Both limited annotation and domain shift are significant challenges frequently encountered in medical image segmentation, leading to derivative scenarios like semi-supervised medical (SSMIS), semi-supervised medical domain generalization (Semi-MDG) and unsupervised medical domain adaptation (UMDA). Conventional methods are generally tailored to specific tasks in isolation, the error accumulation hinders the effective utilization of unlabeled data and limits further improvements, resulting in suboptimal performance when these issues occur. In this paper, we aim to develop a generic framework that masters all three tasks. We found that the key to solving the problem lies in how to generate reliable pseudo labels for the unlabeled data in the presence of domain shift with labeled data and increasing the diversity of the model. To tackle this issue, we employ a Diverse Teaching and Label Propagation Network (DTLP-Net) to boosting the Generic Semi-Supervised Medical Image Segmentation. Our DTLP-Net involves a single student model and two diverse teacher models, which can generate reliable pseudo-labels for the student model. The first teacher model decouple the training process with labeled and unlabeled data, The second teacher is momentum-updated periodically, thus generating reliable yet divers pseudo-labels. To fully utilize the information within the data, we adopt inter-sample and intra-sample data augmentation to learn the global and local knowledge. In addition, to further capture the voxel-level correlations, we propose label propagation to enhance the model robust. We evaluate our proposed framework on five benchmark datasets for SSMIS, UMDA, and Semi-MDG tasks. The results showcase notable improvements compared to state-of-the-art methods across all five settings, indicating the potential of our framework to tackle more challenging SSL scenarios.

翻译：标注数据有限与域偏移是医学图像分割中常见的两大挑战，由此衍生出半监督医学图像分割（SSMIS）、半监督医学域泛化（Semi-MDG）和无监督医学域适应（UMDA）等场景。传统方法通常针对单一任务独立设计，其误差累积问题阻碍了未标注数据的有效利用，限制了性能的进一步提升，导致在应对复合挑战时表现欠佳。本文旨在构建一个能同时处理以上三类任务的通用框架。我们发现，解决该问题的关键在于：如何在存在域偏移的情况下，利用已标注数据为未标注数据生成可靠的伪标签，并提升模型的多样性。为此，我们提出多样化教学与标签传播网络（DTLP-Net），以增强通用半监督医学图像分割的性能。DTLP-Net包含一个学生模型和两个多样化教师模型，可为学生模型生成可靠的伪标签。首个教师模型将标注数据与未标注数据的训练过程解耦；第二个教师模型通过周期性动量更新，生成可靠且多样化的伪标签。为充分挖掘数据信息，我们采用样本间与样本内数据增强策略，以学习全局与局部特征。此外，为更好地捕捉体素级关联性，我们提出标签传播机制以增强模型鲁棒性。我们在五个基准数据集上针对SSMIS、UMDA和Semi-MDG任务进行评估。实验结果表明，在所有五种设定下，我们的方法均较当前最优方法取得显著提升，证明了该框架应对更复杂半监督学习场景的潜力。