ArticleScientific reports2019
OrgaQuant: Human Intestinal Organoid Localization and Quantification Using Deep Convolutional Neural Networks.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 68 papers, 1 of them a synthesis that pooled it.
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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
68 citing papers in PubMed, 1 synthesis or guideline pooled it, 130 citations in OpenAlex.
- Pooled it
- Organoid Intelligent Morphomics: Decoding the organoid morphome through artificial intelligence from phenotypic quantification to mechanistic insight.Bioactive materials · 2027Review
- From pixels to patterns: the AI revolution in stem cell-derived models.Nature methods · 2026Review
- A Single-organoid Workflow for quantitative Imaging classiFication and Tracking (SWIFT).Communications biology · 2026Article
- OrgLine: A versatile pipeline for organoid morphometry using detector-guided prompts.Cell reports methods · 2026Article
- BrAIn: A comprehensive artificial intelligence-based morphology analysis system for brain organoids and neuroscience.Bioengineering & translational medicine · 2026Article
- Tumor Organoid and Microenvironment Cocultures: Implications for Basic and Translational Cancer Research.MedComm · 2026Review
- Organoids in drug development: from predictive models to regulatory integration.Drug discovery today · 2026Review
- Integrated Microfluidic Platform for High-Throughput Generation of Intestinal Organoids in Hydrogel Droplets.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Fast learning-free organoid quantification and tracking with OrganoSeg2.Scientific reports · 2026Article
- From organoid culture to manufacturing: technologies for reproducible and scalable organoid production.npj biomedical innovations · 2026Review
- Development and validation of deep learning for predicting the growth of ovarian cancer organoids.Chinese medical journal · 2026Article
- Inflammasomes meet organoids and artificial intelligence: unraveling the complexity of gynecological inflammation.Frontiers in immunology · 2026Review
- A deep learning-based computational pipeline predicts developmental outcome in retinal organoids.PLoS biology · 2026Article
- Immune and Immune-Integrated Organoids as NextGeneration Platforms for Disease Modeling.MedComm · 2025Review
- Variability vs. phenotype: Multimodal analysis of Dravet syndrome brain organoids powered by deep learning.iScience · 2025Article
- A knowledge-driven deep learning framework for organoid morphological segmentation and characterization.BMC biology · 2025Article
- Impact of mouse tracheal basal cell expansion medium on formation and metabolic characteristics of tracheospheres.BMC molecular and cell biology · 2025Article
- A semi-automated algorithm for image analysis of respiratory organoids.PLoS computational biology · 2025Article
- The Use of Organoid Cultures in Advancing Nutrition Research.Advances in nutrition (Bethesda, Md.) · 2025Review
8 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
Organoid cultures are proving to be powerful in vitro models that closely mimic the cellular constituents of their native tissue. Organoids are typically expanded and cultured in a 3D environment using either naturally derived or synthetic extracellular matrices. Assessing the morphology and growth characteristics of these cultures has been difficult due to the many imaging artifacts that accompany the corresponding images. Unlike single cell cultures, there are no reliable automated segmentation techniques that allow for the localization and quantification of organoids in their 3D culture environment. Here we describe OrgaQuant, a deep convolutional neural network implementation that can locate and quantify the size distribution of human intestinal organoids in brightfield images. OrgaQuant is an end-to-end trained neural network that requires no parameter tweaking; thus, it can be fully automated to analyze thousands of images with no user intervention. To develop OrgaQuant, we created a unique dataset of manually annotated human intestinal organoid images with bounding boxes and trained an object detection pipeline using TensorFlow. We have made the dataset, trained model and inference scripts publicly available along with detailed usage instructions.
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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.