ArticleNature methods2024
Image restoration of degraded time-lapse microscopy data mediated by near-infrared imaging.
Article in Nature methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed.
- Research on the protection and restoration technology of glazed components in ancient architecture.PloS one · 2026Article
- Remodeling and self-healing of individual amyloid tactoids via multiphoton absorption.Nature communications · 2025Article
- Imaging 3D cell cultures with optical microscopy.Nature methods · 2025Review
- Where imaging and metrics meet.Nature methods · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Time-lapse fluorescence microscopy is key to unraveling biological development and function; however, living systems, by their nature, permit only limited interrogation and contain untapped information that can only be captured by more invasive methods. Deep-tissue live imaging presents a particular challenge owing to the spectral range of live-cell imaging probes/fluorescent proteins, which offer only modest optical penetration into scattering tissues. Herein, we employ convolutional neural networks to augment live-imaging data with deep-tissue images taken on fixed samples. We demonstrate that convolutional neural networks may be used to restore deep-tissue contrast in GFP-based time-lapse imaging using paired final-state datasets acquired using near-infrared dyes, an approach termed InfraRed-mediated Image Restoration (IR
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Registered trials
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