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11 March 2020 Digital staining with quantitative phase imaging for time-lapse studies of cellular growth and proliferation (Conference Presentation)
Mikhail E. Kandel, Young Jae Lee, Taylor H. Chen, Yuchen R. He, Nahil Sohb, Gabriel Popescu
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Proceedings Volume 11249, Quantitative Phase Imaging VI; 1124918 (2020) https://doi.org/10.1117/12.2550399
Event: SPIE BiOS, 2020, San Francisco, California, United States
Abstract
Microscopic imaging modalities can be classified into two categories: those that form contrast from external agents such as dyes, and label-free methods that generate contrast from the object’s unmodified structure. While label-free methods such as brightfield, phase contrast, or quantitative phase imaging (QPI) are substantially easier to use, as well as non-toxic, their lack of specificity leads many researchers to turn to labels for insights into biological processes, despite limitations due to photobleaching and phototoxicity. The label-free image may contain the structures of interest, but it is often difficult or time-consuming to distinguish these structures from their surroundings. Here we summarize our recent progress in shattering this tradeoff, by using machine learning to perform automated segmentation on label-free, intrinsic contrast, quantitative phase images.
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Mikhail E. Kandel, Young Jae Lee, Taylor H. Chen, Yuchen R. He, Nahil Sohb, and Gabriel Popescu "Digital staining with quantitative phase imaging for time-lapse studies of cellular growth and proliferation (Conference Presentation)", Proc. SPIE 11249, Quantitative Phase Imaging VI, 1124918 (11 March 2020); https://doi.org/10.1117/12.2550399
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KEYWORDS
Phase imaging
Image segmentation
Lead
Machine learning
Phase contrast
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