Domain shift is a commonly encountered issue in medical imaging solutions, primarily caused by variations in imaging devices and data sources. To mitigate this problem, unsupervised domain adaptation techniques have been employed. However, concerns regarding patient privacy and potential degradation of image quality have led to an increased focus on source-free domain adaptation. In this study, we address the issue of false labels in self-training based source-free domain adaptive medical image segmentation methods. To correct erroneous pseudo-labels, we propose a novel approach called the local-global pseudo-label correction (LGDA) method for source-free domain adaptive medical image segmentation. Our method consists of two components: An offline local context-based pseudo-label correction method that utilizes local context similarity in image space. And an online global pseudo-label correction method based on class prototypes, which corrects erroneously predicted pseudo-labels by considering the relative distance between pixel-wise feature vectors and prototype vectors. We evaluate the performance of our method on three benchmark fundus image datasets for optic disc and cup segmentation. Our method achieves superior performance compared to the state-of-the-art approaches, even without using of any source data.

翻译：域偏移是医学图像解决方案中常见的问题，主要由成像设备和数据源的差异引起。为缓解此问题，已采用无监督域自适应技术。然而，对患者隐私和图像质量潜在退化的担忧使得无源域自适应成为研究焦点。本研究针对基于自我训练的无源域自适应医学图像分割方法中的错误标签问题，提出了一种名为局部-全局伪标签校正（LGDA）的新方法，用于无源域自适应医学图像分割。该方法包含两个部分：基于离线局部上下文的伪标签校正方法，利用图像空间中的局部上下文相似性；以及基于类原型的在线全局伪标签校正方法，通过考虑像素级特征向量与原型向量之间的相对距离，修正错误预测的伪标签。我们在三个基准眼底图像数据集上对视盘和视杯分割任务进行了性能评估。即使未使用任何源数据，该方法仍取得了优于当前最先进方法的性能。