ArticleeLife2022
Tracking cell lineages in 3D by incremental deep learning.
Article in eLife, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
39 citing papers in PubMed.
- Quantitative imaging of dynamic processes in intestinal organoids.Nature protocols · 2026Review
- Advances in organoid imaging and automated morphometric analysis: from optical microscopy to computational approaches.Frontiers in cell and developmental biology · 2026Review
- Ultrack: pushing the limits of cell tracking across biological scales.Nature methods · 2025Article
- CELLECT: contrastive embedding learning for large-scale efficient cell tracking.Nature methods · 2025Article
- Cell tracking with accurate error prediction.Nature methods · 2025Article
- Rapid canalization of chromosome conformation-transcription fingerprints during embryogenesis revealed by fully-automated cell identity decoding with CeSCALE.bioRxiv : the preprint server for biology · 2025Article
- Uncertainty-Gated Min-Cost Flows forbioRxiv : the preprint server for biology · 2025Article
- A model for boundary-driven tissue morphogenesis.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Long-term live imaging, cell identification and cell tracking in regenerating crustacean legs.eLife · 2025Article
- Early coordination of cell migration and cardiac fate determination during mammalian gastrulation.The EMBO journal · 2025Article
- MSP-tracker: A versatile vesicle tracking software tool used to reveal the spatial control of polarized secretion in Drosophila epithelial cells.PLoS biology · 2025Article
- Article
- Article
- Lineage Tracing by Light-Sheet Microscopy and Computational Reconstruction.Methods in molecular biology (Clifton, N.J.) · 2025Article
- AI-based hardware and software tools in microscopy to boost research in immunology and virology.Frontiers in immunology · 2025Review
- Computational Methods for Image Analysis in Craniofacial Development and Disease.Journal of dental research · 2024Review
- Cellular behavior analysis from live-cell imaging of TCR T cell-cancer cell interactions.bioRxiv : the preprint server for biology · 2024Article
- Nuclear instance segmentation and tracking for preimplantation mouse embryos.Development (Cambridge, England) · 2024Article
- Article
- CellTracksColab is a platform that enables compilation, analysis, and exploration of cell tracking data.PLoS biology · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep learning is emerging as a powerful approach for bioimage analysis. Its use in cell tracking is limited by the scarcity of annotated data for the training of deep-learning models. Moreover, annotation, training, prediction, and proofreading currently lack a unified user interface. We present ELEPHANT, an interactive platform for 3D cell tracking that addresses these challenges by taking an incremental approach to deep learning. ELEPHANT provides an interface that seamlessly integrates cell track annotation, deep learning, prediction, and proofreading. This enables users to implement cycles of incremental learning starting from a few annotated nuclei. Successive prediction-validation cycles enrich the training data, leading to rapid improvements in tracking performance. We test the software's performance against state-of-the-art methods and track lineages spanning the entire course of leg regeneration in a crustacean over 1 week (504 timepoints). ELEPHANT yields accurate, fully-validated cell lineages with a modest investment in time and effort.
Indexed as
Identifiers
What OpenQuestion holds
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.