ArticleScientific reports2023
Development of a deep learning based image processing tool for enhanced organoid analysis.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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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
33 citing papers in PubMed.
- Organoid Intelligent Morphomics: Decoding the organoid morphome through artificial intelligence from phenotypic quantification to mechanistic insight.Bioactive materials · 2027Review
- An AI-integrated organoid platform enables high-throughput functional evaluation of bioactive metal ions.Bioactive materials · 2026Article
- OrgLine: A versatile pipeline for organoid morphometry using detector-guided prompts.Cell reports methods · 2026Article
- Deep Learning-Based Prediction of Epithelial Cytokine Responses for the Selection of Functionally Consistent Airway Organoids.Biomimetics (Basel, Switzerland) · 2026Article
- Human organoids: Fit for drug discovery?Stem cell reports · 2026Review
- International multisite implementation of distributed cell-free protein biomanufacturing to advance health and research equity.Science advances · 2026Article
- Image Tracing of Inflammatory Intestinal Organoids via Computational Clearing.Nanomaterials (Basel, Switzerland) · 2026Article
- BrAIn: A comprehensive artificial intelligence-based morphology analysis system for brain organoids and neuroscience.Bioengineering & translational medicine · 2026Article
- An automated image analysis pipeline for wide-field optical redox imaging of patient-derived cancer organoids.Scientific reports · 2026Article
- Fast learning-free organoid quantification and tracking with OrganoSeg2.Scientific reports · 2026Article
- Biomaterials in organoid research: current state and future directions.Frontiers in bioengineering and biotechnology · 2026Review
- Revolutionizing transdermal drug delivery evaluation: the evolution from traditional models to skin organoids and the integration of artificial intelligence.Frontiers in pharmacology · 2026Review
- Methods to analyze cell migration data: fundamentals and practical guidelines.Nature methods · 2026Review
- AI-assisted quantitative analysis for evaluating melanin distribution in 3D pigmented epidermis-on-a-chip models.Frontiers in bioengineering and biotechnology · 2026Article
- Lightweight Deep Learning Model for Classification of Normal and Abnormal Vasculature in Organoid Images.Sensors (Basel, Switzerland) · 2025Article
- Artificial Intelligence in Organoid-Based Disease Modeling: A New Frontier in Precision Medicine.Biomimetics (Basel, Switzerland) · 2025Review
- OSCAR is an online ML-powered tool for organoid cell counting using bright-field images.Cell reports methods · 2025Article
- Immune and Immune-Integrated Organoids as NextGeneration Platforms for Disease Modeling.MedComm · 2025Review
- Enhanced U-Net-Based Deep Learning Model for Automated Segmentation of Organoid Images.Bioengineering (Basel, Switzerland) · 2025Article
- Iteration of Tumor Organoids in Drug Development: Simplification and Integration.Pharmaceuticals (Basel, Switzerland) · 2025Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Contrary to 2D cells, 3D organoid structures are composed of diverse cell types and exhibit morphologies of various sizes. Although researchers frequently monitor morphological changes, analyzing every structure with the naked eye is difficult. Given that deep learning (DL) has been used for 2D cell image segmentation, a trained DL model may assist researchers in organoid image recognition and analysis. In this study, we developed OrgaExtractor, an easy-to-use DL model based on multi-scale U-Net, to perform accurate segmentation of organoids of various sizes. OrgaExtractor achieved an average dice similarity coefficient of 0.853 from a post-processed output, which was finalized with noise removal. Correlation between CellTiter-Glo assay results and daily measured organoid images shows that OrgaExtractor can reflect the actual organoid culture conditions. The OrgaExtractor data can be used to determine the best time point for organoid subculture on the bench and to maintain organoids in the long term.
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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.