School of Microelectronics, Shandong University, Jinan, China.
Institute of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, China.
Cytometry A. 2021 Jun;99(6):610-621. doi: 10.1002/cyto.a.24349. Epub 2021 Apr 22.
Cervical cancer is a major gynecological malignant tumor that threatens women's health. Current cytological methods have certain limitations for cervical cancer early screening. Light scattering patterns can reflect small differences in the internal structure of cells. In this study, we develop a light scattering pattern specific convolutional network (LSPS-net) based on deep learning algorithm and integrate it into a 2D light scattering static cytometry for automatic, label-free analysis of single cervical cells. An accuracy rate of 95.46% for the classification of normal cervical cells and cancerous ones (mixed C-33A and CaSki cells) is obtained. When applied for the subtyping of label-free cervical cell lines, we obtain an accuracy rate of 93.31% with our LSPS-net cytometric technique. Furthermore, the three-way classification of the above different types of cells has an overall accuracy rate of 90.90%, and comparisons with other feature descriptors and classification algorithms show the superiority of deep learning for automatic feature extraction. The LSPS-net static cytometry may potentially be used for cervical cancer early screening, which is rapid, automatic and label-free.
宫颈癌是一种严重威胁妇女健康的妇科恶性肿瘤。目前的细胞学方法在宫颈癌的早期筛查中存在一定的局限性。光散射模式可以反映细胞内部结构的微小差异。在这项研究中，我们开发了一种基于深度学习算法的光散射模式特定卷积网络（LSPS-net），并将其集成到二维光散射静态细胞仪中，用于自动、无标记分析单个宫颈细胞。该方法对正常宫颈细胞和癌细胞（混合 C-33A 和 CaSki 细胞）的分类准确率达到 95.46%。当应用于无标记宫颈细胞系的亚型分类时，我们的 LSPS-net 细胞仪技术的准确率达到 93.31%。此外，上述不同类型细胞的三分类总体准确率为 90.90%，与其他特征描述符和分类算法的比较表明，深度学习在自动特征提取方面具有优越性。LSPS-net 静态细胞仪可能有望用于快速、自动和无标记的宫颈癌早期筛查。
