Medical image segmentation methods often rely on fully supervised approaches to achieve excellent performance, which is contingent upon having an extensive set of labeled images for training. However, annotating medical images is both expensive and time-consuming. Semi-supervised learning offers a solution by leveraging numerous unlabeled images alongside a limited set of annotated ones. In this paper, we introduce a semi-supervised medical image segmentation method based on the mean-teacher model, referred to as Dual-Decoder Consistency via Pseudo-Labels Guided Data Augmentation (DCPA). This method combines consistency regularization, pseudo-labels, and data augmentation to enhance the efficacy of semi-supervised segmentation. Firstly, the proposed model comprises both student and teacher models with a shared encoder and two distinct decoders employing different up-sampling strategies. Minimizing the output discrepancy between decoders enforces the generation of consistent representations, serving as regularization during student model training. Secondly, we introduce mixup operations to blend unlabeled data with labeled data, creating mixed data and thereby achieving data augmentation. Lastly, pseudo-labels are generated by the teacher model and utilized as labels for mixed data to compute unsupervised loss. We compare the segmentation results of the DCPA model with six state-of-the-art semi-supervised methods on three publicly available medical datasets. Beyond classical 10\% and 20\% semi-supervised settings, we investigate performance with less supervision (5\% labeled data). Experimental outcomes demonstrate that our approach consistently outperforms existing semi-supervised medical image segmentation methods across the three semi-supervised settings.

翻译：医学图像分割方法通常依赖全监督方法获得优异性能，这需要大量标注图像用于训练。然而，医学图像标注既昂贵又耗时。半监督学习通过利用大量未标注图像和少量标注图像提供解决方案。本文提出一种基于平均教师模型的半监督医学图像分割方法，称为基于伪标签引导数据增强的双解码器一致性（DCPA）。该方法融合一致性正则化、伪标签和数据增强来提升半监督分割效能。首先，所提模型包含学生模型和教师模型，两者共享编码器，并采用两个采用不同上采样策略的独立解码器。通过最小化解码器之间的输出差异，强制生成一致性表示，在学生模型训练中起到正则化作用。其次，我们引入混合操作将未标注数据与标注数据混合以生成混合数据，从而实现数据增强。最后，由教师模型生成伪标签，并将其作为混合数据的标签来计算无监督损失。我们在三个公开医学数据集上将DCPA模型与六种最先进的半监督方法进行分割结果对比。除经典的10%和20%半监督设置外，我们还研究了弱监督（5%标注数据）下的性能表现。实验结果表明，在三种半监督设置下，我们的方法始终优于现有的半监督医学图像分割方法。