Fluorescence microscopy, which visualizes cellular components with fluorescent stains, is an invaluable method in image cytometry. From these images various cellular features can be extracted. Together these features form phenotypes that can be used to determine effective drug therapies, such as those based on nanomedicines. Unfortunately, fluorescence microscopy is time-consuming, expensive, labour intensive, and toxic to the cells. Bright-field images lack these downsides but also lack the clear contrast of the cellular components and hence are difficult to use for downstream analysis. Generating the fluorescence images directly from bright-field images would get the best of both worlds, but can be very challenging to do for poorly visible cellular structures in the bright-field images. To tackle this problem deep learning models were explored to learn the mapping between bright-field and fluorescence images to enable virtual staining for adipocyte cell images. The models were tailored for each imaging channel, paying particular attention to the various challenges in each case, and those with the highest fidelity in extracted cell-level features were selected. The solutions included utilizing privileged information for the nuclear channel, and using image gradient information and adversarial training for the lipids channel. The former resulted in better morphological and count features and the latter resulted in more faithfully captured defects in the lipids, which are key features required for downstream analysis of these channels.

中文翻译：

---

学会看颜色：从明场图像生成生物学相关的荧光标记

可视化荧光染色的荧光显微镜是图像细胞术中不可估量的方法。从这些图像可以提取出各种细胞特征。这些特征共同形成了可用于确定有效药物治疗的表型，例如基于纳米药物的那些。不幸的是，荧光显微镜检查费时，昂贵，劳动强度大且对细胞有毒。明场图像没有这些缺点，但也缺乏细胞成分的清晰对比，因此很难用于下游分析。直接从明场图像中生成荧光图像将获得两全其美，但是对于在明场图像中可见性较差的细胞结构而言，这可能是非常具有挑战性的。为了解决这个问题，探索了深度学习模型，以学习明场图像和荧光图像之间的映射，以实现脂肪细胞图像的虚拟染色。这些模型是为每个成像通道量身定制的，要特别注意每种情况下的各种挑战，并选择在提取的细胞水平特征中具有最高保真度的模型。解决方案包括为核通道利用特权信息，为脂质通道利用图像梯度信息和对抗训练。前者导致更好的形态和计数特征，而后者导致更忠实地捕获脂质中的缺陷，这是这些通道下游分析所需的关键特征。