Journal
NATURE METHODS
Volume 16, Issue 1, Pages 67-+Publisher
NATURE RESEARCH
DOI: 10.1038/s41592-018-0261-2
Keywords
-
Categories
Funding
- German Federal Ministry for Education and Research (BMBF) through the MICROSYSTEMS project [0316185B]
- Federal Ministry for Economic Affairs and Energy [ZF4184101CR5]
- Deutsche Forschungsgemeinschaft (DFG) through the collaborative research center KIDGEM [SFB 1140, SFB 746, INST 39/839, INST 39/840, INST 39/841]
- Bernstein Award 2012 [01GQ2301]
- Clusters of Excellence BIOSS [EXC 294]
- BrainLinks-Brain-Tools [EXC 1086]
- Swiss National Science Foundation (SNF) [173880]
- ERC Starting grant OptoMotorPath [338041]
- FENS-Kavli Network of Excellence (FKNE)
- [DI 1908/3-1]
- [DI 1908/6-1]
- [DI 1908/7-1]
Ask authors/readers for more resources
U-Net is a generic deep-learning solution for frequently occurring quantification tasks such as cell detection and shape measurements in biomedical image data. We present an ImageJ plugin that enables non-machine-learning experts to analyze their data with U-Net on either a local computer or a remote server/cloud service. The plugin comes with pretrained models for single-cell segmentation and allows for U-Net to be adapted to new tasks on the basis of a few annotated samples.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available