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Automated and accurate segmentation of leaf venation networks via deep learning

Abstract:

Leaf vein network geometry can predict levels of resource transport, defence, and mechanical support that operate at different spatial scales. However, it is challenging to quantify network architecture across scales, due to the difficulties both in segmenting networks from images, and in extracting multi‐scale statistics from subsequent network graph representations. Here we develop deep learning algorithms using convolutional neural networks (CNNs) to automatically segment leaf vein network...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1101/2020.07.19.206631

Authors


More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
SSD
Department:
SOGE
Sub department:
Environmental Change Institute
Role:
Author
ORCID:
0000-0002-5061-2385
More by this author
Institution:
University of Oxford
Division:
SSD
Department:
SOGE
Sub department:
Environmental Change Institute
Role:
Author
More by this author
Institution:
University of Oxford
Division:
SSD
Department:
SOGE
Sub department:
Geography
Role:
Author
ORCID:
0000-0002-3503-4783
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Plant Sciences
Role:
Author
ORCID:
0000-0002-8942-6897
Publisher:
Wiley Publisher's website
Journal:
New Phytologist Journal website
Volume:
229
Issue:
1
Pages:
631-648
Publication date:
2020-10-10
Acceptance date:
2020-09-10
DOI:
EISSN:
1469-8137
ISSN:
0028-646X

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