Deep learning for label-free nuclei detection from implicit phase information of mesenchymal stem cells
Name
boe-12-3-1683.pdf
Description
Published version
Size
7.1 MB
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Adobe PDF
Checksum (MD5)
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Author(s)
Zhang, Zhengyun
Leong, Kim Whye
Vliet, Krystyn Van
Barbastathis, George
Ravasio, Andrea
Date Issued
2021
Journal
Biomedical Optics Express
Publisher
The Optical Society
Citation
Zhang, Zhengyun, Leong, Kim Whye, Vliet, Krystyn Van, Barbastathis, George and Ravasio, Andrea. 2021. "Deep learning for label-free nuclei detection from implicit phase information of mesenchymal stem cells." Biomedical Optics Express, 12 (3).
Version
Final published version
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.
MIT Department
Singapore-MIT Alliance in Research and Technology (SMART)
Massachusetts Institute of Technology. Department of Materials Science and Engineering
Massachusetts Institute of Technology. Department of Mechanical Engineering
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1364/BOE.420266
