The measurement of cell viability plays an essential role in the area of cell biology. At present, the common methods for cell viability assay mainly on the responses of cells to different dyes. However, the additional steps of cell staining will consequently cause time-consuming and laborious efforts. Furthermore, the process of cell staining is invasive and may cause internal structure damage of cells, restricting their reuse in subsequent experiments. In this work, we proposed a label-free method to classify live and dead colonic adenocarcinoma cells by 2D light scattering combined with the deep learning algorithm. The deep convolutional network of YOLO-v3 was used to identify and classify light scattering images of live and dead HT29 cells. This method achieved an excellent sensitivity (93.6%), specificity (94.4%), and accuracy (94%). The results showed that the combination of 2D light scattering images and deep neural network may provide a new label-free method for cellular analysis.

中文翻译：

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基于二维光散射和深度学习分析的死和活结肠腺癌细胞的无标记分类

细胞活力的测量在细胞生物学领域起着至关重要的作用。目前，细胞活力测定的常用方法主要是细胞对不同染料的反应。然而，细胞染色的额外步骤将因此导致耗时且费力的工作。此外，细胞染色的过程是侵入性的，可能会导致细胞内部结构受损，限制了它们在后续实验中的重复使用。在这项工作中，我们提出了一种通过二维光散射结合深度学习算法对活的和死的结肠腺癌细胞进行分类的无标记方法。YOLO-v3 的深度卷积网络用于对活和死 HT29 细胞的光散射图像进行识别和分类。该方法实现了极好的灵敏度 (93.6%)、特异性 (94.4%) 和准确度 (94%)。