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Proceedings Paper • Open Access

Digital staining with quantitative phase imaging for time-lapse studies of cellular growth and proliferation (Conference Presentation)

Paper 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.

Paper Details

Date Published: 11 March 2020
Proc. SPIE 11249, Quantitative Phase Imaging VI, 1124918 (11 March 2020); doi: 10.1117/12.2550399
Show Author Affiliations
Mikhail E. Kandel, Univ. of Illinois (United States)
Young Jae Lee, Univ. of Illinois (United States)
Taylor H. Chen, Univ. of Illinois (United States)
Yuchen R. He, Univ. of Illinois (United States)
Nahil Sohb, Univ. of Illinois (United States)
Gabriel Popescu, Univ. of Illinois (United States)

Published in SPIE Proceedings Vol. 11249:
Quantitative Phase Imaging VI
Yang Liu; Gabriel Popescu; YongKeun Park, Editor(s)

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