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.
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Datasets F1-MSC, F2-GOWT1, F3-SIM, F4-HeLa, DIC1-HeLa, PC1-U373 and PC2-PSC are from the ISBI Cell Tracking Challenge 2015 (ref. 17). Information on how to obtain the data can be found at http://celltrackingchallenge.net/datasets.html, and free registration for the challenge is currently required. Datasets PC3-HKPV, BF1-POL, BF2-PPL and BF3-MiSp are custom and are available from the corresponding author upon reasonable request. Datasets for the detection experiments partially contain unpublished sample-preparation protocols and are currently not freely available. After protocol publication, datasets will be made available on an as-requested basis. Details on sample preparation for our life science experiments can be found in Supplementary Note 3 and the Life Sciences Reporting Summary.
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This work was supported by the German Federal Ministry for Education and Research (BMBF) through the MICROSYSTEMS project (0316185B) to T.F. and A.D.; the Bernstein Award 2012 (01GQ2301) to I.D.; the Federal Ministry for Economic Affairs and Energy (ZF4184101CR5) to A.B.; the Deutsche Forschungsgemeinschaft (DFG) through the collaborative research center KIDGEM (SFB 1140) to D.M., Ö.Ç., T.F. and O.R., and (SFB 746, INST 39/839,840,841) to K.P.; the Clusters of Excellence BIOSS (EXC 294) to T.F., D.M., R.B., A.A., Y.M., D.S., T.L.T., M.P., K.P., M.S., T.B. and O.R.; BrainLinks-Brain-Tools (EXC 1086) to Z.J., K.S., I.D. and T.B.; grants DI 1908/3-1 to J.D., DI 1908/6-1 to Z.J. and K.S., and DI 1908/7-1 to I.D.; the Swiss National Science Foundation (SNF grant 173880) to A.A.; the ERC Starting grant OptoMotorPath (338041) to I.D.; and the FENS-Kavli Network of Excellence (FKNE) to I.D. We thank F. Prósper, E. Bártová, V. Ulman, D. Svoboda, G. van Cappellen, S. Kumar, T. Becker and the Mitocheck consortium for providing a rich diversity of datasets through the ISBI segmentation challenge. We thank P. Fischer for manual image annotations. We thank S. Wrobel for tobacco microspore preparation.