ArticleDevelopment (Cambridge, England)2023
DeXtrusion: automatic recognition of epithelial cell extrusion through machine learning in vivo.
Article in Development (Cambridge, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 21 citations in OpenAlex.
- Epithelial cell extrusion underlies starvation-induced cell loss in a sea anemone.Nature communications · 2026Article
- EpiCure (Epithelial Curation): a versatile and handy tool for curation of epithelial segmentation.bioRxiv : the preprint server for biology · 2026Article
- Cell tracking with accurate error prediction.Nature methods · 2025Article
- Deep learning detection of dynamic exocytosis events in fluorescence TIRF microscopy.PLoS computational biology · 2025Article
- A morphology and secretome map of pyroptosis.Molecular biology of the cell · 2025Article
- Spatial and temporal signatures of cell competition revealed by K-function analysis.Molecular biology of the cell · 2025Article
- Deep learning-based image classification reveals heterogeneous execution of cell death fates during viral infection.Molecular biology of the cell · 2025Article
- Novel imaging and biophysical approaches to study tissue hydraulics in mammalian folliculogenesis.Biophysical reviews · 2024Review
- Deep learning for rapid analysis of cell divisions in vivo during epithelial morphogenesis and repair.eLife · 2024Article
- Deep learning reveals a damage signalling hierarchy that coordinates different cell behaviours driving wound re-epithelialisation.Development (Cambridge, England) · 2024Article
- Mechanical state transitions in the regulation of tissue form and function.Nature reviews. Molecular cell biology · 2024Review
- Live-cell imaging in the deep learning era.Current opinion in cell biology · 2023Review
- DeXtrusion: automatic recognition of epithelial cell extrusion through machine learning in vivo.Development (Cambridge, England) · 2023Article
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
Accurately counting and localising cellular events from movies is an important bottleneck of high-content tissue/embryo live imaging. Here, we propose a new methodology based on deep learning that allows automatic detection of cellular events and their precise xyt localisation on live fluorescent imaging movies without segmentation. We focused on the detection of cell extrusion, the expulsion of dying cells from the epithelial layer, and devised DeXtrusion: a pipeline based on recurrent neural networks for automatic detection of cell extrusion/cell death events in large movies of epithelia marked with cell contour. The pipeline, initially trained on movies of the Drosophila pupal notum marked with fluorescent E-cadherin, is easily trainable, provides fast and accurate extrusion predictions in a large range of imaging conditions, and can also detect other cellular events, such as cell division or cell differentiation. It also performs well on other epithelial tissues with reasonable re-training. Our methodology could easily be applied for other cellular events detected by live fluorescent microscopy and could help to democratise the use of deep learning for automatic event detections in developing tissues.
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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.