Data availability
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 Datasets for the detection experiments partially contain unpublished sample-preparation protocols and are currently not freely available.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.
Change history
References
Sommer, C, Strähle, C, Koethe, U. & Hamprecht, F. A. in Ilastik: interactive learning and segmentation toolkit in IEEE Int. Symp. Biomed. Imaging. 230–233 (IEEE: Piscataway, NJ, USA, 2011).
Arganda-Carreras, I. et al. Bioinformatics 33, 2424–2426 (2017).
Ronneberger, O., Fischer, P. & Brox, T. U-Net: convolutional networks for biomedical image segmentation. in Medical Image Computing and Computer-Assisted Intervention—MICCAI 2015 Vol. 9351, 234–241 (Springer, Cham, Switzerland, 2015).
Rusk, N. Nat. Methods 13, 35 (2016).
Webb, S. Nature 554, 555–557 (2018).
Sadanandan, S. K., Ranefall, P., Le Guyader, S. & Wählby, C. Sci. Rep. 7, 7860 (2017).
Weigert, M. et al. Nat. Methods https://doi.org/10.1038/s41592-018-0216-7 (2018).
Haberl, M. G. et al. Nat. Methods 15, 677–680 (2018).
Ulman, V. et al. Nat. Methods 14, 1141–1152 (2017).
Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. Nat. Methods 9, 671–675 (2012).
Long, J., Shelhamer, E. & Darrell, T. Fully convolutional networks for semantic segmentation. in IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR) 3431–3440 (IEEE, Piscataway, NJ, USA, 2015).
Simonyan, K. & Zisserman, A. Preprint at https://arxiv.org/abs/1409.1556 (2014)
Çiçek, Ö., Abdulkadir, A., Lienkamp, S. S., Brox, T. & Ronneberger, O. 3D U-Net: learning dense volumetric segmentation from sparse annotation. in Medical Image Computing and Computer-Assisted Intervention—MICCAI 2016 Vol. 9901, 424–432 (Springer, Cham, Switzerland, 2016).
Jia, Y. et al. Preprint at https://arxiv.org/abs/1408.5093 (2014).
He, K., Zhang, X., Ren, S. & Sun, J. Preprint at https://arxiv.org/abs/1502.01852 (2015).
Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J. & Zisserman, A. Int. J. Comput. Vis. 88, 303–338 (2010).
Maška, M. et al. Bioinformatics 30, 1609–1617 (2014).
Acknowledgements
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.
Author information
Author notes
Dominic Mai
Present address: SICK AG, Waldkirch, Germany
Robert Bensch
Present address: ANavS GmbH, München, Germany
Alexander Dovzhenko,Olaf Tietz&Sean Walsh
Present address: ScreenSYS GmbH, Freiburg, Germany
Olaf Ronneberger
Present address: DeepMind, London, UK
These authors contributed equally: Thorsten Falk, Dominic Mai, Robert Bensch.
Authors and Affiliations
Department of Computer Science, Albert-Ludwigs-University, Freiburg, Germany
Thorsten Falk,Dominic Mai,Robert Bensch,Özgün Çiçek,Ahmed Abdulkadir,Yassine Marrakchi,Anton Böhm,Thomas Brox&Olaf Ronneberger
BIOSS Centre for Biological Signalling Studies, Freiburg, Germany
Thorsten Falk,Dominic Mai,Robert Bensch,Yassine Marrakchi,Deniz Saltukoglu,Marco Prinz,Klaus Palme,Matias Simons,Thomas Brox&Olaf Ronneberger
CIBSS Centre for Integrative Biological Signalling Studies, Albert-Ludwigs-University, Freiburg, Germany
Thorsten Falk,Yassine Marrakchi,Marco Prinz&Thomas Brox
Life Imaging Center, Center for Biological Systems Analysis, Albert-Ludwigs-University, Freiburg, Germany
Dominic Mai
University Hospital of Old Age Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
Ahmed Abdulkadir
Optophysiology Lab, Institute of Biology III, Albert-Ludwigs-University, Freiburg, Germany
Jan Deubner,Zoe Jäckel,Katharina Seiwald&Ilka Diester
BrainLinks-BrainTools, Albert-Ludwigs-University, Freiburg, Germany
Jan Deubner,Zoe Jäckel,Tuan Leng Tay,Ilka Diester&Thomas Brox
Institute of Biology II, Albert-Ludwigs-University, Freiburg, Germany
Alexander Dovzhenko,Olaf Tietz,Cristina Dal Bosco,Sean Walsh&Klaus Palme
Center for Biological Systems Analysis (ZBSA), Albert-Ludwigs-University, Freiburg, Germany
Deniz Saltukoglu&Matias Simons
Renal Division, University Medical Centre, Freiburg, Germany
Deniz Saltukoglu&Matias Simons
Spemann Graduate School of Biology and Medicine (SGBM), Albert-Ludwigs-University, Freiburg, Germany
Deniz Saltukoglu
Institute of Neuropathology, University Medical Centre, Freiburg, Germany
Tuan Leng Tay&Marco Prinz
