无标签全息成像流细胞计与基于深度学习的检测和分类每秒数千个细胞
Dana Yagoda-Aharoni1, Eden Dotan1, Matan Dudaie1
1School of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, Tel Aviv, Israel.
概括
我们使用数字全息和神经网络开发了一种快速,无标签的细胞分析方法. 这种技术可以实时检测和分类细胞,显著推进诊断的成像流细胞计.
科学领域:
- 生物医学工程 生物医学工程
- 计算生物学 计算生物学
- 光学成像技术的成像
背景情况:
- 无标签成像流细胞计对于实时细胞分析至关重要.
- 目前的方法往往需要化学染色,或者在高通量应用中缺乏速度.
- 数字全息提供了详细的细胞结构信息,没有染色.
研究的目的:
- 为实时,无标签的细胞检测和分类引入一个新的端到端神经网络.
- 为了实现使用数字全息和定量相位成像的高速成像流细胞计.
- 为了证明该方法在不同细胞类型和条件的适应性.
主要方法:
- 开发了一个定制的两级神经网络,集成固定卷积层与图像处理过器进行检测.
- 利用两个随后的卷积层来分类检测到的细胞.
- 采用基于数字全息图的无标签定量成像流动细胞计,用于在流动过程中对细胞进行成像.
主要成果:
- 在0.44毫秒内实现细胞检测和分类,从而实现实时分析.
- 与YOLOv8n.等现有方法相比,已经证明了超过10倍的速度.
- 在T细胞和癌细胞上成功验证了该方法,显示了适应性.
- 能够每秒钟成像,检测和分类数千个细胞.
结论:
- 拟议的神经网络方法促进了高通量,无标签的成像流细胞计.
- 这种方法为实时细胞分析提供了显著的加速,减少了计算复杂性.
- 潜在的应用包括实时细胞监测,早期疾病检测和高速诊断.
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