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Title: Deep learning for label-free nuclei detection from implicit phase information of mesenchymal stem cells
Authors: Zhang, Zhengyun
Leong, Kim Whye
Van Vliet, Krystyn
Barbastathis, George
Ravasio, Andrea
Issue Date: 1-Mar-2021
Publisher: The Optical Society
Citation: Zhang, Zhengyun, Leong, Kim Whye, Van Vliet, Krystyn, Barbastathis, George, Ravasio, Andrea (2021-03-01). Deep learning for label-free nuclei detection from implicit phase information of mesenchymal stem cells. Biomedical Optics Express 12 (3) : 1683-1706. ScholarBank@NUS Repository.
Rights: Attribution 4.0 International
Abstract: Monitoring of adherent cells in culture is routinely performed in biological and clinical laboratories, and it is crucial for large-scale manufacturing of cells needed in cell-based clinical trials and therapies. However, the lack of reliable and easily implementable label-free techniques makes this task laborious and prone to human subjectivity. We present a deep-learning-based processing pipeline that locates and characterizes mesenchymal stem cell nuclei from a few bright-field images captured at various levels of defocus under collimated illumination. Our approach builds upon phase-from-defocus methods in the optics literature and is easily applicable without the need for special microscopy hardware, for example, phase contrast objectives, or explicit phase reconstruction methods that rely on potentially bias-inducing priors. Experiments show that this label-free method can produce accurate cell counts as well as nuclei shape statistics without the need for invasive staining or ultraviolet radiation. We also provide detailed information on how the deep-learning pipeline was designed, built and validated, making it straightforward to adapt our methodology to different types of cells. Finally, we discuss the limitations of our technique and potential future avenues for exploration. © 2021 Optical Society of America
Source Title: Biomedical Optics Express
ISSN: 2156-7085
DOI: 10.1364/boe.420266
Rights: Attribution 4.0 International
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