ArticlePatterns (New York, N.Y.)2024
VONet: A deep learning network for 3D reconstruction of organoid structures with a minimal number of confocal images.
Article in Patterns (New York, N.Y.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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.
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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
6 citing papers in PubMed.
- Organoid Intelligent Morphomics: Decoding the organoid morphome through artificial intelligence from phenotypic quantification to mechanistic insight.Bioactive materials · 2027Review
- Artificial intelligence virtual bone organoids (AIVBOs).Journal of orthopaedic translation · 2026Review
- Review
- Innovations in skin microphysiological systems for nonclinical testing and FDA modernization.Microsystems & nanoengineering · 2026Review
- Organoids meet microfluidics: recent advancements, challenges, and future of organoids-on-chip.In vitro models · 2025Review
- AI-based hardware and software tools in microscopy to boost research in immunology and virology.Frontiers in immunology · 2025Review
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Organoids and 3D imaging techniques are crucial for studying human tissue structure and function, but traditional 3D reconstruction methods are expensive and time consuming, relying on complete z stack confocal microscopy data. This paper introduces VONet, a deep learning-based system for 3D organoid rendering that uses a fully convolutional neural network to reconstruct entire 3D structures from a minimal number of z stack images. VONet was trained on a library of over 39,000 virtual organoids (VOs) with diverse structural features and achieved an average intersection over union of 0.82 in performance validation. Remarkably, VONet can predict the structure of deeper focal plane regions, unseen by conventional confocal microscopy. This innovative approach and VO dataset offer significant advancements in 3D bioimaging technologies.
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Registered trials
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