Belashov A V, Zhikhoreva A A, Belyaeva T N, Kornilova E S, Salova A V, Semenova I V, Vasyutinskii O S
J Opt Soc Am A Opt Image Sci Vis. 2020 Feb 1;37(2):346-352. doi: 10.1364/JOSAA.382135.
Digital holographic microscopy supplemented with the developed cell segmentation and machine learning and classification algorithms is implemented for quantitative description of the dynamics of cellular necrosis induced by photodynamic treatment in vitro. It is demonstrated that the developed algorithms operating with a set of optical, morphological, and physiological parameters of cells, obtained from their phase images, can be used for automatic distinction between live and necrotic cells. The developed classifier provides high accuracy of about 95.5% and allows for calculation of survival rates in the course of cell death.
结合所开发的细胞分割、机器学习和分类算法的数字全息显微镜，用于体外光动力治疗诱导的细胞坏死动力学的定量描述。结果表明，所开发的算法通过从细胞的相位图像中获取的一组光学、形态学和生理学参数进行操作，可用于自动区分活细胞和坏死细胞。所开发的分类器提供了约95.5%的高精度，并允许计算细胞死亡过程中的存活率。
