脑医学图像分割是医学图像处理中的一项关键任务，在中风、阿尔茨海默病、脑肿瘤等疾病的预测和诊断中发挥着重要作用。然而，由于不同扫描仪、成像协议和人群之间存在较大的站点间差异，不同来源的数据集之间存在巨大的分布差异。这导致实际应用中出现跨域问题。近年来，针对脑图像分割中的跨领域问题进行了大量研究。本综述遵循系统评价和荟萃分析首选报告项目（PRISMA）的标准进行数据处理和分析。我们从 2018 年 1 月至 2023 年 12 月从 PubMed、Web of Science 和 IEEE 数据库中检索了相关论文，从所选论文中提取了有关医学领域、成像模式、解决跨领域问题的方法、实验设计和数据集的信息。此外，我们还比较了中风病灶分割、白质分割和脑肿瘤分割等方法的性能。本综述共纳入并分析了 71 项研究。解决跨域问题的方法包括迁移学习、归一化、无监督学习、Transformer 模型和卷积神经网络 (CNN)。在 ATLAS 数据集上，与非自适应方法相比，领域自适应方法在中风病变分割任务中的总体改进约为 3%。然而，鉴于目前基于MICCAI 2017中白质分割任务方法和BraTS中脑肿瘤分割任务方法的研究数据集和实验方法的多样性，直观地比较这些方法的优缺点是具有挑战性的。人们已经应用各种技术来解决脑图像分割中的跨领域问题，但目前缺乏统一的数据集集合和实验标准。例如，许多研究仍然基于n重交叉验证，而直接基于跨站点或数据集交叉验证的方法相对较少。此外，由于脑分割领域的医学图像类型多样，因此无法直接对性能进行简单直观的比较。这些挑战需要在未来的研究中解决。版权所有 © 2024 Yanzhen, Song, Wanping, Zufang and Wang.

Brain medical image segmentation is a critical task in medical image processing, playing a significant role in the prediction and diagnosis of diseases such as stroke, Alzheimer's disease, and brain tumors. However, substantial distribution discrepancies among datasets from different sources arise due to the large inter-site discrepancy among different scanners, imaging protocols, and populations. This leads to cross-domain problems in practical applications. In recent years, numerous studies have been conducted to address the cross-domain problem in brain image segmentation.This review adheres to the standards of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) for data processing and analysis. We retrieved relevant papers from PubMed, Web of Science, and IEEE databases from January 2018 to December 2023, extracting information about the medical domain, imaging modalities, methods for addressing cross-domain issues, experimental designs, and datasets from the selected papers. Moreover, we compared the performance of methods in stroke lesion segmentation, white matter segmentation and brain tumor segmentation.A total of 71 studies were included and analyzed in this review. The methods for tackling the cross-domain problem include Transfer Learning, Normalization, Unsupervised Learning, Transformer models, and Convolutional Neural Networks (CNNs). On the ATLAS dataset, domain-adaptive methods showed an overall improvement of ~3 percent in stroke lesion segmentation tasks compared to non-adaptive methods. However, given the diversity of datasets and experimental methodologies in current studies based on the methods for white matter segmentation tasks in MICCAI 2017 and those for brain tumor segmentation tasks in BraTS, it is challenging to intuitively compare the strengths and weaknesses of these methods.Although various techniques have been applied to address the cross-domain problem in brain image segmentation, there is currently a lack of unified dataset collections and experimental standards. For instance, many studies are still based on n-fold cross-validation, while methods directly based on cross-validation across sites or datasets are relatively scarce. Furthermore, due to the diverse types of medical images in the field of brain segmentation, it is not straightforward to make simple and intuitive comparisons of performance. These challenges need to be addressed in future research.Copyright © 2024 Yanzhen, Song, Wanping, Zufang and Wang.