ReviewDevelopment (Cambridge, England)2021
Deep learning for bioimage analysis in developmental biology.
Review in Development (Cambridge, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 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
36 citing papers in PubMed.
- DySTrack - a modular smart microscopy tool for live tracking of dynamic samples on modern commercial microscopes.Journal of cell science · 2026Article
- Deep learning-supported image quantification of epithelial cell shapes and its application to polycystic kidney disease.PLoS computational biology · 2026Article
- Review of Progress of AI in Biomimetics: From Biological Patterns to Closed-Loop Discovery.Biomimetics (Basel, Switzerland) · 2026Review
- Unsupervised Variational-Autoencoder-Based Analysis of Morphological Representations in Magnetic-Nanoparticle-Treated Macrophages.Bioengineering (Basel, Switzerland) · 2026Article
- Fifty high-content light sheet fluorescence microscopy datasets of Tribolium castaneum embryogenesis.Scientific data · 2025Article
- OoCount: a machine-learning-based approach to mouse ovarian follicle counting and classification†.Biology of reproduction · 2025Article
- MIFA: Metadata, Incentives, Formats and Accessibility guidelines to improve the reuse of AI datasets for bioimage analysis.Nature methods · 2025Review
- SNAP-Tech 2.0.Nature chemical biology · 2025Article
- Review
- bia-binder: a web-native cloud compute service for the bioimage analysis community.Bioinformatics (Oxford, England) · 2025Article
- Deep learning models link local cellular features with whole-animal growth dynamics in zebrafish.Life science alliance · 2025Article
- Deep learning-based high-resolution time inference for deciphering dynamic gene regulation from fixed embryos.Nature communications · 2025Article
- Quantifying the relationship between cell proliferation and morphology during development of the face.Development (Cambridge, England) · 2025Article
- Machine learning approaches for image classification in developmental biology and clinical embryology.Development (Cambridge, England) · 2025Review
- 3D visualization of uterus and ovary: tissue clearing techniques and biomedical applications.Frontiers in bioengineering and biotechnology · 2025Review
- Nuclear instance segmentation and tracking for preimplantation mouse embryos.Development (Cambridge, England) · 2024Article
- Novel imaging and biophysical approaches to study tissue hydraulics in mammalian folliculogenesis.Biophysical reviews · 2024Review
- Deep 3D histology powered by tissue clearing, omics and AI.Nature methods · 2024Review
- Dev-ResNet: automated developmental event detection using deep learning.The Journal of experimental biology · 2024Article
- Modular segmentation, spatial analysis and visualization of volume electron microscopy datasets.Nature protocols · 2024Review
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.
Funding
Abstract
Deep learning has transformed the way large and complex image datasets can be processed, reshaping what is possible in bioimage analysis. As the complexity and size of bioimage data continues to grow, this new analysis paradigm is becoming increasingly ubiquitous. In this Review, we begin by introducing the concepts needed for beginners to understand deep learning. We then review how deep learning has impacted bioimage analysis and explore the open-source resources available to integrate it into a research project. Finally, we discuss the future of deep learning applied to cell and developmental biology. We analyze how state-of-the-art methodologies have the potential to transform our understanding of biological systems through new image-based analysis and modelling that integrate multimodal inputs in space and time.
Indexed as
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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.