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TopoRoot: a method for computing hierarchy and fine-grained traits of maize roots from 3D imaging

BACKGROUND: 3D imaging, such as X-ray CT and MRI, has been widely deployed to study plant root structures. Many computational tools exist to extract coarse-grained features from 3D root images, such as total volume, root number and total root length. However, methods that can accurately and efficien...

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Autores principales: Zeng, Dan, Li, Mao, Jiang, Ni, Ju, Yiwen, Schreiber, Hannah, Chambers, Erin, Letscher, David, Ju, Tao, Topp, Christopher N.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8667396/
https://www.ncbi.nlm.nih.gov/pubmed/34903248
http://dx.doi.org/10.1186/s13007-021-00829-z
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author Zeng, Dan
Li, Mao
Jiang, Ni
Ju, Yiwen
Schreiber, Hannah
Chambers, Erin
Letscher, David
Ju, Tao
Topp, Christopher N.
author_facet Zeng, Dan
Li, Mao
Jiang, Ni
Ju, Yiwen
Schreiber, Hannah
Chambers, Erin
Letscher, David
Ju, Tao
Topp, Christopher N.
author_sort Zeng, Dan
collection PubMed
description BACKGROUND: 3D imaging, such as X-ray CT and MRI, has been widely deployed to study plant root structures. Many computational tools exist to extract coarse-grained features from 3D root images, such as total volume, root number and total root length. However, methods that can accurately and efficiently compute fine-grained root traits, such as root number and geometry at each hierarchy level, are still lacking. These traits would allow biologists to gain deeper insights into the root system architecture. RESULTS: We present TopoRoot, a high-throughput computational method that computes fine-grained architectural traits from 3D images of maize root crowns or root systems. These traits include the number, length, thickness, angle, tortuosity, and number of children for the roots at each level of the hierarchy. TopoRoot combines state-of-the-art algorithms in computer graphics, such as topological simplification and geometric skeletonization, with customized heuristics for robustly obtaining the branching structure and hierarchical information. TopoRoot is validated on both CT scans of excavated field-grown root crowns and simulated images of root systems, and in both cases, it was shown to improve the accuracy of traits over existing methods. TopoRoot runs within a few minutes on a desktop workstation for images at the resolution range of 400^3, with minimal need for human intervention in the form of setting three intensity thresholds per image. CONCLUSIONS: TopoRoot improves the state-of-the-art methods in obtaining more accurate and comprehensive fine-grained traits of maize roots from 3D imaging. The automation and efficiency make TopoRoot suitable for batch processing on large numbers of root images. Our method is thus useful for phenomic studies aimed at finding the genetic basis behind root system architecture and the subsequent development of more productive crops. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13007-021-00829-z.
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spelling pubmed-86673962021-12-13 TopoRoot: a method for computing hierarchy and fine-grained traits of maize roots from 3D imaging Zeng, Dan Li, Mao Jiang, Ni Ju, Yiwen Schreiber, Hannah Chambers, Erin Letscher, David Ju, Tao Topp, Christopher N. Plant Methods Methodology BACKGROUND: 3D imaging, such as X-ray CT and MRI, has been widely deployed to study plant root structures. Many computational tools exist to extract coarse-grained features from 3D root images, such as total volume, root number and total root length. However, methods that can accurately and efficiently compute fine-grained root traits, such as root number and geometry at each hierarchy level, are still lacking. These traits would allow biologists to gain deeper insights into the root system architecture. RESULTS: We present TopoRoot, a high-throughput computational method that computes fine-grained architectural traits from 3D images of maize root crowns or root systems. These traits include the number, length, thickness, angle, tortuosity, and number of children for the roots at each level of the hierarchy. TopoRoot combines state-of-the-art algorithms in computer graphics, such as topological simplification and geometric skeletonization, with customized heuristics for robustly obtaining the branching structure and hierarchical information. TopoRoot is validated on both CT scans of excavated field-grown root crowns and simulated images of root systems, and in both cases, it was shown to improve the accuracy of traits over existing methods. TopoRoot runs within a few minutes on a desktop workstation for images at the resolution range of 400^3, with minimal need for human intervention in the form of setting three intensity thresholds per image. CONCLUSIONS: TopoRoot improves the state-of-the-art methods in obtaining more accurate and comprehensive fine-grained traits of maize roots from 3D imaging. The automation and efficiency make TopoRoot suitable for batch processing on large numbers of root images. Our method is thus useful for phenomic studies aimed at finding the genetic basis behind root system architecture and the subsequent development of more productive crops. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13007-021-00829-z. BioMed Central 2021-12-13 /pmc/articles/PMC8667396/ /pubmed/34903248 http://dx.doi.org/10.1186/s13007-021-00829-z Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Methodology
Zeng, Dan
Li, Mao
Jiang, Ni
Ju, Yiwen
Schreiber, Hannah
Chambers, Erin
Letscher, David
Ju, Tao
Topp, Christopher N.
TopoRoot: a method for computing hierarchy and fine-grained traits of maize roots from 3D imaging
title TopoRoot: a method for computing hierarchy and fine-grained traits of maize roots from 3D imaging
title_full TopoRoot: a method for computing hierarchy and fine-grained traits of maize roots from 3D imaging
title_fullStr TopoRoot: a method for computing hierarchy and fine-grained traits of maize roots from 3D imaging
title_full_unstemmed TopoRoot: a method for computing hierarchy and fine-grained traits of maize roots from 3D imaging
title_short TopoRoot: a method for computing hierarchy and fine-grained traits of maize roots from 3D imaging
title_sort toporoot: a method for computing hierarchy and fine-grained traits of maize roots from 3d imaging
topic Methodology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8667396/
https://www.ncbi.nlm.nih.gov/pubmed/34903248
http://dx.doi.org/10.1186/s13007-021-00829-z
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