Deep learning-enabled filter-free fluorescence microscope

doi:10.1126/sciadv.adq2494

2025-01-03

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DOI：10.1126/sciadv.adq2494

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摘要

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光学滤波是荧光显微镜中不可或缺的一部分，用于选择性突出用特定荧光团标记的分子并抑制背景噪声。然而，使用光学滤波组件会增加显微系统的复杂度、尺寸和成本，使其不太适用于多荧光通道、高速成像。在此，我们提出了一种基于深度学习的无滤波荧光显微成像方法，该方法采用数字光谱滤波技术。该技术允许在图像采集后自动选择荧光通道，并通过计算光谱漂移引起的颜色变化来准确预测荧光，同时考虑激发散射的影响。使用不同荧光团标记的细胞和组织在不同放大倍数下的荧光预测已得到验证。该技术能够以稳健的灵敏度和特异性准确识别标记，其结果与参考标准一致。除荧光显微镜外，基于深度学习的光谱滤波策略还有潜力推动其他生物医学应用的发展，包括细胞计数和内窥镜检查。

期刊

Science Advances

IF:

12.5

论文数:

2.0W

被引数:

18.1W

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