Deep learning (DL) has shown remarkable success in various medical imaging data analysis applications. However, it remains challenging for DL models to achieve good generalization, especially when the training and testing datasets are collected at sites with different scanners, due to domain shift caused by differences in data distributions. Domain adaptation has emerged as an effective means to address this challenge by mitigating domain gaps in medical imaging applications. In this review, we specifically focus on domain adaptation approaches for DL-based medical image segmentation. We first present the motivation and background knowledge underlying domain adaptations, then provide a comprehensive review of domain adaptation applications in medical image segmentations, and finally discuss the challenges, limitations, and future research trends in the field to promote the methodology development of domain adaptation in the context of medical image segmentation. Our goal was to provide researchers with up-to-date references on the applications of domain adaptation in medical image segmentation studies.

翻译：深度学习在各种医学影像数据分析应用中已展现出显著成功。然而，由于数据分布差异导致的域偏移，深度学习模型难以实现良好的泛化性能，尤其当训练集和测试集来自不同扫描仪采集的站点时。域适应通过缓解医学影像应用中的域差异，已成为应对这一挑战的有效手段。本综述聚焦于基于深度学习的医学图像分割域适应方法。我们首先阐述了域适应的动机和背景知识，继而全面回顾了域适应在医学图像分割中的应用，最后探讨了该领域的挑战、局限及未来研究趋势，以期推动域适应方法在医学图像分割中的方法论发展。本研究旨在为研究者提供域适应在医学图像分割研究中应用的最新参考文献。