Studies on semi-supervised medical image segmentation (SSMIS) have seen fast progress recently. Due to the limited labelled data, SSMIS methods mainly focus on effectively leveraging unlabeled data to enhance the segmentation performance. However, despite their promising performance, current state-of-the-art methods often prioritize integrating complex techniques and loss terms rather than addressing the core challenges of semi-supervised scenarios directly. We argue that the key to SSMIS lies in generating substantial and appropriate prediction disagreement on unlabeled data. To this end, we emphasize the crutiality of data perturbation and model stabilization in semi-supervised segmentation, and propose a simple yet effective approach to boost SSMIS performance significantly, dubbed DPMS. Specifically, we first revisit SSMIS from three distinct perspectives: the data, the model, and the loss, and conduct a comprehensive study of corresponding strategies to examine their effectiveness. Based on these examinations, we then propose DPMS, which adopts a plain teacher-student framework with a standard supervised loss and unsupervised consistency loss. To produce appropriate prediction disagreements, DPMS perturbs the unlabeled data via strong augmentations to enlarge prediction disagreements considerably. On the other hand, using EMA teacher when strong augmentation is applied does not necessarily improve performance. DPMS further utilizes a forwarding-twice and momentum updating strategies for normalization statistics to stabilize the training on unlabeled data effectively. Despite its simplicity, DPMS can obtain new state-of-the-art performance on the public 2D ACDC and 3D LA datasets across various semi-supervised settings, e.g. obtaining a remarkable 22.62% improvement against previous SOTA on ACDC with 5% labels.

翻译：半监督医学图像分割（SSMIS）研究近期取得了快速进展。由于标注数据有限，SSMIS方法主要关注如何有效利用未标注数据来提升分割性能。然而，尽管当前最先进的方法表现优异，它们往往优先整合复杂技术和损失项，而非直接应对半监督场景中的核心挑战。我们认为半监督图像分割的关键在于生成未标注数据上充分且适当的预测分歧。为此，我们强调数据扰动与模型稳定化在半监督分割中的关键作用，并提出一种简单而有效的方法（名为DPMS）以显著提升SSMIS性能。具体而言，我们首先从数据、模型和损失三个不同视角重新审视SSMIS，并对相应策略的有效性进行全面研究。基于这些分析，我们提出DPMS，该框架采用标准教师-学生结构，结合常规监督损失和无监督一致性损失。为产生适当的预测分歧，DPMS通过强数据增强扰动未标注数据，大幅扩大预测分歧。另一方面，在应用强增强时，使用指数移动平均（EMA）教师并不总能提升性能。DPMS进一步采用前向两次传播和动量更新策略来调整归一化统计量，从而有效稳定未标注数据上的训练过程。尽管方法简洁，DPMS在公共2D ACDC和3D LA数据集上的多种半监督设定中均取得了新的最先进性能，例如在ACDC仅使用5%标注数据时，相较于先前最优方法实现了22.62%的显著提升。