Semi-supervised medical image segmentation is an effective method for addressing scenarios with limited labeled data. Existing methods mainly rely on frameworks such as mean teacher and dual-stream consistency learning. These approaches often face issues like error accumulation and model structural complexity, while also neglecting the interaction between labeled and unlabeled data streams. To overcome these challenges, we propose a Bidirectional Channel-selective Semantic Interaction~(BCSI) framework for semi-supervised medical image segmentation. First, we propose a Semantic-Spatial Perturbation~(SSP) mechanism, which disturbs the data using two strong augmentation operations and leverages unsupervised learning with pseudo-labels from weak augmentations. Additionally, we employ consistency on the predictions from the two strong augmentations to further improve model stability and robustness. Second, to reduce noise during the interaction between labeled and unlabeled data, we propose a Channel-selective Router~(CR) component, which dynamically selects the most relevant channels for information exchange. This mechanism ensures that only highly relevant features are activated, minimizing unnecessary interference. Finally, the Bidirectional Channel-wise Interaction~(BCI) strategy is employed to supplement additional semantic information and enhance the representation of important channels. Experimental results on multiple benchmarking 3D medical datasets demonstrate that the proposed method outperforms existing semi-supervised approaches.

翻译：半监督医学图像分割是解决标注数据有限场景的有效方法。现有方法主要依赖于均值教师和双流一致性学习等框架。这些方法通常面临误差累积和模型结构复杂性问题，同时忽略了标注与未标注数据流之间的交互作用。为克服这些挑战，我们提出了一种用于半监督医学图像分割的双向通道选择性语义交互（BCSI）框架。首先，我们提出语义-空间扰动（SSP）机制，该机制通过两种强增强操作扰动数据，并利用弱增强生成的伪标签进行无监督学习。此外，我们通过对两种强增强预测结果施加一致性约束，进一步提升模型的稳定性和鲁棒性。其次，为减少标注与未标注数据交互过程中的噪声，我们提出通道选择性路由（CR）组件，该组件动态选择最相关的通道进行信息交换。该机制确保仅激活高度相关的特征，最大限度地减少不必要的干扰。最后，采用双向通道交互（BCI）策略来补充额外的语义信息，并增强重要通道的表征能力。在多个基准3D医学数据集上的实验结果表明，所提方法优于现有的半监督方法。