ArticleNature methods2021
DeepImageJ: A user-friendly environment to run deep learning models in ImageJ.
Article in Nature methods, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 130 papers.
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Who cites it
130 citing papers in PubMed.
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- Celldetective, an AI-enhanced image analysis tool for unraveling dynamic cell interactions.eLife · 2026Article
- CODAvision: best practices and a user-friendly interface for rapid, customizable segmentation of medical images.Nature protocols · 2026Review
- WaveUNet+: Preserving Root System Architecture Integrity in In Situ Root Segmentation via a Unified Spectral-Spatial Framework.Plants (Basel, Switzerland) · 2026Article
- SAMJ: fast image annotation on ImageJ/Fiji via segment anything model.Nature communications · 2026Article
- Cell Structure Segmentation in TEM Images of Murine Skin Melanoma Cells by Deep Learning Model.Journal of imaging · 2026Article
- NucVerse3D: generalizable 3D nuclear instance segmentation across heterogeneous microscopy modalities.Scientific reports · 2026Article
- Multidimensional data analysis and classification using SMIAL.Communications biology · 2026Article
- Article
- Systematic evaluation of computational methods for cell segmentation.Briefings in bioinformatics · 2026Article
- Visualizing the multidimensional landscape of biological variation in modern microscopy.Frontiers in bioinformatics · 2026Article
- Self-Driving Microscopes: AI Meets Super-Resolution Microscopy.Small methods · 2026Review
- EMito-Metrix enables automated evaluation of mitochondrial morphology across species.Nature metabolism · 2025Article
- CLEM-Reg: an automated point cloud-based registration algorithm for volume correlative light and electron microscopy.Nature methods · 2025Article
- Visualizing nexus of porous architecture and reactive transport in heterogeneous catalysis by deep learning computer vision and transfer learning.Nature communications · 2025Article
- Standardised TruAI Automated Quantification of Intracellular Neuromelanin Granules in Human Brain Tissue Sections.Neuropathology and applied neurobiology · 2025Article
- BiaPy: accessible deep learning on bioimages.Nature methods · 2025Article
- SegElegans: Instance segmentation using dual convolutional recurrent neural network decoder in Caenorhabditis elegans microscopic images.Computers in biology and medicine · 2025Article
70 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
DeepImageJ is a user-friendly solution that enables the generic use of pre-trained deep learning models for biomedical image analysis in ImageJ. The deepImageJ environment gives access to the largest bioimage repository of pre-trained deep learning models (BioImage Model Zoo). Hence, nonexperts can easily perform common image processing tasks in life-science research with deep learning-based tools including pixel and object classification, instance segmentation, denoising or virtual staining. DeepImageJ is compatible with existing state of the art solutions and it is equipped with utility tools for developers to include new models. Very recently, several training frameworks have adopted the deepImageJ format to deploy their work in one of the most used softwares in the field (ImageJ). Beyond its direct use, we expect deepImageJ to contribute to the broader dissemination and reuse of deep learning models in life sciences applications and bioimage informatics.
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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.