DeepCell Kiosk: scaling deep learning-enabled cellular image analysis with Kubernetes.
Journal
Nature methods
ISSN: 1548-7105
Titre abrégé: Nat Methods
Pays: United States
ID NLM: 101215604
Informations de publication
Date de publication:
01 2021
01 2021
Historique:
received:
15
10
2019
accepted:
23
11
2020
pubmed:
6
1
2021
medline:
9
3
2021
entrez:
5
1
2021
Statut:
ppublish
Résumé
Deep learning is transforming the analysis of biological images, but applying these models to large datasets remains challenging. Here we describe the DeepCell Kiosk, cloud-native software that dynamically scales deep learning workflows to accommodate large imaging datasets. To demonstrate the scalability and affordability of this software, we identified cell nuclei in 10
Identifiants
pubmed: 33398191
doi: 10.1038/s41592-020-01023-0
pii: 10.1038/s41592-020-01023-0
pmc: PMC8759612
mid: NIHMS1744528
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
43-45Subventions
Organisme : NCI NIH HHS
ID : U24 CA224309
Pays : United States
Commentaires et corrections
Type : ErratumIn
Références
Ouyang, W. et al. Analysis of the Human Protein Atlas Image Classification competition. Nat. Methods 16, 1254–1261 (2019).
doi: 10.1038/s41592-019-0658-6
Falk, T. et al. U-Net: deep learning for cell counting, detection, and morphometry. Nat. Methods 16, 67–70 (2019).
doi: 10.1038/s41592-018-0261-2
Van Valen, D. A. et al. Deep learning automates the quantitative analysis of individual cells in live-cell imaging experiments. PLoS Comput. Biol. 12, e1005177 (2016).
doi: 10.1371/journal.pcbi.1005177
Schmidt, U., Weigert, M., Broaddus, C. & Myers, G. Cell detection with star-convex polygons. In Medical Image Computing and Computer Assisted Intervention—MICCAI 2018 (eds. Frangi, A. F. et al.) 265–273 (Springer International Publishing, 2018).
Moen, E. et al. Accurate cell tracking and lineage construction in live-cell imaging experiments with deep learning. Preprint at bioRxiv https://doi.org/10.1101/803205 (2019).
Anjum, S. & Gurari, D. CTMC: Cell Tracking with Mitosis Detection dataset challenge. in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition (CVPR) Workshops 982–983 (2020).
Gómez-de-Mariscal, E. et al. DeepImageJ: a user-friendly plugin to run deep learning models in ImageJ. Preprint at bioRxiv https://doi.org/10.1101/799270 (2019).
Ouyang, W., Mueller, F., Hjelmare, M., Lundberg, E. & Zimmer, C. ImJoy: an open-source computational platform for the deep learning era. Nat. Methods 16, 1199–1200 (2019).
doi: 10.1038/s41592-019-0627-0
von Chamier, L. et al. ZeroCostDL4Mic: an open platform to simplify access and use of deep-learning in microscopy. Preprint at bioRxiv https://doi.org/10.1101/2020.03.20.000133 (2020).
McQuin, C. et al. CellProfiler 3.0: next-generation image processing for biology. PLoS Biol. 16, e2005970 (2018).
doi: 10.1371/journal.pbio.2005970
Haberl, M. G. et al. CDeep3M—Plug-and-Play cloud-based deep learning for image segmentation. Nat. Methods 15, 677–680 (2018).
doi: 10.1038/s41592-018-0106-z
Emami Khoonsari, P. et al. Interoperable and scalable data analysis with microservices: applications in metabolomics. Bioinformatics 35, 3752–3760 (2019).
doi: 10.1093/bioinformatics/btz160
Novella, J. A. et al. Container-based bioinformatics with Pachyderm. Bioinformatics 35, 839–846 (2019).
doi: 10.1093/bioinformatics/bty699
Capuccini, M. et al. On-demand virtual research environments using microservices. PeerJ Comput. Sci. 5, e232 (2019).
doi: 10.7717/peerj-cs.232
Peters, K. et al. PhenoMeNal: processing and analysis of metabolomics data in the cloud. GigaScience 8, giy149 (2019).
Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).
doi: 10.1038/nmeth.2019
Sofroniew, N. et al. napari/napari: 0.3.5. Zenodo https://doi.org/10.5281/zenodo.3900158 (2020).
Altschul, S. F. et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 25, 3389–3402 (1997).
doi: 10.1093/nar/25.17.3389
Keren, L. et al. A structured tumor-immune microenvironment in triple negative breast cancer revealed by multiplexed ion beam imaging. Cell 174, 1373–1387 (2018).
doi: 10.1016/j.cell.2018.08.039