Quantification of phenotypic heterogeneity present amongst bacterial cells can be a challenging task. Conventionally, classification and counting of bacteria sub-populations is achieved with manual microscopy, due to the lack of alternative, high-throughput, autonomous approaches. In this work, we apply classification-type convolutional neural networks (cCNN) to classify and enumerate bacterial cell sub-populations (B. subtilis clusters). Here, we demonstrate that the accuracy of the cCNN developed in this study can be as high as 86% when trained on a relatively small dataset (81 images). We also developed a new image preprocessing algorithm, specific to fluorescent microscope images, which increases the amount of training data available for the neural network by 72 times. By summing the classified cells together, the algorithm provides a total cell count which is on parity with manual counting, but is 10.2 times more consistent and 3.8 times faster. Finally, this work presents a complete solution framework for those wishing to learn and implement cCNN in their synthetic biology work.

中文翻译：

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利用卷积神经网络对细菌细胞亚群进行自动分类。

细菌细胞中存在的表型异质性的量化可能是一项艰巨的任务。常规地，由于缺乏替代的，高通量的，自主的方法，细菌的亚群的分类和计数通过手动显微镜来实现。在这项工作中，我们应用分类类型的卷积神经网络（cCNN）来分类和枚举细菌细胞亚群（枯草芽孢杆菌簇）。在这里，我们证明了在相对较小的数据集（81张图像）上进行训练时，本研究开发的cCNN的准确性可以高达86％。我们还开发了针对荧光显微镜图像的新图像预处理算法，该算法将可用于神经网络的训练数据量增加了72倍。通过将分类的单元格汇总在一起，该算法提供的总单元计数与手动计数相当，但是一致性提高了10.2倍，速度提高了3.8倍。最后，这项工作为希望在合成生物学工作中学习和实施cCNN的人们提供了一个完整的解决方案框架。