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Microscopy cell counting and detection with fully convolutional regression networks

Abstract:

This paper concerns automated cell counting and detection in microscopy images. The approach we take is to use convolutional neural networks (CNNs) to regress a cell spatial density map across the image. This is applicable to situations where traditional single-cell segmentation-based methods do not work well due to cell clumping or overlaps. We make the following contributions: (i) we develop and compare architectures for two fully convolutional regression networks (FCRNs) for this task; (ii...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1080/21681163.2016.1149104

Authors


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Role:
Author
ORCID:
0000-0003-3804-2639
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Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Oxford college:
St Hilda's College
Role:
Author
ORCID:
0000-0002-3060-3772
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Oxford college:
Brasenose College
Role:
Author
ORCID:
0000-0002-8945-8573
Publisher:
Taylor and Francis Publisher's website
Journal:
Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization Journal website
Volume:
6
Issue:
3
Pages:
283-292
Publication date:
2016-05-02
Acceptance date:
2016-01-28
DOI:
EISSN:
2168-1171
ISSN:
2168-1163
Source identifiers:
667964
Keywords:
Pubs id:
pubs:667964
UUID:
uuid:7cb85c63-ced4-4a1f-ab18-f1e1c90420dd
Local pid:
pubs:667964
Deposit date:
2018-11-21

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