Detection of non-small cell lung cancer cells based on microfluidic polarization microscopic image analysis
Detection of non-small cell lung cancer cells based on microfluidic polarization microscopic image analysis
复制标题
基于微流控偏振显微图像分析的非小细胞肺癌细胞检测
DOI：
10.1002/elps.201800284
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发表时间：
2019
期刊：
影响因子：
2.9
通讯作者：
Zhang Xiaohui
中科院分区：
文献类型：
--
作者：
Wang Yanjuan;Wang Junsheng;Meng Jie;Ding Gege;Shi Zhi;Wang Ruoyu;Zhang Xiaohui
在肺癌的早期诊断中，偏光显微镜是获取生物组织光学信息的有力工具。本文提出了一种新的微流控偏振成像和分析方法，用于癌相关成纤维细胞和两种非小细胞肺癌细胞A549和H322的检测和分类。基于商业显微镜构建偏光显微镜系统以获得细胞的3 × 3 Mueller矩阵。基于Muller矩阵分解算法，在空域和频域分析的基础上，选取合适的分类参数表征细胞的不同极化特性。最后，基于机器学习的逻辑回归模型被应用于确定最佳特征参数和分类细胞。该方法综合了细胞的形态信息和细胞在不同极化状态下的极化特性。这是首次将偏光显微图像分析方法应用于非小细胞肺癌细胞的检测和分类。实验结果表明，所提出的微流控偏振显微图像分析方法能够有效地对细胞进行分类。与Muller矩阵测量和计算方法相比，该方法在偏振图像的获取和偏振图像的分析处理方面都得到了极大的简化。
In early diagnosis of lung cancer, a polarization microscopy is a powerful tool to obtain the optical information of biological tissues. In this paper, a new microfluidic polarization imaging and analysis method was proposed for the detection and classification of cancer‐associated fibroblasts and the two kinds of non‐small cell lung cancer cells, A549 and H322. A polarizing microscopy system was constructed based on a commercial microscope to obtain 3*3 Mueller matrix of cells. Based on the Muller matrix decomposition algorithm and analysis in spatial domain and frequency domain, appropriate classification parameters were selected for the characterization of different polarization characteristics of cells. Finally, the logistic regression models based on machine learning were applied to determine optimal feature parameters and classify cells. This method integrated the morphological information of the cells, and the polarization characteristics of the cells in different polarization states. It is for the first time that the polarization microscopic image analysis method has been applied to the detection and classification of non‐small cell lung cancer cells. The results show that the presented microfluidic polarization microscopic image analysis method could classify cells effectively. Compared with the Muller matrix measurement and calculation methods, the method proposed in this paper was greatly simplified in both the acquisition of polarized images and the analysis and processing of polarized images.