| 周雨宁,夏 华,王晓莉,柳竞涵,翟 嘉,李晨曦,蒋景英.基于谱分解动态散射成像的细胞无标记
检测与分类方法[J].电子测量与仪器学报,2022,36(6):42-47 |
| 基于谱分解动态散射成像的细胞无标记
检测与分类方法 |
| Study on label-free cell detection and classification method byusing spectral decomposition-based dynamic scattering imaging |
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| DOI： |
| 中文关键词: 动态散射成像 谱分解 无标记细胞检测与分类 机器学习 |
| 英文关键词:dynamic light scattering imaging spectral decomposition label-free cell detection and classification machine learning |
| 基金项目:国家自然科学基金（81971662, 81871396）、北京市自然科学基金（7202105）、天津市自然科学基金（20JCZDJC00630）项目资助 |
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| 摘要点击次数: 921 |
| 全文下载次数: 1230 |
| 中文摘要: |
| 细胞成像及检测技术在医学研究及临床诊断领域具有重要的研究意义和应用价值,而无标记与高通量检测尤其具有挑
战。 本研究基于动态散射理论的细胞成像方法,搭建了动态散射成像系统,提出了基于谱分解的动态信号提取算法,结合机器
学习算法实现了无标记、高通量的细胞分类。 采用血细胞、EG7-OVA 肿瘤细胞、A549 肺癌肿瘤细胞进行实验验证,结果表明本
文提出的方法对血细胞与肿瘤细胞识别准确率可达 98%以上,对于血细胞、EG7-OVA 细胞和 A549 细胞之间的三分类识别率约
为 91%。 本文实现的细胞检测和分类方法具有临床应用前景。 |
| 英文摘要: |
| Cell imaging and detection are of great significance in the field of biomedical research and clinical diagnosis, while label-free
and high-throughput detections are particularly challenging. On the basis of dynamic scattering theory, this study built a dynamic
scattering imaging system, proposed a spectral decomposition-based dynamic signal extraction algorithm, and achieved label-free and
high-throughput cell classification by combining machine learning algorithms. Blood cells, EG7-OVA tumor cells and A549 lung cancer
tumor cells are used to verify the current method. Experimental results show 98% accuracy for binary classification of blood cells and
tumor cells, and 91% accuracy for the three-type classification of blood cells, EG7-OVA and A549. In summary, the proposed method
provides high-throughput, label-free cell detection and classification, and is potential for clinical application. |
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