Deep learning-based models in medical imaging often struggle to generalize effectively to new scans due to data heterogeneity arising from differences in hardware, acquisition parameters, population, and artifacts. This limitation presents a significant challenge in adopting machine learning models for clinical practice. We propose an unsupervised method for robust domain adaptation in brain MRI segmentation by leveraging MRI-specific augmentation techniques. To evaluate the effectiveness of our method, we conduct extensive experiments across diverse datasets, modalities, and segmentation tasks, comparing against the state-of-the-art methods. The results show that our proposed approach achieves high accuracy, exhibits broad applicability, and showcases remarkable robustness against domain shift in various tasks, surpassing the state-of-the-art performance in the majority of cases.

翻译：基于深度学习的医学影像模型常因硬件、采集参数、人群差异及伪影等导致的数据异质性，难以有效泛化至新扫描数据。这一局限为机器学习模型在临床实践中的应用带来了重大挑战。我们提出一种无监督方法，通过利用MRI特异性增强技术，实现脑MRI分割中稳健的域适应。为评估该方法有效性，我们在多样化的数据集、模态及分割任务中开展广泛实验，并与现有最优方法进行对比。结果表明，所提方法在多数任务中达到高精度，展现出广泛适用性及对域偏移的显著鲁棒性，其性能超越了当前最优方法。