人工智能生成的注释数据集用于NCI中的多种癌症放射学收集图像数据公众号
Gowtham Krishnan Murugesan1, Diana McCrumb2, Mariam Aboian3
1BAMF Health, Grand Rapids, MI, USA. gowtham.murugesan@bamfhealth.com.
Scientific data
|October 23, 2024
概括
该项目为癌症研究收藏品生成了人工智能驱动的成像注释,显著增加了可用的细分数据. 这些增强的数据集支持开发先进的医学成像工具和算法.
科学领域:
- 医疗成像医学成像
- 放射学中的人工智能
- 癌症研究 信息学 信息学
背景情况:
- 国家癌症研究所 (NCI) 图像数据共享 (IDC) 提供了有价值的公共癌症放射学数据.
- 在IDC收藏中现有的细分注释很少,限制了先进的AI开发.
- 在用于癌症研究的注释医学成像数据集中存在重大差距.
研究的目的:
- 为了增加现有的国家癌症研究所 (NCI) 图像数据共享 (IDC) 收藏与高质量的细分注释.
- 增强公共癌症放射学数据集对人工智能驱动的研究的有用性.
- 为医疗成像分析创建一个全面的,人工智能生成的数据集.
主要方法:
- 利用人工智能 (AI) 在11个IDC收藏中生成组织,器官和癌症的细分注释.
- 包含多种成像方式,包括计算机断层扫描 (CT),磁共振成像 (MRI) 和正子发射断层扫描 (PET).
- 确保注释符合医学数字成像和通信 (DICOM) 标准,以实现无集成.
主要成果:
- 制作了11个不同的IDC图像集合的大量AI生成注释数据集.
- 覆盖了各种解剖区域,如胸部,乳房,脏,前列腺和肝脏.
- 一个AI注释的子集被放射科医生验证和纠正,以评估AI模型的性能.
结论:
- 人工智能生成的注释显著增加了公共癌症成像数据集中的细分数量和质量.
- 这些增强的符合DICOM的数据集可以作为第三方分析集合轻松集成到NCI IDC中.
- 公共可访问的模型,图像和注释将加速新型癌症成像工具和算法的开发.
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