Single-detector dual-modality imaging flow cytometry for label-free cell analysis with machine learning

被引:7
|
作者
Wang, Zhiwen [1 ,2 ]
Liu, Qiao [3 ]
Chu, Ran [4 ]
Song, Kun [4 ]
Su, Xuantao [1 ]
机构
[1] Shandong Univ, Sch Microelect, Jinan 250101, Peoples R China
[2] Shandong Univ, Sch Control Sci & Engn, Inst Biomed Engn, Jinan 250061, Peoples R China
[3] Shandong Univ, Sch Basic Med Sci, Dept Mol Med & Genet, Jinan 250012, Peoples R China
[4] Shandong Univ, Cheeloo Coll Med, Dept Obstet & Gynecol, Jinan 250063, Peoples R China
基金
国家自然科学基金重大研究计划;
关键词
Imaging flow cytometry; Dual-modality; Light scattering; Label-free; Machine learning; LIGHT-SCATTERING; CLASSIFICATION; SPECTROSCOPY; VIEW;
D O I
10.1016/j.optlaseng.2023.107665
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
A dual-modality imaging flow cytometer (dIFC) that obtains both brightfield and light scattering images of single cells simultaneously on one detector was developed. The unique optical layout of our system with a knife-edge right-angle (KERA) prism is key for single-detector dual-modality imaging. By using our dIFC, label-free bright-field and light scattering images of single micrometer particles and cells (ovarian cancer cell lines A2780 and Hey) were obtained. Automatic classification of ovarian cell subtypes was achieved with an accuracy of 89.52% for light scattering imaging, and 70.25% for brightfield imaging by integration of dIFC with machine learning. Compared with the obtaining of brightfield and fluorescence images in conventional IFC, our dIFC is promising for label-free cellular analysis especially by adopting the light scattering image modality.
引用
收藏
页数:8
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