Source-free test-time adaptation for medical image segmentation aims to enhance the adaptability of segmentation models to diverse and previously unseen test sets of the target domain, which contributes to the generalizability and robustness of medical image segmentation models without access to the source domain. Ensuring consistency between target edges and paired inputs is crucial for test-time adaptation. To improve the performance of test-time domain adaptation, we propose a multi task consistency guided source-free test-time domain adaptation medical image segmentation method which ensures the consistency of the local boundary predictions and the global prototype representation. Specifically, we introduce a local boundary consistency constraint method that explores the relationship between tissue region segmentation and tissue boundary localization tasks. Additionally, we propose a global feature consistency constraint toto enhance the intra-class compactness. We conduct extensive experiments on the segmentation of benchmark fundus images. Compared to prediction directly by the source domain model, the segmentation Dice score is improved by 6.27\% and 0.96\% in RIM-ONE-r3 and Drishti GS datasets, respectively. Additionally, the results of experiments demonstrate that our proposed method outperforms existing competitive domain adaptation segmentation algorithms.

翻译：无源测试时自适应医学图像分割旨在增强分割模型对目标领域中多样化且未见过的测试集的适应性，这有助于提升医学图像分割模型的泛化能力和鲁棒性，且无需访问源域。确保目标边缘与配对输入之间的一致性对于测试时自适应至关重要。为改进测试时领域自适应的性能，我们提出了一种多任务一致性引导的无源测试时领域自适应医学图像分割方法，该方法确保了局部边界预测与全局原型表示的一致性。具体而言，我们引入了一种局部边界一致性约束方法，探索组织区域分割与组织边界定位任务之间的关系。此外，我们提出了一种全局特征一致性约束，以增强类内紧凑性。我们在基准眼底图像分割上进行了大量实验。与直接使用源域模型进行预测相比，在RIM-ONE-r3和Drishti GS数据集上，分割Dice分数分别提升了6.27%和0.96%。此外，实验结果表明，我们提出的方法优于现有的竞争性领域自适应分割算法。