  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.
