芯片上无标签的细胞分类直接基于离轴全息图和空间频率不变的深度学习.
Matan Dudaie1, Itay Barnea1, Noga Nissim1
1Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, 69978, Tel Aviv, Israel.
Scientific reports
|July 31, 2023
概括
我们开发了一种快速,无标签的方法,使用原始数字全息图来对细胞进行分类. 这种新方法通过直接分析全息图像,绕过漫长的重建步骤来加速成像流细胞计.
科学领域:
- 生物光子学 生物光子学
- 计算生物学 计算生物学
- 细胞成像 细胞成像
背景情况:
- 图像流细胞计对于细胞分析至关重要.
- 目前的方法通常需要耗时的细胞形状重建来进行分类.
- 没有标签的技术是可取的,以保持细胞活力和简化工作流程.
研究的目的:
- 开发一种使用原始数字全息图的快速,无标签的细胞分类方法.
- 为了绕过全息成像流细胞计中的计算密集的相位形状重建步骤.
- 在高通量查中提高细胞分类的速度和稳定性.
主要方法:
- 获取流中的细胞的原始离轴数字全息图.
- 在原始全息图像上训练的卷积神经网络 (CNN) 的开发.
- CNN的设计是不变的空间频率和边缘方向在全息图.
- 使用四种不同的癌细胞类型进行验证.
主要成果:
- 从原始全息图直接证明了单个细胞的成功无标签分类.
- 在不需要进行定量阶段形状重建的情况下实现了分类.
- 展示了CNN处理全息边缘图案变化的能力.
- 在多个癌症细胞系上验证了该方法.
结论:
- 这种方法在成像流细胞计中显著加快了无标签细胞分类.
- 对原始数字全息图的直接分析提供了比传统的基于重建的方法更快,更强大的替代方案.
- 这种方法对各种生物和临床应用中的实时,高通量细胞分析具有前景.
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