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Deep learning for cellular image analysis

Moen, Erick and Bannon, Dylan and Kudo, Takamasa and Graf, William and Covert, Markus and Van Valen, David (2019) Deep learning for cellular image analysis. Nature Methods, 16 (12). pp. 1233-1246. ISSN 1548-7091. https://resolver.caltech.edu/CaltechAUTHORS:20190528-110726386

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Abstract

Recent advances in computer vision and machine learning underpin a collection of algorithms with an impressive ability to decipher the content of images. These deep learning algorithms are being applied to biological images and are transforming the analysis and interpretation of imaging data. These advances are positioned to render difficult analyses routine and to enable researchers to carry out new, previously impossible experiments. Here we review the intersection between deep learning and cellular image analysis and provide an overview of both the mathematical mechanics and the programming frameworks of deep learning that are pertinent to life scientists. We survey the field’s progress in four key applications: image classification, image segmentation, object tracking, and augmented microscopy. Last, we relay our labs’ experience with three key aspects of implementing deep learning in the laboratory: annotating training data, selecting and training a range of neural network architectures, and deploying solutions. We also highlight existing datasets and implementations for each surveyed application.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1038/s41592-019-0403-1DOIArticle
https://rdcu.be/bEG8BPublisherFree ReadCube access
https://www.nature.com/articles/s41592-019-0403-1/tables/2PublisherTable 2 - Available datasets
ORCID:
AuthorORCID
Moen, Erick0000-0002-5947-7044
Kudo, Takamasa0000-0002-9709-5549
Covert, Markus0000-0002-5993-8912
Van Valen, David0000-0001-7534-7621
Additional Information:© 2019 Springer Nature Publishing AG. Received 24 October 2018; Accepted 03 April 2019; Published 27 May 2019. Data availability: Links to the data referred to in this Review can be found in Table 2. We thank A. Anandkumar, M. Angelo, L. Cai, S. Cooper, M. Elowitz, K.C. Huang, G. Johnson, A. Karpathy, L. Keren, A. Raj, T. Vora, and R. Wollman for helpful discussions and comments. This work was supported by several funding sources, including the Allen Discovery Center (award supporting W.G.; award supporting T.K., M.C., and D.V.V.), the Burroughs Wellcome Fund Postdoctoral Enrichment Program, a Figure Eight AI for Everyone award, and the NIH (subaward U24CA224309-01 to D.V.V.). Author Contributions: E.M., D.B., T.K., W.G., M.C., and D.V.V. wrote the paper. The authors declare no competing interests.
Funders:
Funding AgencyGrant Number
Allen Discovery CenterUNSPECIFIED
Burroughs Wellcome FundUNSPECIFIED
NIHU24CA224309-01
Subject Keywords:Image processing; Software
Issue or Number:12
Record Number:CaltechAUTHORS:20190528-110726386
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20190528-110726386
Official Citation:Moen, E., Bannon, D., Kudo, T. et al. Deep learning for cellular image analysis. Nat Methods 16, 1233–1246 (2019) doi:10.1038/s41592-019-0403-1
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:95814
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:28 May 2019 20:11
Last Modified:04 Dec 2019 15:45

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