Research Center for Analytical Instrumentation, Institute of Cyber-Systems and Control, State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, P. R. China.
Analyst. 2019 May 13;144(10):3274-3281. doi: 10.1039/c9an00149b.
Chip-based digital assays such as the digital polymerase chain reaction (digital PCR), digital loop-mediated amplification (digital LAMP), digital enzyme-linked immunosorbent assay (digital ELISA) and digital proximity ligation assay (digital PLA) need high-throughput quantification of the captured fluorescence image data. However, traditional methods that are mainly based on image segmentation using either a fixed threshold or an automated hard threshold failed to extract valid signals over a broad range of image characteristics. In this study, we introduce a new method for automated image analysis to extract signals applied to chip-based digital assays. This approach precisely locates each micro-compartment based on the structure design of the chip, thereby eliminating the interference of non-signal noise in the image. Utilizing the principle that the human eyes can distinguish between the positive micro-compartments and the negative micro-compartments, we take the parameters of each micro-compartment together with its surrounding micro-compartments as the training dataset of the Random Forest classifier to classify the micro-compartments and extract valid signals, thus solving the problem caused by the differences among images. Furthermore, we adopted the iteration methodology that adds the output of a model's prediction to the input of the next model's training dataset, until the output of a model's prediction reaches the accuracy we expected, which improves the work efficiency during data training greatly. We demonstrate the method on the dPCR dataset and it performs well without any manual adjustment of settings. The results show that our proposed method can recognize the positive signals from the fluorescence images with an accuracy of 97.78%. With minor modification, bio-instrument companies or researchers can integrate this method into their digital assay devices' software conveniently.
基于芯片的数字分析，如数字聚合酶链反应（digital PCR）、数字环介导扩增（digital LAMP）、数字酶联免疫吸附测定（digital ELISA）和数字临近连接分析（digital PLA），需要对捕获的荧光图像数据进行高通量定量。然而，传统的方法主要基于使用固定阈值或自动硬阈值的图像分割，无法在广泛的图像特征范围内提取有效的信号。在本研究中，我们引入了一种新的自动图像分析方法，用于提取应用于基于芯片的数字分析的信号。该方法基于芯片的结构设计精确地定位每个微室，从而消除了图像中非信号噪声的干扰。利用人眼可以区分阳性微室和阴性微室的原理，我们将每个微室及其周围微室的参数作为随机森林分类器的训练数据集，对微室进行分类并提取有效信号，从而解决了由于图像差异引起的问题。此外，我们采用了迭代方法，即将模型预测的输出添加到下一个模型的训练数据集的输入中，直到模型预测的输出达到我们预期的准确性，这大大提高了数据训练的工作效率。我们在 dPCR 数据集上进行了方法验证，并且无需任何手动调整设置即可良好运行。结果表明，我们提出的方法可以从荧光图像中识别出阳性信号，准确率达到 97.78%。只需稍加修改，生物仪器公司或研究人员就可以方便地将这种方法集成到他们的数字分析设备的软件中。
