Avdelningen för Radiofysik, Goteborgs Universitet Institutionen for Kliniska Vetenskaper, Göteborg, Sweden.
Sektionen för Klinisk Neurovetenskap, Goteborgs Universitet Institutionen for Neurovetenskap och Fysiologi, Göteborg, Sweden.
BMJ Open. 2019 Feb 13;9(2):e024824. doi: 10.1136/bmjopen-2018-024824.
Automatic brain lesion segmentation from medical images has great potential to support clinical decision making. Although numerous methods have been proposed, significant challenges must be addressed before they will become established in clinical and research practice. We aim to elucidate the state of the art, to provide a synopsis of competing approaches and identify contrasts between them.
We present the background and study design of a scoping review for automatic brain lesion segmentation methods for conventional MRI according to the framework proposed by Arksey and O'Malley. We aim to identify common image processing steps as well as mathematical and computational theories implemented in these methods. We will aggregate the evidence on the efficacy and identify limitations of the approaches. Methods to be investigated work with standard MRI sequences from human patients examined for brain lesions, and are validated with quantitative measures against a trusted reference. PubMed, IEEE Xplore and Scopus will be searched using search phrases that will ensure an inclusive and unbiased overview. For matching records, titles and abstracts will be screened to ensure eligibility. Studies will be excluded if a full paper is not available or is not written in English, if non-standard MR sequences are used, if there is no quantitative validation, or if the method is not automatic. In the data charting phase, we will extract information about authors, publication details and study cohort. We expect to find information about preprocessing, segmentation and validation procedures. We will develop an analytical framework to collate, summarise and synthesise the data.
Ethical approval for this study is not required since the information will be extracted from published studies. We will submit the review report to a peer-reviewed scientific journal and explore other venues for presenting the work.
从医学图像中自动分割脑损伤具有极大的潜力,可以辅助临床决策。尽管已经提出了许多方法,但在它们成为临床和研究实践的标准之前,还必须解决一些重大挑战。我们旨在阐明现状,概述竞争方法,并确定它们之间的差异。
我们根据 Arksey 和 O'Malley 提出的框架,展示了一项针对常规 MRI 自动脑损伤分割方法的范围综述的背景和研究设计。我们旨在确定这些方法中常用的图像处理步骤以及所采用的数学和计算理论。我们将汇总这些方法的疗效证据,并确定其局限性。所研究的方法适用于针对脑损伤进行检查的人类患者的标准 MRI 序列,并使用定量指标对其进行验证,以确保与可靠的参考标准相匹配。我们将使用包含所有相关研究的搜索词在 PubMed、IEEE Xplore 和 Scopus 中进行搜索。对于匹配的记录,将筛选标题和摘要以确保符合纳入标准。如果无法获得完整的论文或论文不是用英文撰写的、如果使用非标准的 MR 序列、如果没有进行定量验证、或者方法不是自动的,则将排除这些研究。在数据图表制作阶段,我们将提取有关作者、出版物细节和研究队列的信息。我们预计会找到有关预处理、分割和验证程序的信息。我们将开发一个分析框架来整理、总结和综合数据。
由于从已发表的研究中提取信息,因此本研究不需要伦理批准。我们将把综述报告提交给同行评议的科学期刊,并探索其他展示工作的途径。