U-Net: deep learning for cell counting, detection, and morphometry.
Journal
Nature methods
ISSN: 1548-7105
Titre abrégé: Nat Methods
Pays: United States
ID NLM: 101215604
Informations de publication
Date de publication:
01 2019
01 2019
Historique:
received:
26
07
2018
accepted:
19
11
2018
pubmed:
19
12
2018
medline:
25
6
2019
entrez:
19
12
2018
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 30559429
doi: 10.1038/s41592-018-0261-2
pii: 10.1038/s41592-018-0261-2
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
67-70Commentaires et corrections
Type : ErratumIn
Références
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).
doi: 10.1093/bioinformatics/btx180
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).
doi: 10.1038/nmeth.3707
Webb, S. Nature 554, 555–557 (2018).
doi: 10.1038/d41586-018-02174-z
Sadanandan, S. K., Ranefall, P., Le Guyader, S. & Wählby, C. Sci. Rep. 7, 7860 (2017).
doi: 10.1038/s41598-017-07599-6
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).
doi: 10.1038/s41592-018-0106-z
Ulman, V. et al. Nat. Methods 14, 1141–1152 (2017).
doi: 10.1038/nmeth.4473
Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. Nat. Methods 9, 671–675 (2012).
doi: 10.1038/nmeth.2089
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).
doi: 10.1007/s11263-009-0275-4
Maška, M. et al. Bioinformatics 30, 1609–1617 (2014).
doi: 10.1093/bioinformatics/btu080