乳房DCEDL:一个标准化的深度学习准备的乳房DCE-MRI数据集,包括2070名患者
Naomi Fridman1, Bubby Solway2, Tomer Fridman2
1Department of Industrial Engineering, Ariel University, Ariel, Israel. naominoe.fridman@msmail.ariel.ac.il.
Scientific data
|January 15, 2026
概括
一个名为BreastDCEDL的新数据集,为2070名乳腺癌患者提供了标准化的3D动态对比增强磁共振成像 (DCE-MRI) 数据. 本资源支持人工智能开发,用于预测治疗反应和推进瘤学研究.
科学领域:
- 在瘤学瘤学.
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 动态对比增强磁共振成像 (DCE-MRI) 对于评估乳腺癌治疗疗效至关重要.
- 目前的深度学习进步受到缺乏标准化的多中心成像数据集的阻碍.
研究的目的:
- 介绍BreastDCEDL,这是一个新的,大规模的,深度学习准备的数据集,用于预治疗3D DCE-MRI扫描.
- 促进先进的人工智能模型的开发,用于预测和分析乳腺癌治疗反应.
主要方法:
- 汇编了来自三组 (I-SPY1,I-SPY2,杜克) 的2,070名患者的3D DCE-MRI扫描.
- 将DICOM文件转换为标准的3D NIfTI格式,确保数据完整性和分辨率.
- 综合统一的瘤注释 (面具/边界框) 和协调的临床元数据,包括pCR,HR和HER2状态.
主要成果:
- 乳腺DCEDL数据集包含2070名患者进行预治疗3D DCE-MRI扫描.
- 包括全面的注释和临床数据,可用于1,452名患者的pCR.
- 具有预定义,平衡的火车验证测试分割功能,以确保可靠的模型评估.
结论:
- 乳腺DCEDL解决了乳腺癌研究公共成像资源的重大差距.
- 数据集和相关工具将有助于在瘤学中开发AI驱动的预测模型.
- 促进社区驱动的AI研究,以改善乳腺癌患者的护理和结果.
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