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CytoCensus, mapping cell identity and division in tissues and organs using machine learning.

Abstract:

A major challenge in cell and developmental biology is the automated identification and quantitation of cells in complex multilayered tissues. We developed CytoCensus: an easily deployed implementation of supervised machine learning that extends convenient 2D ‘point-and-click’ user training to 3D detection of cells in challenging datasets with ill-defined cell boundaries. In tests on such datasets, CytoCensus outperforms other freely available image analysis software in accuracy and speed of ...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.7554/elife.51085

Authors


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Institution:
University of Oxford
Division:
MSD
Department:
Biochemistry
Oxford college:
Linacre College
Role:
Author
ORCID:
0000-0001-9326-3827
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Institution:
University of Oxford
Division:
MSD
Department:
RDM
Sub department:
RDM Clinical Laboratory Sciences
Oxford college:
Hertford College
Role:
Author
ORCID:
0000-0003-2685-4226
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Role:
Author
ORCID:
0000-0003-4670-1139
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Engineering and Physical Sciences Research Council More from this funder
Publisher:
eLife Sciences Publications, Ltd Publisher's website
Journal:
eLife Journal website
Volume:
9
Article number:
e51085
Place of publication:
England
Publication date:
2020-05-19
Acceptance date:
2020-03-17
DOI:
EISSN:
2050-084X
Pmid:
32423529
Language:
English
Keywords:
Pubs id:
1105963
Local pid:
pubs:1105963
Deposit date:
2020-06-03

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