The 2022 and 2023 crossMoDA challenge results show that training on heterogeneous multi-institutional data reduces segmentation outliers on homogeneous test sets, while cochlea Dice declined in 2023.
Unsupervised Domain Adaptation in Semantic Segmentation Based on Pixel Alignment and Self-Training
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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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crossMoDA Challenge: Evolution of Cross-Modality Domain Adaptation Techniques for Vestibular Schwannoma and Cochlea Segmentation from 2021 to 2023
The 2022 and 2023 crossMoDA challenge results show that training on heterogeneous multi-institutional data reduces segmentation outliers on homogeneous test sets, while cochlea Dice declined in 2023.