Label-free prediction of three-dimensional fluorescence images from transmitted light microscopy
By Chawin Ounkomol, S. Seshamani, Mary M. Maleckar, F. Collman, Gregory R. Johnson. Nature Methods, 2018
493 citations recorded by the source corpus.
Understanding cells as integrated systems is central to modern biology. Although fluorescence microscopy can resolve subcellular structure in living cells, it is expensive, is slow, and can damage cells. We present a label-free method for predicting three-dimensional fluorescence directly from transmitted-light images and demonstrate that it can be used to generate multi-structure, integrated images. The method can also predict immunofluorescence (IF) from electron micrograph (EM) inputs, extending the potential applications. Convolutional neural networks enable prediction of fluorescently labeled structures from three-dimensional time-lapse transmitted-light images. Applications include multiplexed long time-lapse imaging and prediction of fluorescence in electron micrographs.
Subfields: Biophysics, Media Technology
View Paper: https://www.semanticscholar.org/paper/070a7bb5607fe6c81974190644ea62f119b146f4
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Citations in the corpus
- Deep learning for cellular image analysis (2019, methodology): “…and nucleus, researchers at the Allen Institute used a conditional generative model to create photo-realistic 3D fluorescence microscopy images, including the structural and functional information that the images are meant to represent, from 3D transmitted light microscopy images 138,140,142,143 .”
- High-performance medicine: the convergence of human and artificial intelligence (2019, background): “Many of the actual output recommendations for treatment were shown to be erroneous, such as suggesting the use of bevacizumab in a patient with severe bleeding, which represents an explicit contraindication and ‘black box’ warning for the drug 189 .”