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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