ArticleeLife2020
Revealing architectural order with quantitative label-free imaging and deep learning.
Article in eLife, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
41 citing papers in PubMed, 85 citations in OpenAlex.
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- A correlative quantitative phase contrast and fluorescence super-resolution microscope for imaging molecules in their cellular context.bioRxiv : the preprint server for biology · 2025Article
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- Revealing 3D microanatomical structures of unlabeled thick cancer tissues using holotomography and virtual H&E staining.Nature communications · 2025Article
- Extensible Immunofluorescence (ExIF) accessibly generates high-plexity datasets by integrating standard 4-plex imaging data.Nature communications · 2025Article
- Surpassing the Diffraction Limit in Label-Free Optical Microscopy.ACS photonics · 2024Review
- Mantis: High-throughput 4D imaging and analysis of the molecular and physical architecture of cells.PNAS nexus · 2024Article
- Information-Distilled Generative Label-Free Morphological Profiling Encodes Cellular Heterogeneity.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Permittivity tensor imaging: modular label-free imaging of 3D dry mass and 3D orientation at high resolution.Nature methods · 2024Article
- Quantitative phase imaging based on holography: trends and new perspectives.Light, science & applications · 2024Review
- Mantis: high-throughput 4D imaging and analysis of the molecular and physical architecture of cells.bioRxiv : the preprint server for biology · 2024Article
- Dynamic label-free analysis of SARS-CoV-2 infection reveals virus-induced subcellular remodeling.Nature communications · 2024Article
- Perspective on quantitative phase imaging to improve precision cancer medicine.Journal of biomedical optics · 2024Article
- PIFiA: self-supervised approach for protein functional annotation from single-cell imaging data.Molecular systems biology · 2024Article
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- On the use of deep learning for phase recovery.Light, science & applications · 2024Review
- Practical considerations for birefringence microscopy of myelin structure: Microscope design and tissue processing for effective imaging.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Artificial intelligence-enabled quantitative phase imaging methods for life sciences.Nature methods · 2023Review
- Cell-Sized Confinements Alter Molecular Diffusion in Concentrated Polymer Solutions Due to Length-Dependent Wetting of Polymers.ACS materials Au · 2023Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors at 3 institutions in 1 country.
Funding
Abstract
We report quantitative label-free imaging with phase and polarization (QLIPP) for simultaneous measurement of density, anisotropy, and orientation of structures in unlabeled live cells and tissue slices. We combine QLIPP with deep neural networks to predict fluorescence images of diverse cell and tissue structures. QLIPP images reveal anatomical regions and axon tract orientation in prenatal human brain tissue sections that are not visible using brightfield imaging. We report a variant of U-Net architecture, multi-channel 2.5D U-Net, for computationally efficient prediction of fluorescence images in three dimensions and over large fields of view. Further, we develop data normalization methods for accurate prediction of myelin distribution over large brain regions. We show that experimental defects in labeling the human tissue can be rescued with quantitative label-free imaging and neural network model. We anticipate that the proposed method will enable new studies of architectural order at spatial scales ranging from organelles to tissue.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.