Cross-modal MRI segmentation is of great value for computer-aided medical diagnosis, enabling flexible data acquisition and model generalization. However, most existing methods have difficulty in handling local variations in domain shift and typically require a significant amount of data for training, which hinders their usage in practice. To address these problems, we propose a novel adaptive domain generalization framework, which integrates a learning-free cross-domain representation based on image gradient maps and a class prior-informed test-time adaptation strategy for mitigating local domain shift. We validate our approach on two multi-modal MRI datasets with six cross-modal segmentation tasks. Across all the task settings, our method consistently outperforms competing approaches and shows a stable performance even with limited training data.

翻译：跨模态MRI分割对计算机辅助医学诊断具有重要价值，能够实现灵活的数据采集和模型泛化。然而，现有方法大多难以处理域偏移中的局部变化，且通常需要大量数据进行训练，这限制了其在实际中的应用。为解决这些问题，我们提出了一种新颖的自适应域泛化框架，该框架整合了基于图像梯度图的无学习跨域表示和基于类别先验的测试时自适应策略，以缓解局部域偏移。我们在两个多模态MRI数据集上，针对六项跨模态分割任务进行了验证。在所有任务设置中，我们的方法均持续优于竞争方法，即便在训练数据有限的情况下也表现出稳定的性能。