■脑部医学图像分割是医学图像处理中的一项关键任务,在中风等疾病的预测和诊断中发挥着重要作用,老年痴呆症,和脑肿瘤。然而,由于不同扫描仪之间的站点间差异很大,因此不同来源的数据集之间的分布差异很大,成像协议,和人口。这导致实际应用中的跨域问题。近年来,已经进行了许多研究来解决大脑图像分割中的跨域问题。
■本评论遵循系统评论和荟萃分析(PRISMA)的首选报告项目的标准,用于数据处理和分析。我们从PubMed检索了相关论文,WebofScience,和IEEE数据库从2018年1月到2023年12月,提取有关医疗领域的信息,成像模式,解决跨域问题的方法,实验设计,和来自选定论文的数据集。此外,我们比较了中风病变分割方法的性能,脑白质分割和脑肿瘤分割。
■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,规范化,无监督学习,变压器型号,和卷积神经网络(CNN)。在ATLAS数据集上,领域自适应方法显示,与非自适应方法相比,卒中病变分割任务总体改善约3%.然而,鉴于当前研究中基于MICCAI2017中白质分割任务的方法和BraTS中脑肿瘤分割任务的方法的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。
■尽管已经应用了各种技术来解决大脑图像分割中的跨域问题,目前缺乏统一的数据集和实验标准。例如,许多研究仍然基于n折交叉验证,而直接基于跨站点或数据集的交叉验证的方法相对较少。此外,由于大脑分割领域的医学图像类型多种多样,对性能进行简单直观的比较并不容易。这些挑战需要在未来的研究中解决。
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.