质量引导的半监督学习用于医学图像分割

摘要

训练精确的医学图像分割模型需要大量密集标注数据，获取这类数据成本高昂且耗时。半监督学习通过同时利用大量未标注数据和少量标注数据进行学习，有效缓解了这一问题。然而，现有大多数半监督方法依赖伪标签处理未标注数据，并通常通过模型置信度或不确定性评估其可靠性——这些评估方式具有自我参照性，缺乏对分割质量的明确判定依据。为此，我们提出了一种质量引导的半监督学习框架，该框架训练专用网络从图像-掩膜对中估计分割质量。质量预测器基于通过合成数据损坏生成的变质量掩膜（结合部分训练分割模型产生的不完美输出）进行训练，能够捕获训练过程中出现的真实错误模式。我们通过两种互补机制将质量预测器融入半监督学习：质量感知正则化损失与基于质量的伪标签样本重加权方案。实验表明，该方法可作为即插即用式增强模块嵌入现有半监督框架。在五个数据集和多种架构上的广泛实验证明，该方法相较于现有半监督方法具有持续优势，推动了半监督医学图像分割领域的最新技术水平发展。

English

Training accurate medical image segmentation models requires large amounts of densely annotated data, which is costly and time-consuming to obtain. Semi-supervised learning (SSL) alleviates this by learning from both abundant unlabeled data and limited labeled data. However, most modern SSL methods rely on pseudolabels for unlabeled data, and typically assess their reliability through model confidence or uncertainty, measures that are self-referential and lack explicit grounding in segmentation quality. Instead, we propose a quality-guided SSL framework that trains a dedicated network to estimate segmentation quality from image-mask pairs. The predictor is trained on variable-quality masks generated through synthetic corruptions augmented with imperfect outputs from partially trained segmentation models, capturing realistic error patterns encountered during training. We integrate the quality predictor into SSL through two complementary mechanisms: a quality-aware regularization loss and a quality-based pseudolabel sample reweighting scheme. We show that our method serves as a drop-in enhancement to existing SSL frameworks. Extensive experiments across five datasets and multiple architectures demonstrate consistent improvements over competing SSL methods, advancing the state-of-the-art in semi-supervised medical image segmentation.