Fully-automated root image analysis (faRIA)

Abstract High-throughput root phenotyping in the soil became an indispensable quantitative tool for the assessment of effects of climatic factors and molecular perturbation on plant root morphology, development and function. To efficiently analyse a large amount of structurally complex soil-root ima...

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Autores principales:	Narendra Narisetti, Michael Henke, Christiane Seiler, Astrid Junker, Jörn Ostermann, Thomas Altmann, Evgeny Gladilin
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/a4060d5db9134350afa66d6cf3ddc167
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spelling	oai:doaj.org-article:a4060d5db9134350afa66d6cf3ddc1672021-12-02T17:06:09ZFully-automated root image analysis (faRIA)10.1038/s41598-021-95480-y2045-2322https://doaj.org/article/a4060d5db9134350afa66d6cf3ddc1672021-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-95480-yhttps://doaj.org/toc/2045-2322Abstract High-throughput root phenotyping in the soil became an indispensable quantitative tool for the assessment of effects of climatic factors and molecular perturbation on plant root morphology, development and function. To efficiently analyse a large amount of structurally complex soil-root images advanced methods for automated image segmentation are required. Due to often unavoidable overlap between the intensity of fore- and background regions simple thresholding methods are, generally, not suitable for the segmentation of root regions. Higher-level cognitive models such as convolutional neural networks (CNN) provide capabilities for segmenting roots from heterogeneous and noisy background structures, however, they require a representative set of manually segmented (ground truth) images. Here, we present a GUI-based tool for fully automated quantitative analysis of root images using a pre-trained CNN model, which relies on an extension of the U-Net architecture. The developed CNN framework was designed to efficiently segment root structures of different size, shape and optical contrast using low budget hardware systems. The CNN model was trained on a set of 6465 masks derived from 182 manually segmented near-infrared (NIR) maize root images. Our experimental results show that the proposed approach achieves a Dice coefficient of 0.87 and outperforms existing tools (e.g., SegRoot) with Dice coefficient of 0.67 by application not only to NIR but also to other imaging modalities and plant species such as barley and arabidopsis soil-root images from LED-rhizotron and UV imaging systems, respectively. In summary, the developed software framework enables users to efficiently analyse soil-root images in an automated manner (i.e. without manual interaction with data and/or parameter tuning) providing quantitative plant scientists with a powerful analytical tool.Narendra NarisettiMichael HenkeChristiane SeilerAstrid JunkerJörn OstermannThomas AltmannEvgeny GladilinNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Narendra Narisetti Michael Henke Christiane Seiler Astrid Junker Jörn Ostermann Thomas Altmann Evgeny Gladilin Fully-automated root image analysis (faRIA)
description	Abstract High-throughput root phenotyping in the soil became an indispensable quantitative tool for the assessment of effects of climatic factors and molecular perturbation on plant root morphology, development and function. To efficiently analyse a large amount of structurally complex soil-root images advanced methods for automated image segmentation are required. Due to often unavoidable overlap between the intensity of fore- and background regions simple thresholding methods are, generally, not suitable for the segmentation of root regions. Higher-level cognitive models such as convolutional neural networks (CNN) provide capabilities for segmenting roots from heterogeneous and noisy background structures, however, they require a representative set of manually segmented (ground truth) images. Here, we present a GUI-based tool for fully automated quantitative analysis of root images using a pre-trained CNN model, which relies on an extension of the U-Net architecture. The developed CNN framework was designed to efficiently segment root structures of different size, shape and optical contrast using low budget hardware systems. The CNN model was trained on a set of 6465 masks derived from 182 manually segmented near-infrared (NIR) maize root images. Our experimental results show that the proposed approach achieves a Dice coefficient of 0.87 and outperforms existing tools (e.g., SegRoot) with Dice coefficient of 0.67 by application not only to NIR but also to other imaging modalities and plant species such as barley and arabidopsis soil-root images from LED-rhizotron and UV imaging systems, respectively. In summary, the developed software framework enables users to efficiently analyse soil-root images in an automated manner (i.e. without manual interaction with data and/or parameter tuning) providing quantitative plant scientists with a powerful analytical tool.
format	article
author	Narendra Narisetti Michael Henke Christiane Seiler Astrid Junker Jörn Ostermann Thomas Altmann Evgeny Gladilin
author_facet	Narendra Narisetti Michael Henke Christiane Seiler Astrid Junker Jörn Ostermann Thomas Altmann Evgeny Gladilin
author_sort	Narendra Narisetti
title	Fully-automated root image analysis (faRIA)
title_short	Fully-automated root image analysis (faRIA)
title_full	Fully-automated root image analysis (faRIA)
title_fullStr	Fully-automated root image analysis (faRIA)
title_full_unstemmed	Fully-automated root image analysis (faRIA)
title_sort	fully-automated root image analysis (faria)
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/a4060d5db9134350afa66d6cf3ddc167
work_keys_str_mv	AT narendranarisetti fullyautomatedrootimageanalysisfaria AT michaelhenke fullyautomatedrootimageanalysisfaria AT christianeseiler fullyautomatedrootimageanalysisfaria AT astridjunker fullyautomatedrootimageanalysisfaria AT jornostermann fullyautomatedrootimageanalysisfaria AT thomasaltmann fullyautomatedrootimageanalysisfaria AT evgenygladilin fullyautomatedrootimageanalysisfaria
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Fully-automated root image analysis (faRIA)

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