Liu Shanshan, Chu Ran, Xie Jinmei, Song Kun, Su Xuantao
School of Microelectronics, Shandong University, Jinan, China.
Institute of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, China.
Cytometry A. 2023 Mar;103(3):240-250. doi: 10.1002/cyto.a.24684. Epub 2022 Sep 7.
Cervical cancer is a high-risk disease that threatens women's health globally. In this study, we developed the multi-modal static cytometry that adopted different features to classify the typical human cervical epithelial cells (H8) and cervical cancer cells (HeLa). With the light-sheet static cytometry, we obtain brightfield (BF) images, fluorescence (FL) images and two-dimensional (2D) light scattering (LS) patterns of single cervical cells. Three feature extraction methods are used to extract multi-modal features based on different data characteristics. Analysis and classification of morphological and textural features demonstrate the potential of intracellular mitochondria in cervical cancer cell classification. The deep learning method is used to automatically extract deep features of label-free LS patterns, and an accuracy of 76.16% for the classification of the above two kinds of cervical cells is obtained, which is higher than the other two single modes (BF and FL). Our multi-modal static cytometry uses a variety of feature extraction and analysis methods to provide the mitochondria as promising internal biomarkers for cervical cancer diagnosis, and to show the promise of label-free, automatic classification of early cervical cancer with deep learning-based 2D light scattering.
宫颈癌是一种在全球范围内威胁女性健康的高危疾病。在本研究中，我们开发了多模态静态细胞术，该技术采用不同特征对典型的人宫颈上皮细胞（H8）和宫颈癌细胞（HeLa）进行分类。通过光片静态细胞术，我们获得了单个宫颈细胞的明场（BF）图像、荧光（FL）图像和二维（2D）光散射（LS）模式。基于不同的数据特征，使用三种特征提取方法来提取多模态特征。形态学和纹理特征的分析与分类证明了细胞内线粒体在宫颈癌细胞分类中的潜力。利用深度学习方法自动提取无标记LS模式的深度特征，对上述两种宫颈细胞分类的准确率达到76.16%，高于其他两种单一模式（BF和FL）。我们的多模态静态细胞术使用多种特征提取和分析方法，将线粒体作为宫颈癌诊断中有前景的内部生物标志物，并展示了基于深度学习的2D光散射对早期宫颈癌进行无标记、自动分类的前景。
