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用BIDS标准化CT数据:在肺部和大脑成像中的应用

Giulia Raffaella De Luca, Hugo Boniface, Stefano Diciotti

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|December 3, 2025

概括

这项研究将脑成像数据结构 (BIDS) 扩展到计算机断层扫描 (CT) 数据,使得医学AI的标准化数据成为可能. 这有助于人工智能模型验证,并提高诊断准确度.

科学领域:

医学成像信息学医疗成像信息学
放射学放射学是一门学科.
人工智能在医学中的应用

背景情况:

计算机断层扫描 (CT) 图像越来越多地用于神经放射学和胸部成像等领域.
对CT数据的标准化对于在医学中开发和验证人工智能 (AI) 工具至关重要.
现有的数据结构,如脑成像数据结构 (BIDS),对于神经成像至关重要,但需要扩展到CT.

研究的目的:

为了展示大脑成像数据结构 (BIDS) 的扩展,以容纳计算机断层扫描 (CT) 数据.
通过使用标准化CT数据集,促进用于医疗应用的AI工具的开发和验证.
通过使CT数据可互操作,可访问和可重复使用,促进开放科学原则.

主要方法:

将现有的CT数据集 (OASIS-3和国家肺部查试验 - NLST) 转换为BIDS格式.
开发BIDS应用程序用于利用Sybil AI工具预测肺癌风险.
实施数据标准化和AI模型验证框架.

主要成果:

成功将OASIS-3和NLST数据集转换为BIDS格式.
用AI驱动的肺癌风险预测功能BIDS应用程序的演示.
建立一个标准化的框架,促进数据共享和AI模型验证.

结论:

将BIDS扩展到CT数据,为医学成像数据提供了一个标准化的格式.
该框架支持AI工具在临床环境中的开发,验证和部署.
增强的数据可访问性和可重复使用性加速了人工智能研究,提高了诊断准确性,并有助于临床决策.

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