As one of the most widely used assays in biological research, an enumeration of the bacterial cell colonies is an important but time-consuming and labor-intensive process. To speed up the colony counting, a machine learning method is presented for counting the colony forming units (CFUs), which is referred to as CFUCounter. This cell-counting program processes digital images and segments bacterial colonies. The algorithm combines unsupervised machine learning, iterative adaptive thresholding, and local-minima-based watershed segmentation to enable an accurate and robust cell counting. Compared to a manual counting method, CFUCounter supports color-based CFU classification, allows plates containing heterologous colonies to be counted individually, and demonstrates overall performance (slope 0.996, SD 0.013, 95%CI: 0.97–1.02, p value < 1e-11, r = 0.999) indistinguishable from the gold standard of point-and-click counting. This CFUCounter application is open-source and easy to use as a unique addition to the arsenal of colony-counting tools.

中文翻译：

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用于计数细胞集落形成单位的机器学习

作为生物学研究中使用最广泛的检测方法之一，细菌细胞集落的计数是一个重要但耗时且劳动密集型的过程。为了加快菌落计数，提出了一种用于计数菌落形成单位 (CFU) 的机器学习方法，称为 CFUCounter。该细胞计数程序处理数字图像并分割细菌菌落。该算法结合了无监督机器学习、迭代自适应阈值和基于局部最小值的分水岭分割，以实现准确和稳健的细胞计数。与手动计数方法相比，CFUCounter 支持基于颜色的 CFU 分类，允许对含有异源菌落的板进行单独计数，并展示了整体性能（斜率 0.996，SD 0.013，95%CI：0.97–1.02，p 值 < 1e-11 , r = 0.999) 与点击计数的黄金标准没有区别。这个 CFUCounter 应用程序是开源的，易于使用，是菌落计数工具库的独特补充。