Journal article
Microscopy cell counting and detection with fully convolutional regression networks
- Abstract:
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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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- Files:
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(Accepted manuscript, pdf, 4.2MB)
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- Publisher copy:
- 10.1080/21681163.2016.1149104
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Authors
Funding
Bibliographic Details
- 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:
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2168-1171
- ISSN:
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2168-1163
- Source identifiers:
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667964
Item Description
- Keywords:
- Pubs id:
-
pubs:667964
- UUID:
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uuid:7cb85c63-ced4-4a1f-ab18-f1e1c90420dd
- Local pid:
- pubs:667964
- Deposit date:
- 2018-11-21
Terms of use
- Copyright holder:
- Informa UK Limited
- Copyright date:
- 2016
- Notes:
- © 2016 Informa UK Limited, trading as Taylor and Francis Group. This is the accepted manuscript version of the article. The final version is available online from Taylor and Francis at: https://doi.org/10.1080/21681163.2016.1149104
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