Journal article
Analysis of live cell images: methods, tools and opportunities
- Abstract:
- Advances in optical microscopy, biosensors and cell culturing technologies have transformed live cell imaging. Thanks to these advances live cell imaging plays an increasingly important role in basic biology research as well as at all stages of drug development. Image analysis methods are needed to extract quantitative information from these vast and complex data sets. The aim of this review is to provide an overview of available image analysis methods for live cell imaging, in particular required preprocessing image segmentation, cell tracking and data visualisation methods. The potential opportunities recent advances in machine learning, especially deep learning, and computer vision provide are being discussed. This review includes overview of the different available software packages and toolkits.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
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(Preview, Accepted manuscript, pdf, 4.9MB, Terms of use)
-
- Publisher copy:
- 10.1016/j.ymeth.2017.02.007
Authors
+ Centre for Doctoral Training
in Healthcare Innovation
More from this funder
- Funding agency for:
- Nketia, T
- Grant:
- Wellcome Institutional Strategic Support Fund of the University of Oxford
+ Wellcome Trust
More from this funder
- Funding agency for:
- Nketia, T
- Grant:
- Wellcome Institutional Strategic Support Fund of the University of Oxford
+ Engineering and Physical Sciences Research Council
More from this funder
- Funding agency for:
- Nketia, T
- Sailem, H
- Rittscher, J
- Grant:
- Wellcome Institutional Strategic Support Fund of the University of Oxford
- See- 1070 BiByte Programme Grant (EP /M013774/1
- See- 1070 BiByte Programme Grant (EP /M013774/1
- Publisher:
- Elsevier
- Journal:
- Methods More from this journal
- Volume:
- 115
- Pages:
- 65–79
- Publication date:
- 2017-02-27
- Acceptance date:
- 2017-02-21
- DOI:
- ISSN:
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1046-2023
- Keywords:
- Pubs id:
-
pubs:681670
- UUID:
-
uuid:fcb928f9-37bc-48ad-a10a-ee124fc37c1e
- Local pid:
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pubs:681670
- Source identifiers:
-
681670
- Deposit date:
-
2017-02-24
Terms of use
- Copyright holder:
- Nketia et al
- Copyright date:
- 2017
- Notes:
- Copyright © 2017 Published by Elsevier Inc. This is the accepted manuscript version of the article. The final version is available online from Elsevier at: https://doi.org/10.1016/j.ymeth.2017.02.007
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