2021
DOI: 10.1371/journal.pone.0258546
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Learning to see colours: Biologically relevant virtual staining for adipocyte cell images

Abstract: Fluorescence microscopy, which visualizes cellular components with fluorescent stains, is an invaluable method in image cytometry. From these images various cellular features can be extracted. Together these features form phenotypes that can be used to determine effective drug therapies, such as those based on nanomedicines. Unfortunately, fluorescence microscopy is time-consuming, expensive, labour intensive, and toxic to the cells. Bright-field images lack these downsides but also lack the clear contrast of … Show more

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Cited by 10 publications
(6 citation statements)
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“…Training with an adversarial scheme using a conditional GAN approach has been shown to enhance performance in virtual staining tasks 35 . In preliminary experiments, we tested different GAN models and adversarial weightings, but we chose cWGAN-GP due to its stability of training on our dataset.…”
Section: Discussionmentioning
confidence: 99%
“…Training with an adversarial scheme using a conditional GAN approach has been shown to enhance performance in virtual staining tasks 35 . In preliminary experiments, we tested different GAN models and adversarial weightings, but we chose cWGAN-GP due to its stability of training on our dataset.…”
Section: Discussionmentioning
confidence: 99%
“…Had the performance been significantly poorer for the BF models, than those based on FL, one avenue for exploration would have been to perform virtual staining [15, 16, 17] to generate virtually stained images from which to subsequently base the MoA prediction. However, going via this route would lose any morphological information that is only present in the BF images.…”
Section: Discussionmentioning
confidence: 99%
“…Consequently, so‐called label‐free or virtual staining approaches have been considered which may allow bypassing fluorescent labeling altogether. Recent studies in fluorescent imaging have used deep learning models to virtually stain brightfield images of adipose tissue [11], detect acute lymphoblastic leukemia cells [12] and to discriminate between different cell lines [13]. Machine learning on IFC brightfield and darkfield images have been used to distinguish cell types [8] and transitions between cell states [7].…”
Section: Introductionmentioning
confidence: 99%