Abstract
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.
Abstract (translated)
源自由测试时间适应医疗图像分割旨在增强分割模型的适应性，使其能适应目标域中的多样化和之前未见过的测试集。这有助于实现没有访问源域的医疗图像分割模型的泛化能力和稳健性。确保目标边缘与成对输入之间的一致性对测试时间适应至关重要。为了提高测试时间域适应的性能，我们提出了一个多任务一致性引导的源自由测试时间域适应医疗图像分割方法，确保局部边界预测和全局原型表示的一致性。具体来说，我们引入了组织区域分割与组织边界定位任务之间的关系。此外，我们还提出了全局特征一致性约束以增强类内压缩性。我们对基准基金图像进行了广泛的实验。与直接由源域模型预测的分割结果相比，RIM-ONE-r3和Drishti GS数据集中的分割Dice分数分别提高了6.27%和0.96%。此外，实验结果表明，与现有的竞争域适应分割算法相比，我们提出的方法具有优异的性能。
URL
https://arxiv.org/abs/2310.11766
