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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
Version:
Accepted Manuscript

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

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Department:
Hertford College
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Department:
Oxford, MSD, Biochemistry
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Department:
Oxford, MSD, Physiology Anatomy and Genetics
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Department:
Lincoln College
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Department:
Oxford, MSD, Biochemistry
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Funding agency for:
Hailstone, M
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Publisher:
Springer, Cham Publisher's website
Volume:
9913
Pages:
244-255
Series:
Lecture Notes in Computer Science
Publication date:
2016
Acceptance date:
2016-07-08
DOI:
ISSN:
0302-9743
Pubs id:
pubs:636515
URN:
uri:dc18f849-635d-4ce1-9bf0-18b2f959846d
UUID:
dc18f849-635d-4ce1-9bf0-18b2f959846d
Local pid:
pubs:636515
ISBN:
978-3-319-46603-3

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