Semi-automated Root Image Analysis (saRIA)

Abstract Quantitative characterization of root system architecture and its development is important for the assessment of a complete plant phenotype. To enable high-throughput phenotyping of plant roots efficient solutions for automated image analysis are required. Since plants naturally grow in an...

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Autores principales:	Narendra Narisetti, Michael Henke, Christiane Seiler, Rongli Shi, Astrid Junker, Thomas Altmann, Evgeny Gladilin
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2019
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/a51fcfac80354d729ece98048c640e80
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spelling	oai:doaj.org-article:a51fcfac80354d729ece98048c640e802021-12-02T13:57:01ZSemi-automated Root Image Analysis (saRIA)10.1038/s41598-019-55876-32045-2322https://doaj.org/article/a51fcfac80354d729ece98048c640e802019-12-01T00:00:00Zhttps://doi.org/10.1038/s41598-019-55876-3https://doaj.org/toc/2045-2322Abstract Quantitative characterization of root system architecture and its development is important for the assessment of a complete plant phenotype. To enable high-throughput phenotyping of plant roots efficient solutions for automated image analysis are required. Since plants naturally grow in an opaque soil environment, automated analysis of optically heterogeneous and noisy soil-root images represents a challenging task. Here, we present a user-friendly GUI-based tool for semi-automated analysis of soil-root images which allows to perform an efficient image segmentation using a combination of adaptive thresholding and morphological filtering and to derive various quantitative descriptors of the root system architecture including total length, local width, projection area, volume, spatial distribution and orientation. The results of our semi-automated root image segmentation are in good conformity with the reference ground-truth data (mean dice coefficient = 0.82) compared to IJ_Rhizo and GiAroots. Root biomass values calculated with our tool within a few seconds show a high correlation (Pearson coefficient = 0.8) with the results obtained using conventional, pure manual segmentation approaches. Equipped with a number of adjustable parameters and optional correction tools our software is capable of significantly accelerating quantitative analysis and phenotyping of soil-, agar- and washed root images.Narendra NarisettiMichael HenkeChristiane SeilerRongli ShiAstrid JunkerThomas AltmannEvgeny GladilinNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 9, Iss 1, Pp 1-10 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Narendra Narisetti Michael Henke Christiane Seiler Rongli Shi Astrid Junker Thomas Altmann Evgeny Gladilin Semi-automated Root Image Analysis (saRIA)
description	Abstract Quantitative characterization of root system architecture and its development is important for the assessment of a complete plant phenotype. To enable high-throughput phenotyping of plant roots efficient solutions for automated image analysis are required. Since plants naturally grow in an opaque soil environment, automated analysis of optically heterogeneous and noisy soil-root images represents a challenging task. Here, we present a user-friendly GUI-based tool for semi-automated analysis of soil-root images which allows to perform an efficient image segmentation using a combination of adaptive thresholding and morphological filtering and to derive various quantitative descriptors of the root system architecture including total length, local width, projection area, volume, spatial distribution and orientation. The results of our semi-automated root image segmentation are in good conformity with the reference ground-truth data (mean dice coefficient = 0.82) compared to IJ_Rhizo and GiAroots. Root biomass values calculated with our tool within a few seconds show a high correlation (Pearson coefficient = 0.8) with the results obtained using conventional, pure manual segmentation approaches. Equipped with a number of adjustable parameters and optional correction tools our software is capable of significantly accelerating quantitative analysis and phenotyping of soil-, agar- and washed root images.
format	article
author	Narendra Narisetti Michael Henke Christiane Seiler Rongli Shi Astrid Junker Thomas Altmann Evgeny Gladilin
author_facet	Narendra Narisetti Michael Henke Christiane Seiler Rongli Shi Astrid Junker Thomas Altmann Evgeny Gladilin
author_sort	Narendra Narisetti
title	Semi-automated Root Image Analysis (saRIA)
title_short	Semi-automated Root Image Analysis (saRIA)
title_full	Semi-automated Root Image Analysis (saRIA)
title_fullStr	Semi-automated Root Image Analysis (saRIA)
title_full_unstemmed	Semi-automated Root Image Analysis (saRIA)
title_sort	semi-automated root image analysis (saria)
publisher	Nature Portfolio
publishDate	2019
url	https://doaj.org/article/a51fcfac80354d729ece98048c640e80
work_keys_str_mv	AT narendranarisetti semiautomatedrootimageanalysissaria AT michaelhenke semiautomatedrootimageanalysissaria AT christianeseiler semiautomatedrootimageanalysissaria AT ronglishi semiautomatedrootimageanalysissaria AT astridjunker semiautomatedrootimageanalysissaria AT thomasaltmann semiautomatedrootimageanalysissaria AT evgenygladilin semiautomatedrootimageanalysissaria
_version_	1718392338241290240

Semi-automated Root Image Analysis (saRIA)

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