has outperformed the non-UDA baseline significantly, and it received rank-2 on CrossMoDA validation phase Leaderboard with a mean Dice score of 0.8395.

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