一种对混养细胞基于形态的细胞分选方法

目的通过对采集的细胞图像的定量识别,并结合基于机器学习的聚类分析,实现对混合培养的多种细胞基于形态的快速识别分选。方法对体外混合培养的A549和3T3两种细胞进行免疫荧光染色以表征其形态轮廓,利用CellProfiler对采集的荧光图片进行细胞形态特征的提取,再通过CellProfiler Analyst对提取的数据进行机器学习,训练出一种规则,形成一种泛化能力,以达到对混合培养的两种细胞进行识别分选的目的。结果训练分类器准确率为81.24%,可以实现A549和3T3细胞的二分类。结论机器学习有助于提升数据聚类分析的准确率,将其应用于细胞图像的识别,可为临床对组织切片进行快速病理检测提供预判断,从而减轻医生的工作量,提高诊断的准确率。

Morphologically Based Cell Classification in Mixed Cultures

Objective To make quantitative analysis on collected cell images combined with machine learning integrated clustering algorithm, so as to explore a method for fast recognition and classification of cells in mixed cultures based on morphology. Methods The morphometric properties of A549 and 3T3 cells in vitro were characterized by immunostaining, the fluorescent images were then analyzed with CellProfiler to extract the parameters of cell morphology. The parameters were loaded into CellProfiler Analyst to be trained with machine learning algorithm, and a rule was developed to form a generalization capability for cell classification in mixed cultures. Results The accuracy of the training classifier was 81-24%, and the binary classifications of A549 and 3T3 cells could be realized. Conclusions The method of machine learning is very effective in parameter clustering. The application of machine learning into cell image recognition can provide pre-judgment for rapid pathological examination of tissue sections, thereby reducing the workload of doctors and improving the accuracy of diagnosis.