Fluorescence microscopy allows for a detailed inspection of cells, cellular networks and anatomical landmarks by staining with a variety of carefully selected markers visualized as colour channels. Quantitative characterization of structures in acquired images often relies on automatic image analysis methods. Despite the success of deep learning methods in other vision applications, their potential for fluorescence image analysis remains underexploited. One reason lies in the considerable workload required to train accurate models, which are normally specific for a given combination of markers and therefore applicable to a very restricted number of experimental settings. We herein propose ‘marker sampling and excite’—a neural network approach with a modality sampling strategy and a novel attention module that together enable (1) flexible training with heterogeneous datasets with combinations of markers and (2) successful utility of learned models on arbitrary subsets of markers prospectively. We show that our single neural network solution performs comparably to an upper bound scenario in which an ensemble of many networks is naively trained for each possible marker combination separately. We also demonstrate the feasibility of this framework in high-throughput biological analysis by revising a recent quantitative characterization of bone-marrow vasculature in three-dimensional confocal microscopy datasets and further confirm the validity of our approach on another substantially different dataset of microvessels in foetal liver tissues. Not only can our work substantially ameliorate the use of deep learning in fluorescence microscopy analysis, but it can also be utilized in other fields with incomplete data acquisitions and missing modalities.

荧光显微镜可以通过用各种精心选择的标记（可视化为颜色通道）进行染色来详细检查细胞、细胞网络和解剖标志。所获取图像中结构的定量表征通常依赖于自动图像分析方法。尽管深度学习方法在其他视觉应用中取得了成功，但它们在荧光图像分析方面的潜力仍未得到充分开发。原因之一在于训练精确模型需要相当大的工作量，这些模型通常特定于给定的标记组合，因此适用于数量非常有限的实验设置。我们在此提出“标记采样和激发”——一种具有模态采样策略和新颖的注意力模块的神经网络方法，它们共同实现（1）使用具有标记组合的异构数据集进行灵活训练，以及（2）在任意情况下成功利用学习模型前瞻性标记子集。我们表明，我们的单个神经网络解决方案的性能与上限场景相当，在上限场景中，许多网络的集合分别针对每个可能的标记组合进行了简单的训练。我们还通过修改三维共焦显微镜数据集中骨髓脉管系统的最新定量特征来证明该框架在高通量生物分析中的可行性，并进一步证实我们的方法在另一个截然不同的胎儿肝脏微血管数据集上的有效性组织。我们的工作不仅可以大大改善深度学习在荧光显微镜分析中的使用，而且还可以用于数据采集不完整和缺失模式的其他领域。