Consistency learning is a central strategy to tackle unlabeled data in semi-supervised medical image segmentation (SSMIS), which enforces the model to produce consistent predictions under the perturbation. However, most current approaches solely focus on utilizing a specific single perturbation, which can only cope with limited cases, while employing multiple perturbations simultaneously is hard to guarantee the quality of consistency learning. In this paper, we propose an Adaptive Bidirectional Displacement (ABD) approach to solve the above challenge. Specifically, we first design a bidirectional patch displacement based on reliable prediction confidence for unlabeled data to generate new samples, which can effectively suppress uncontrollable regions and still retain the influence of input perturbations. Meanwhile, to enforce the model to learn the potentially uncontrollable content, a bidirectional displacement operation with inverse confidence is proposed for the labeled images, which generates samples with more unreliable information to facilitate model learning. Extensive experiments show that ABD achieves new state-of-the-art performances for SSMIS, significantly improving different baselines. Source code is available at https://github.com/chy-upc/ABD.

翻译：一致性学习是处理半监督医学图像分割（SSMIS）中未标注数据的关键策略，它强制模型在扰动下生成一致的预测结果。然而，当前大多数方法仅专注于利用特定的单一扰动，这只能应对有限情况，而同时使用多种扰动又难以保证一致性学习的质量。本文提出了一种自适应双向位移（ABD）方法来解决上述挑战。具体而言，我们首先基于未标注数据的可靠预测置信度设计了一种双向块位移操作以生成新样本，该方法能有效抑制不可控区域并保留输入扰动的影响。同时，为使模型学习潜在不可控内容，针对已标注图像提出了一种具有逆置信度的双向位移操作，生成包含更多不可靠信息的样本以促进模型学习。大量实验表明，ABD在SSMIS任务上取得了新的最优性能，显著提升了不同基线模型的效果。源代码已开源至https://github.com/chy-upc/ABD。