Applying pre-trained medical segmentation models on out-of-domain images often yields predictions of insufficient quality. Several strategies have been proposed to maintain model performance, such as finetuning or unsupervised- and source-free domain adaptation. These strategies set restrictive requirements for data availability. In this study, we propose to combine domain generalization and test-time adaptation to create a highly effective approach for reusing pre-trained models in unseen target domains. Domain-generalized pre-training on source data is used to obtain the best initial performance in the target domain. We introduce the MIND descriptor previously used in image registration tasks as a further technique to achieve generalization and present superior performance for small-scale datasets compared to existing approaches. At test-time, high-quality segmentation for every single unseen scan is ensured by optimizing the model weights for consistency given different image augmentations. That way, our method enables separate use of source and target data and thus removes current data availability barriers. Moreover, the presented method is highly modular as it does not require specific model architectures or prior knowledge of involved domains and labels. We demonstrate this by integrating it into the nnUNet, which is currently the most popular and accurate framework for medical image segmentation. We employ multiple datasets covering abdominal, cardiac, and lumbar spine scans and compose several out-of-domain scenarios in this study. We demonstrate that our method, combined with pre-trained whole-body CT models, can effectively segment MR images with high accuracy in all of the aforementioned scenarios. Open-source code can be found here: https://github.com/multimodallearning/DG-TTA

翻译：针对预训练医学分割模型应用于域外图像时预测质量不足的问题，现有策略如微调、无监督域自适应及无源域自适应均对数据可用性设置了严格限制。本研究提出结合域泛化与测试时自适应，构建高效方法以复用预训练模型于未见目标域。通过源数据域泛化预训练获得目标域初始最佳性能，引入图像配准中常用的MIND描述符作为泛化增强技术，在小规模数据集上展现出优于现有方法的性能。测试阶段，通过优化模型权重对不同图像增强保持一致性，确保每个未见扫描图像的高质量分割。该方法实现源域与目标域数据独立使用，消除了当前数据可用性壁垒。其高度模块化特性无需特定模型架构或域/标签先验知识，可通过集成至当前最广泛使用的医学图像分割框架nnUNet实现。本研究采用涵盖腹部、心脏及腰椎扫描的多数据集，构建多种域外场景验证方法有效性。实验表明，将本方法与预训练全身CT模型结合，可在所有上述场景中实现MR图像的高精度分割。开源代码见：https://github.com/multimodallearning/DG-TTA