  Accurate automatic segmentation of medical images typically requires large
datasets with high-quality annotations, making it less applicable in clinical
settings due to limited training data. One-shot segmentation based on learned
transformations (OSSLT) has shown promise when labeled data is extremely
limited, typically including unsupervised deformable registration, data
augmentation with learned registration, and segmentation learned from augmented
data. However, current one-shot segmentation methods are challenged by limited
data diversity during augmentation, and potential label errors caused by
imperfect registration. To address these issues, we propose a novel one-shot
medical image segmentation method with adversarial training and label error
rectification (AdLER), with the aim of improving the diversity of generated
data and correcting label errors to enhance segmentation performance.
Specifically, we implement a novel dual consistency constraint to ensure
anatomy-aligned registration that lessens registration errors. Furthermore, we
develop an adversarial training strategy to augment the atlas image, which
ensures both generation diversity and segmentation robustness. We also propose
to rectify potential label errors in the augmented atlas images by estimating
segmentation uncertainty, which can compensate for the imperfect nature of
deformable registration and improve segmentation authenticity. Experiments on
the CANDI and ABIDE datasets demonstrate that the proposed AdLER outperforms
previous state-of-the-art methods by 0.7% (CANDI), 3.6% (ABIDE "seen"), and
4.9% (ABIDE "unseen") in segmentation based on Dice scores, respectively. The
source code will be available at https://github.com/hsiangyuzhao/AdLER.
