Identifying nuclei is a standard first step when analysing cells in microscopy images. The traditional approach relies on signal from a DNA stain, or fluorescent transgene expression localised to the nucleus. However, imaging techniques that do not use fluorescence can also carry useful information. Here, we used brightfield and fluorescence images of fixed cells with fluorescently labelled DNA, and confirmed that three convolutional neural network architectures can be adapted to segment nuclei from the brightfield channel, relying on fluorescence signal to extract the ground truth for training. We found that U-Net achieved the best overall performance, Mask R-CNN provided an additional benefit of instance segmentation, and that DeepCell proved too slow for practical application. We trained the U-Net architecture on over 200 dataset variations, established that accurate segmentation is possible using as few as 16 training images, and that models trained on images from similar cell lines can extrapolate well. Acquiring data from multiple focal planes further helps distinguish nuclei in the samples. Overall, our work helps to liberate a fluorescence channel reserved for nuclear staining, thus providing more information from the specimen, and reducing reagents and time required for preparing imaging experiments.

中文翻译：

---

使用在荧光标记样本上训练的神经网络对明场细胞图像中的细胞核进行实际分割

在分析显微镜图像中的细胞时，识别细胞核是标准的第一步。传统方法依赖于来自 DNA 染色的信号，或定位于细胞核的荧光转基因表达。然而，不使用荧光的成像技术也可以携带有用的信息。在这里，我们使用带有荧光标记 DNA 的固定细胞的明场和荧光图像，并确认三个卷积神经网络架构可以适应从明场通道中分割细胞核，依靠荧光信号来提取训练的基本事实。我们发现 U-Net 实现了最佳的整体性能，Mask R-CNN 提供了实例分割的额外好处，而 DeepCell 被证明对于实际应用来说太慢了。我们在 200 多个数据集变体上训练了 U-Net 架构，确定使用少至 16 个训练图像就可以进行准确分割，并且在来自相似细胞系的图像上训练的模型可以很好地外推。从多个焦平面获取数据进一步有助于区分样品中的原子核。总体而言，我们的工作有助于释放为核染色保留的荧光通道，从而提供更多来自标本的信息，并减少准备成像实验所需的试剂和时间。