Fluorescence reconstruction microscopy (FRM) describes a class of techniques where transmitted light images are passed into a convolutional neural network which then outputs predicted epifluorescence images. This approach enables many benefits including reduced phototoxicity, freeing up of fluorescence channels, simplified sample preparation, and the ability to re-process legacy data for new insights. However, current FRM benchmarks are abstractions that are difficult to relate to how valuable or trustworthy an FRM prediction is. Here, we relate the conventional benchmarks and demonstrations to practical and familiar cell biology analyses to demonstrate that FRM should be judged in context. We further demonstrate that it performs remarkably well even with lower-magnification microscopy data, as are often collected in high content imaging. Specifically, we present promising results for nuclei, cell-cell junctions, and fine feature reconstruction; provide data-driven experimental design guidelines; and provide the code, sample data, and user manual to enable more widespread adoption of FRM.

中文翻译：

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实用的荧光重建显微镜用于大样品和低放大倍率成像

荧光重建显微镜（FRM）描述了一类技术，其中将透射光图像传递到卷积神经网络，然后输出预测的落射荧光图像。这种方法可以带来许多好处，包括降低光毒性，释放荧光通道，简化样品制备以及重新处理旧数据以获取新见解的能力。但是，当前的FRM基准是很难与FRM预测的价值或可信度相关的抽象。在这里，我们将常规基准和演示与实际和熟悉的细胞生物学分析相关联，以表明应根据具体情况来判断FRM。我们进一步证明，即使使用低倍率显微镜数据（通常在高内涵成像中经常收集），它的性能也非常好。具体来说，我们为细胞核，细胞间连接和精细特征重建提供了有希望的结果；提供数据驱动的实验设计指南；并提供代码，示例数据和用户手册，以使FRM得到更广泛的采用。