Machine Learning Assisted Classification of Cell Lines and Cell States on Quantitative Phase Images
| Autor: | A. V. Salova, Oleg S. Vasyutinskii, T. N. Belyaeva, Irina V. Semenova, Elena S. Kornilova, Andrey V. Belashov, A.A. Zhikhoreva |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Programmed cell death
Cell type quantitative phase imaging Computer science QH301-705.5 Cell cell classification 3T3 digital holography Phase image Article necrosis HeLa Machine Learning A549 Cell Line Tumor Classifier (linguistics) medicine Humans Microscopy Phase-Contrast Biology (General) machine-learning algorithms biology business.industry apoptosis Pattern recognition General Medicine biology.organism_classification medicine.anatomical_structure cell death Apoptosis Cell culture Artificial intelligence business HeLa Cells |
| Zdroj: | Cells, Vol 10, Iss 2587, p 2587 (2021) Cells Volume 10 Issue 10 |
| ISSN: | 2073-4409 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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