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Microscopy cell counting with fully convolutional regression networks

Abstract:

This paper concerns automated cell counting in microscopy images. The approach we take is to adapt 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 overlap. We make the following contributions: (i) we develop and compare architectures for two Fully Convolutional Regression Networks (FCRNs) for this task; (ii) since the n...

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Publication status:
Not published
Peer review status:
Peer reviewed

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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Oxford college:
Brasenose College
Role:
Author
Publisher:
TUM Technische Universität München Publisher's website
Host title:
18th International Conference on Medical Image Computing and Computer Analysis Interventions (MICCAI 2015)
Publication date:
2015-01-01
Acceptance date:
2015-02-02
Source identifiers:
678982
Pubs id:
pubs:678982
UUID:
uuid:3ffa263f-48d7-4479-8a11-8d0edc07793c
Local pid:
pubs:678982
Deposit date:
2017-02-09

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