Unsupervised Domain Adaptation in Semantic Segmentation Based on Pixel Alignment and Self-Training
This work addresses domain shift in medical image segmentation, which is incremental as it builds on existing UDA methods.
The paper tackled unsupervised domain adaptation for semantic segmentation in medical imaging by aligning pixel distributions and using self-training, achieving a mean Dice score of 0.8395 and rank-2 on a validation leaderboard.
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.