一个大型的公开数据集,包含注释的临床MRI和急性中风患者的元数据
Chin-Fu Liu1,2, Richard Leigh3, Brenda Johnson3
1Center for Imaging Science, Johns Hopkins University, Baltimore, MD, USA.
Scientific data
|August 22, 2023
概括
一个新的公共数据集,包括2888个中风MRI和病变细分,有助于AI的发展. 这种资源有助于从中风图像中对大脑功能模型进行自动分析.
科学领域:
- 神经成像是一种神经成像.
- 人工智能在医学中的应用
- 脑卒中研究 脑卒中研究
背景情况:
- 分析中风诱导的大脑功能变化是复杂的,因为病变的变化.
- 自动化图像分析工具对于大规模的中风研究至关重要.
- 开发用于中风成像的AI需要广泛的,高质量的数据集.
研究的目的:
- 引入来自急性和亚急性中风患者的多模式MRI的大型公共数据集.
- 为人工智能模型培训提供由人类监督的病变细分和元数据.
- 促进用于中风图像分析的自动化工具的开发.
主要方法:
- 编制了2888个多模式临床MRI的数据集.
- 包括每个MRI的手动病变细分.
- 提供相关的患者元数据.
主要成果:
- 为中风研究建立了一个大规模,高质量的数据集.
- 数据集使人工智能模型可以用于诸如损伤细分和评分等任务的训练.
- 促进基于病变的研究,将功能与病变模式相关联.
结论:
- 公共数据集支持在中风成像分析中使用人工智能驱动的自动化.
- 加快研究了解中风后的大脑功能.
- 能够实现更可复制和有意义的中风冲击模型.
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