Ioffe Institute, 26, Polytekhnicheskaya, 194021 St. Petersburg, Russia.
Institute of Cytology of RAS, 4, Tikhoretsky pr., 194064 St. Petersburg, Russia.
Cells. 2021 Sep 29;10(10):2587. doi: 10.3390/cells10102587.
In this report, we present implementation and validation of machine-learning classifiers for distinguishing between cell types (HeLa, A549, 3T3 cell lines) and states (live, necrosis, apoptosis) based on the analysis of optical parameters derived from cell phase images. Validation of the developed classifier shows the accuracy for distinguishing between the three cell types of about 93% and between different cell states of the same cell line of about 89%. In the field test of the developed algorithm, we demonstrate successful evaluation of the temporal dynamics of relative amounts of live, apoptotic and necrotic cells after photodynamic treatment at different doses.
在本报告中，我们介绍了基于细胞相位图像光学参数分析，用于区分细胞类型（HeLa、A549、3T3 细胞系）和状态（活细胞、坏死、凋亡）的机器学习分类器的实现和验证。所开发分类器的验证结果表明，对三种细胞类型的区分准确率约为 93%，对同一细胞系不同细胞状态的区分准确率约为 89%。在开发算法的现场测试中，我们成功评估了不同剂量光动力处理后活细胞、凋亡细胞和坏死细胞相对数量的时间动态。
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