ArticlePloS one2020
OrganoidTracker: Efficient cell tracking using machine learning and manual error correction.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.
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Who cites it
46 citing papers in PubMed.
- Organoid Intelligent Morphomics: Decoding the organoid morphome through artificial intelligence from phenotypic quantification to mechanistic insight.Bioactive materials · 2027Review
- Tutorial: interrogating biology using organoid technologies.Nature protocols · 2026Review
- Quantitative imaging of dynamic processes in intestinal organoids.Nature protocols · 2026Review
- OrgLine: A versatile pipeline for organoid morphometry using detector-guided prompts.Cell reports methods · 2026Article
- Single-cell morphodynamics predict cell fate decisions during mucociliary epithelial differentiation.Molecular systems biology · 2026Article
- Image Tracing of Inflammatory Intestinal Organoids via Computational Clearing.Nanomaterials (Basel, Switzerland) · 2026Article
- Article
- 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
- Advances in organoid imaging and automated morphometric analysis: from optical microscopy to computational approaches.Frontiers in cell and developmental biology · 2026Review
- Temporal reassignment and correspondence evaluation with quality control for time-course imaging of 3D cell culture.Cell reports methods · 2025Article
- Immune and Immune-Integrated Organoids as NextGeneration Platforms for Disease Modeling.MedComm · 2025Review
- Single-cell morphodynamics predict cell fate decisions during mucociliary epithelial differentiation.bioRxiv : the preprint server for biology · 2025Article
- Cell tracking with accurate error prediction.Nature methods · 2025Article
- A knowledge-driven deep learning framework for organoid morphological segmentation and characterization.BMC biology · 2025Article
- Epithelial tension controls intestinal cell extrusion.Science (New York, N.Y.) · 2025Article
- Migratory responses in enucleated cells: The forces driving the locomotion movement of unicellular organisms.PNAS nexus · 2025Article
- Intestinal tuft cell subtypes represent successive stages of maturation driven by crypt-villus signaling gradients.Nature communications · 2025Article
- Digitalized organoids: integrated pipeline for high-speed 3D analysis of organoid structures using multilevel segmentation and cellular topology.Nature methods · 2025Article
- Imaging 3D cell cultures with optical microscopy.Nature methods · 2025Review
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Time-lapse microscopy is routinely used to follow cells within organoids, allowing direct study of division and differentiation patterns. There is an increasing interest in cell tracking in organoids, which makes it possible to study their growth and homeostasis at the single-cell level. As tracking these cells by hand is prohibitively time consuming, automation using a computer program is required. Unfortunately, organoids have a high cell density and fast cell movement, which makes automated cell tracking difficult. In this work, a semi-automated cell tracker has been developed. To detect the nuclei, we use a machine learning approach based on a convolutional neural network. To form cell trajectories, we link detections at different time points together using a min-cost flow solver. The tracker raises warnings for situations with likely errors. Rapid changes in nucleus volume and position are reported for manual review, as well as cases where nuclei divide, appear and disappear. When the warning system is adjusted such that virtually error-free lineage trees can be obtained, still less than 2% of all detected nuclei positions are marked for manual analysis. This provides an enormous speed boost over manual cell tracking, while still providing tracking data of the same quality as manual tracking.
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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.