Edinburgh Imaging Academy, University of Edinburgh, Edinburgh, UK; International Institute of Health Sciences, Sri Lanka.
Edinburgh Imaging Academy, University of Edinburgh, Edinburgh, UK; Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK.
Comput Med Imaging Graph. 2021 Mar;88:101867. doi: 10.1016/j.compmedimag.2021.101867. Epub 2021 Jan 13.
White matter hyperintensities (WMH), of presumed vascular origin, are visible and quantifiable neuroradiological markers of brain parenchymal change. These changes may range from damage secondary to inflammation and other neurological conditions, through to healthy ageing. Fully automatic WMH quantification methods are promising, but still, traditional semi-automatic methods seem to be preferred in clinical research. We systematically reviewed the literature for fully automatic methods developed in the last five years, to assess what are considered state-of-the-art techniques, as well as trends in the analysis of WMH of presumed vascular origin.
We registered the systematic review protocol with the International Prospective Register of Systematic Reviews (PROSPERO), registration number - CRD42019132200. We conducted the search for fully automatic methods developed from 2015 to July 2020 on Medline, Science direct, IEE Explore, and Web of Science. We assessed risk of bias and applicability of the studies using QUADAS 2.
The search yielded 2327 papers after removing 104 duplicates. After screening titles, abstracts and full text, 37 were selected for detailed analysis. Of these, 16 proposed a supervised segmentation method, 10 proposed an unsupervised segmentation method, and 11 proposed a deep learning segmentation method. Average DSC values ranged from 0.538 to 0.91, being the highest value obtained from an unsupervised segmentation method. Only four studies validated their method in longitudinal samples, and eight performed an additional validation using clinical parameters. Only 8/37 studies made available their methods in public repositories.
We found no evidence that favours deep learning methods over the more established k-NN, linear regression and unsupervised methods in this task. Data and code availability, bias in study design and ground truth generation influence the wider validation and applicability of these methods in clinical research.
脑白质高信号（WMH），推测为血管源性，是脑实质改变的可见且可量化的神经影像学标志物。这些变化的范围可能从炎症和其他神经状况引起的损害，到健康衰老。全自动 WMH 定量方法很有前途，但在临床研究中，传统的半自动方法似乎仍然更受欢迎。我们系统地回顾了过去五年中开发的全自动方法的文献，以评估哪些方法被认为是最先进的技术，以及推测为血管源性的 WMH 分析的趋势。
我们在国际前瞻性系统评价注册库（PROSPERO）中注册了系统评价方案，注册号为 - CRD42019132200。我们在 Medline、Science direct、IEE Explore 和 Web of Science 上搜索了 2015 年至 2020 年 7 月开发的全自动方法。我们使用 QUADAS 2 评估了研究的偏倚风险和适用性。
去除 104 篇重复文献后，搜索得到 2327 篇文献。经过筛选标题、摘要和全文，选择了 37 篇进行详细分析。其中，16 篇提出了监督分割方法，10 篇提出了无监督分割方法，11 篇提出了深度学习分割方法。平均 DSC 值范围为 0.538 至 0.91，最高值来自无监督分割方法。只有 4 项研究在纵向样本中验证了他们的方法，8 项研究使用临床参数进行了额外的验证。只有 8/37 项研究在公共存储库中提供了他们的方法。
我们没有发现任何证据表明深度学习方法优于更成熟的 k-NN、线性回归和无监督方法。数据和代码可用性、研究设计偏倚和真实值生成会影响这些方法在临床研究中的更广泛验证和适用性。
