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

doi:10.1038/s41592-018-0261-2

. 2019 Jan;16(1):67-70.

doi: 10.1038/s41592-018-0261-2. Epub 2018 Dec 17.

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

Thorsten Falk^#^{1

2

3}, Dominic Mai^#^{1

2

4

5}, Robert Bensch^#^{1

2

6}, Özgün Çiçek¹, Ahmed Abdulkadir^{1

7}, Yassine Marrakchi^{1

2

3}, Anton Böhm¹, Jan Deubner^{8

9}, Zoe Jäckel^{8

9}, Katharina Seiwald⁸, Alexander Dovzhenko^{10

11}, Olaf Tietz^{10

11}, Cristina Dal Bosco¹⁰, Sean Walsh^{10

11}, Deniz Saltukoglu^{2

12

13

14}, Tuan Leng Tay^{9

15

16}, Marco Prinz^{2

3

15}, Klaus Palme^{2

10}, Matias Simons^{2

12

13

17}, Ilka Diester^{8

9

18}, Thomas Brox^{1

2

3

9}, Olaf Ronneberger^{19

20

21}

Affiliations

¹ Department of Computer Science, Albert-Ludwigs-University, Freiburg, Germany.
² BIOSS Centre for Biological Signalling Studies, Freiburg, Germany.
³ CIBSS Centre for Integrative Biological Signalling Studies, Albert-Ludwigs-University, Freiburg, Germany.
⁴ Life Imaging Center, Center for Biological Systems Analysis, Albert-Ludwigs-University, Freiburg, Germany.
⁵ SICK AG, Waldkirch, Germany.
⁶ ANavS GmbH, München, Germany.
⁷ University Hospital of Old Age Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland.
⁸ Optophysiology Lab, Institute of Biology III, Albert-Ludwigs-University, Freiburg, Germany.
⁹ BrainLinks-BrainTools, Albert-Ludwigs-University, Freiburg, Germany.
¹⁰ Institute of Biology II, Albert-Ludwigs-University, Freiburg, Germany.
¹¹ ScreenSYS GmbH, Freiburg, Germany.
¹² Center for Biological Systems Analysis (ZBSA), Albert-Ludwigs-University, Freiburg, Germany.
¹³ Renal Division, University Medical Centre, Freiburg, Germany.
¹⁴ Spemann Graduate School of Biology and Medicine (SGBM), Albert-Ludwigs-University, Freiburg, Germany.
¹⁵ Institute of Neuropathology, University Medical Centre, Freiburg, Germany.
¹⁶ Institute of Biology I, Albert-Ludwigs-University, Freiburg, Germany.
¹⁷ Paris Descartes University-Sorbonne Paris Cité, Imagine Institute, Paris, France.
¹⁸ Bernstein Center Freiburg, Albert-Ludwigs-University, Freiburg, Germany.
¹⁹ Department of Computer Science, Albert-Ludwigs-University, Freiburg, Germany. ronneber@informatik.uni-freiburg.de.
²⁰ BIOSS Centre for Biological Signalling Studies, Freiburg, Germany. ronneber@informatik.uni-freiburg.de.
²¹ DeepMind, London, UK. ronneber@informatik.uni-freiburg.de.

^# Contributed equally.

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

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

Thorsten Falk et al. Nat Methods. 2019 Jan.

. 2019 Jan;16(1):67-70.

doi: 10.1038/s41592-018-0261-2. Epub 2018 Dec 17.

Authors

Affiliations

¹ Department of Computer Science, Albert-Ludwigs-University, Freiburg, Germany.
² BIOSS Centre for Biological Signalling Studies, Freiburg, Germany.
³ CIBSS Centre for Integrative Biological Signalling Studies, Albert-Ludwigs-University, Freiburg, Germany.
⁴ Life Imaging Center, Center for Biological Systems Analysis, Albert-Ludwigs-University, Freiburg, Germany.
⁵ SICK AG, Waldkirch, Germany.
⁶ ANavS GmbH, München, Germany.
⁷ University Hospital of Old Age Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland.
⁸ Optophysiology Lab, Institute of Biology III, Albert-Ludwigs-University, Freiburg, Germany.
⁹ BrainLinks-BrainTools, Albert-Ludwigs-University, Freiburg, Germany.
¹⁰ Institute of Biology II, Albert-Ludwigs-University, Freiburg, Germany.
¹¹ ScreenSYS GmbH, Freiburg, Germany.
¹² Center for Biological Systems Analysis (ZBSA), Albert-Ludwigs-University, Freiburg, Germany.
¹³ Renal Division, University Medical Centre, Freiburg, Germany.
¹⁴ Spemann Graduate School of Biology and Medicine (SGBM), Albert-Ludwigs-University, Freiburg, Germany.
¹⁵ Institute of Neuropathology, University Medical Centre, Freiburg, Germany.
¹⁶ Institute of Biology I, Albert-Ludwigs-University, Freiburg, Germany.
¹⁷ Paris Descartes University-Sorbonne Paris Cité, Imagine Institute, Paris, France.
¹⁸ Bernstein Center Freiburg, Albert-Ludwigs-University, Freiburg, Germany.
¹⁹ Department of Computer Science, Albert-Ludwigs-University, Freiburg, Germany. ronneber@informatik.uni-freiburg.de.
²⁰ BIOSS Centre for Biological Signalling Studies, Freiburg, Germany. ronneber@informatik.uni-freiburg.de.
²¹ DeepMind, London, UK. ronneber@informatik.uni-freiburg.de.

^# Contributed equally.

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

Erratum in

Author Correction: U-Net: deep learning for cell counting, detection, and morphometry.
Falk T, Mai D, Bensch R, Çiçek Ö, Abdulkadir A, Marrakchi Y, Böhm A, Deubner J, Jäckel Z, Seiwald K, Dovzhenko A, Tietz O, Bosco CD, Walsh S, Saltukoglu D, Tay TL, Prinz M, Palme K, Simons M, Diester I, Brox T, Ronneberger O. Falk T, et al. Nat Methods. 2019 Apr;16(4):351. doi: 10.1038/s41592-019-0356-4. Nat Methods. 2019. PMID: 30804552

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.

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References

1. 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).
1. Arganda-Carreras, I. et al. Bioinformatics 33, 2424–2426 (2017). - DOI
1. 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).
1. Rusk, N. Nat. Methods 13, 35 (2016). - DOI
1. Webb, S. Nature 554, 555–557 (2018). - DOI

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[1] 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).

[2] 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).

[3] Arganda-Carreras, I. et al. Bioinformatics 33, 2424–2426 (2017). - DOI

[4] Arganda-Carreras, I. et al. Bioinformatics 33, 2424–2426 (2017). - DOI

[5] 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).

[6] 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).

[7] Rusk, N. Nat. Methods 13, 35 (2016). - DOI

[8] Rusk, N. Nat. Methods 13, 35 (2016). - DOI

[9] Webb, S. Nature 554, 555–557 (2018). - DOI

[10] Webb, S. Nature 554, 555–557 (2018). - DOI

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

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

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