Institute of Biology I, Albert-Ludwigs-University, Freiburg, Germany
Tuan Leng Tay
Paris Descartes University-Sorbonne Paris Cité, Imagine Institute, Paris, France
Matias Simons
Bernstein Center Freiburg, Albert-Ludwigs-University, Freiburg, Germany
Ilka Diester
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Contributions
T.F., D.M., R.B., Y.M., Ö.Ç., T.B. and O.R. selected and designed the computational experiments. T.F., R.B., D.M., Y.M., A.B. and Ö.Ç. performed the experiments: R.B., D.M., Y.M. and A.B. (2D), and T.F. and Ö.Ç. (3D). R.B., Ö.Ç., A.A., T.F. and O.R. implemented the U-Net extensions into caffe. T.F. designed and implemented the Fiji plugin. D.S. and M.S. selected, prepared and recorded the keratinocyte dataset PC3-HKPV. T.F. and O.R. prepared the airborne-pollen dataset BF1-POL. A.D., S.W., O.T., C.D.B. and K.P. selected, prepared and recorded the protoplast and microspore datasets BF2-PPL and BF3-MiSp. T.L.T. and M.P. prepared, recorded and annotated the data for the microglial proliferation experiment. J.D., K.S. and Z.J. selected, prepared and recorded the optogenetic dataset. I.D., J.D. and Z.J. manually annotated the optogenetic dataset. I.D., T.F., D.M., R.B., Ö.Ç., T.B. and O.R. wrote the manuscript.
Corresponding author
Correspondence to Olaf Ronneberger.
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Integrated supplementary information
Supplementary Figure 1 The U-Net architecture in the example of a 2D cell segmentation network.
(left) Input: An image tile with 540×540 pixels and C channels (blue box). (right) Output: The K-class soft-max segmentation with 356×356 pixels (yellow box). Blocks show the computed feature hierarchy. Numbers atop each network block: number of feature channels; numbers left to each block: spatial feature map shape in pixels. Yellow arrows: Data flow
Supplementary Figure 2 Separation of touching cells by using pixelwise loss weights.
(a) Generated segmentation mask with one-pixel wide background ridge between touching cells (white: foreground, black: background). (b) Map showing pixel-wise loss weights to enforce the network to separate touching cells
Supplementary Figure 3 Training data augmentation through random smooth elastic deformation.
(a) Upper left: Raw image; Upper right: Labels; Lower Left: Loss Weights; Lower Right: 20μm grid (for illustration purpose only) (b) Deformation field (black arrows) generated using bicubic interpolation from a coarse grid of displacement vectors (blue arrows; magnification: 5×). Vector components are drawn from a Gaussian distribution (σ = 10px). (c) Backwarp-transformed images of (a) using the deformation field
Supplementary information
Supplementary Text and Figures
Supplementary Figures 1–3 and Supplementary Notes 1–3
Supplementary Software 1
Caffe_unet binary package (GPU version with cuDNN, Recommended). Pre-compiled binary version of the caffe_unet backend software for Ubuntu 16.04, cuda 8.0.61 (https://developer.nvidia.com/cuda-80-ga2-download-archive) and cuDNN 7.1.4 for cuda 8.0 (https://developer.nvidia.com/rdp/cudnn-archive). At time of publication cuDNN download from the nVidia website requires free registration as nVidia developer
Supplementary Software 2
Caffe_unet binary package (GPU version no cuDNN). Pre-compiled binary version of the caffe_unet backend software for Ubuntu 16.04 and cuda 8.0.61 (https://developer.nvidia.com/cuda-80-ga2-downloadarchive)
Supplementary Software 3
Caffe_unet binary package (CPU version). Pre-compiled binary version of the caffe_unet backend software for Ubuntu 16.04
Supplementary Software 4
The source code difference (patch file) to the open source caffe deep learning software (https://github.com/BVLC/caffe.git commit hash d1208dbf313698de9ef70b3362c89cfddb51c520). Checkout the correspondingly tagged commit and apply the patch using “git apply” to get the full source for custom builds
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Falk, T., Mai, D., Bensch, R. et al. U-Net: deep learning for cell counting, detection, and morphometry. Nat Methods 16, 67–70 (2019). https://doi.org/10.1038/s41592-018-0261-2
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DOI: https://doi.org/10.1038/s41592-018-0261-2