Consistency learning with feature perturbation is a widely used strategy in semi-supervised medical image segmentation. However, many existing perturbation methods rely on dropout, and thus require a careful manual tuning of the dropout rate, which is a sensitive hyperparameter and often difficult to optimize and may lead to suboptimal regularization. To overcome this limitation, we propose VQ-Seg, the first approach to employ vector quantization (VQ) to discretize the feature space and introduce a novel and controllable Quantized Perturbation Module (QPM) that replaces dropout. Our QPM perturbs discrete representations by shuffling the spatial locations of codebook indices, enabling effective and controllable regularization. To mitigate potential information loss caused by quantization, we design a dual-branch architecture where the post-quantization feature space is shared by both image reconstruction and segmentation tasks. Moreover, we introduce a Post-VQ Feature Adapter (PFA) to incorporate guidance from a foundation model (FM), supplementing the high-level semantic information lost during quantization. Furthermore, we collect a large-scale Lung Cancer (LC) dataset comprising 828 CT scans annotated for central-type lung carcinoma. Extensive experiments on the LC dataset and other public benchmarks demonstrate the effectiveness of our method, which outperforms state-of-the-art approaches. Code available at: https://github.com/script-Yang/VQ-Seg.

翻译：基于特征扰动的一致性学习是半监督医学图像分割中广泛采用的策略。然而，现有许多扰动方法依赖于随机失活（dropout），因此需要仔细手动调整失活率，这是一个敏感的超参数，通常难以优化，并可能导致次优的正则化效果。为克服这一局限，我们提出了VQ-Seg，这是首个利用向量量化（VQ）离散化特征空间，并引入一种新颖且可控的量化扰动模块（QPM）以替代随机失活的方法。我们的QPM通过打乱码本索引的空间位置来扰动离散表示，从而实现有效且可控的正则化。为减轻量化可能导致的信息损失，我们设计了一种双分支架构，其中量化后的特征空间由图像重建和分割任务共享。此外，我们引入了后量化特征适配器（PFA），以整合来自基础模型（FM）的指导，补充量化过程中丢失的高层语义信息。进一步地，我们收集了一个大规模肺癌（LC）数据集，包含828例针对中央型肺癌标注的CT扫描。在LC数据集及其他公开基准上的大量实验证明了我们方法的有效性，其性能优于现有最先进方法。代码发布于：https://github.com/script-Yang/VQ-Seg。