DeepImageJ: A user-friendly environment to run deep learning models in ImageJ
Published 2021 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
DeepImageJ: A user-friendly environment to run deep learning models in ImageJ
Authors
Keywords
-
Journal
NATURE METHODS
Volume 18, Issue 10, Pages 1192-1195
Publisher
Springer Science and Business Media LLC
Online
2021-10-01
DOI
10.1038/s41592-021-01262-9
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Quantitative digital microscopy with deep learning
- (2021) Benjamin Midtvedt et al. Applied Physics Reviews
- Open-source deep-learning software for bioimage segmentation
- (2021) Alice M. Lucas et al. MOLECULAR BIOLOGY OF THE CELL
- Democratising deep learning for microscopy with ZeroCostDL4Mic
- (2021) Lucas von Chamier et al. Nature Communications
- Cryo-electron Tomography Workflows for Quantitative Analysis of Actin Networks Involved in Cell Migration
- (2020) Florian Fäßler et al. MICROSCOPY AND MICROANALYSIS
- A Bird’s-Eye View of Deep Learning in Bioimage Analysis
- (2020) Erik Meijering Computational and Structural Biotechnology Journal
- The ImageJ ecosystem: Open‐source software for image visualization, processing, and analysis
- (2020) Alexandra B. Schroeder et al. PROTEIN SCIENCE
- Virtual histological staining of unlabelled tissue-autofluorescence images via deep learning
- (2019) Yair Rivenson et al. Nature Biomedical Engineering
- DeepClas4Bio: Connecting bioimaging tools with deep learning frameworks for image classification
- (2019) A. Inés et al. COMPUTERS IN BIOLOGY AND MEDICINE
- Deep learning for cellular image analysis
- (2019) Erick Moen et al. NATURE METHODS
- ilastik: interactive machine learning for (bio)image analysis
- (2019) Stuart Berg et al. NATURE METHODS
- ImJoy: an open-source computational platform for the deep learning era
- (2019) Wei Ouyang et al. NATURE METHODS
- Nucleus segmentation across imaging experiments: the 2018 Data Science Bowl
- (2019) Juan C. Caicedo et al. NATURE METHODS
- Deep-Learning-Based Segmentation of Small Extracellular Vesicles in Transmission Electron Microscopy Images
- (2019) Estibaliz Gómez-de-Mariscal et al. Scientific Reports
- Deep-STORM: super-resolution single-molecule microscopy by deep learning
- (2018) Elias Nehme et al. Optica
- Content-aware image restoration: pushing the limits of fluorescence microscopy
- (2018) Martin Weigert et al. NATURE METHODS
- U-Net: deep learning for cell counting, detection, and morphometry
- (2018) Thorsten Falk et al. NATURE METHODS
- Fiji: an open-source platform for biological-image analysis
- (2012) Johannes Schindelin et al. NATURE METHODS
- NIH Image to ImageJ: 25 years of image analysis
- (2012) Caroline A Schneider et al. NATURE METHODS
Find the ideal target journal for your manuscript
Explore over 38,000 international journals covering a vast array of academic fields.
SearchAsk a Question. Answer a Question.
Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.
Get Started