Su Xuantao, Liu Shanshan, Qiao Xu, Yang Yan, Song Kun, Kong Beihua
Opt Express. 2015 Oct 19;23(21):27558-65. doi: 10.1364/OE.23.027558.
We develop a pattern recognition cytometric technique for label-free cell classification. Two dimensional (2D) light scattering patterns from single cells and cell aggregates are obtained with a static cytometer. Good performance of the cytometric setup is verified by comparing yeast cell experimental results with theoretical simulations. Adaptive boosting (AdaBoost) method (a machine learning algorithm) is adopted for the analysis of the 2D light scattering patterns. It is shown that aggregates of three yeast cells can be well differentiated from aggregates of four yeast cells by this pattern recognition cytometric technique. We demonstrate that the pattern recognition cytometry can perform label-free classification of normal cervical cells and HeLa cells with a high accuracy rate.
我们开发了一种用于无标记细胞分类的模式识别细胞计数技术。使用静态细胞仪获取单细胞和细胞聚集体的二维（2D）光散射模式。通过将酵母细胞实验结果与理论模拟进行比较，验证了细胞计数设置的良好性能。采用自适应增强（AdaBoost）方法（一种机器学习算法）对二维光散射模式进行分析。结果表明，通过这种模式识别细胞计数技术，可以很好地区分三个酵母细胞的聚集体和四个酵母细胞的聚集体。我们证明，模式识别细胞计数法能够以高准确率对正常宫颈细胞和HeLa细胞进行无标记分类。
