Center for Imaging Science, Johns Hopkins University, Baltimore, MD, USA.
Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
Sci Data. 2023 Aug 22;10(1):548. doi: 10.1038/s41597-023-02457-9.
To extract meaningful and reproducible models of brain function from stroke images, for both clinical and research proposes, is a daunting task severely hindered by the great variability of lesion frequency and patterns. Large datasets are therefore imperative, as well as fully automated image post-processing tools to analyze them. The development of such tools, particularly with artificial intelligence, is highly dependent on the availability of large datasets to model training and testing. We present a public dataset of 2,888 multimodal clinical MRIs of patients with acute and early subacute stroke, with manual lesion segmentation, and metadata. The dataset provides high quality, large scale, human-supervised knowledge to feed artificial intelligence models and enable further development of tools to automate several tasks that currently rely on human labor, such as lesion segmentation, labeling, calculation of disease-relevant scores, and lesion-based studies relating function to frequency lesion maps.
为了从中风图像中提取出具有临床和研究意义的、可重现的大脑功能模型，这是一项艰巨的任务，严重受到病变频率和模式极大变异性的阻碍。因此，大型数据集以及用于分析它们的全自动图像后处理工具是必不可少的。这些工具的开发，特别是人工智能，高度依赖于可用于模型训练和测试的大型数据集。我们提出了一个包含 2888 名急性和早期亚急性中风患者的多模态临床 MRI 的公共数据集，具有手动病变分割和元数据。该数据集提供了高质量、大规模、受人类监督的知识，以喂养人工智能模型，并能够进一步开发工具，使目前依赖人工劳动的任务实现自动化，例如病变分割、标记、疾病相关评分的计算，以及基于病变的功能与病变频率图谱相关的研究。
