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3-D density kernel estimation for counting in microscopy image volumes using 3-D image filters and random decision trees

Abstract:

We describe a means through which cells can be accurately counted in 3-D microscopy image data, using only weakly annotated images as input training material. We update an existing 2-D density kernel estimation approach into 3-D and we introduce novel 3-D features which encapsulate the 3-D neighbourhood surrounding each voxel. The proposed 3-D density kernel estimation (DKE-3-D) method, which utilises an ensemble of random decision trees, is computationally efficient and achieves state-of-the...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1007/978-3-319-46604-0_18

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Institution:
University of Oxford
Oxford college:
Hertford College
Role:
Author
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Institution:
University of Oxford
Division:
MSD
Department:
Biochemistry
Role:
Author
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Institution:
University of Oxford
Division:
MSD
Department:
Physiology Anatomy & Genetics
Role:
Author
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Institution:
University of Oxford
Oxford college:
Lincoln College
Role:
Author
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Institution:
University of Oxford
Division:
MSD
Department:
Biochemistry
Role:
Author
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Funding agency for:
Davis, I
Grant:
Senior Research Fellowship (081858
104924/14/Z/14
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Funding agency for:
Yang, L
Grant:
Scholarship in Humanities
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Funding agency for:
Yang, L
Grant:
Scholarship in Humanities
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Funding agency for:
Lalwani, M
Patient, R
Grant:
RM/13/3/30159
RM/13/3/30159
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Funding agency for:
Waithe, D
Hailstone, M
Grant:
EP/L016052/1
EP/L016052/1
Publisher:
Springer, Cham Publisher's website
Host title:
European Conference on Computer Vision: ECCV 2016: Computer Vision – ECCV 2016 Workshops
Series:
Lecture Notes in Computer Science
Volume:
9913
Pages:
244-255
Publication date:
2016-01-01
Acceptance date:
2016-07-08
DOI:
ISSN:
0302-9743
ISBN:
9783319466033
Keywords:
Pubs id:
pubs:636515
UUID:
uuid:dc18f849-635d-4ce1-9bf0-18b2f959846d
Local pid:
pubs:636515
Source identifiers:
636515
Deposit date:
2016-08-08

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