U-Net: deep learning for cell counting, detection, and morphometry

Abstract

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.

Keywords

Plug-inDeep learningComputer scienceSegmentationArtificial intelligenceCloud computingImage segmentationNet (polyhedron)Pattern recognition (psychology)Machine learningOperating system

MeSH Terms

Cell CountCloud ComputingDeep LearningNeural NetworksComputerSoftware Design

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Publication Info

Year: 2018
Type: article
Volume: 16
Issue: 1
Pages: 67-70
Citations: 1903
Access: Closed

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APA Style

                            
                                
                                    Thorsten Falk, 
                                
                                    Dominic Mai, 
                                
                                    Robert Bensch
                                
                                et al.
                            
                            (2018). 
                            U-Net: deep learning for cell counting, detection, and morphometry. 
                            Nature Methods
                            , 16
                            (1)
                            , 67-70.
                            https://doi.org/10.1038/s41592-018-0261-2
                        

Identifiers

DOI: 10.1038/s41592-018-0261-2
PMID: 30559429

Data Quality

Data completeness: 81%