Fully-automated root image analysis (faRIA)
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14740%2F21%3A00124293" target="_blank" >RIV/00216224:14740/21:00124293 - isvavai.cz</a>
Result on the web
<a href="https://www.nature.com/articles/s41598-021-95480-y" target="_blank" >https://www.nature.com/articles/s41598-021-95480-y</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1038/s41598-021-95480-y" target="_blank" >10.1038/s41598-021-95480-y</a>
Alternative languages
Result language
angličtina
Original language name
Fully-automated root image analysis (faRIA)
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10609 - Biochemical research methods
Result continuities
Project
<a href="/en/project/EF16_026%2F0008446" target="_blank" >EF16_026/0008446: Signal integration and epigenetic reprograming for plant productivity</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Nature Scientific Reports
ISSN
2045-2322
e-ISSN
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Volume of the periodical
11
Issue of the periodical within the volume
1
Country of publishing house
DE - GERMANY
Number of pages
15
Pages from-to
16047
UT code for WoS article
000684558900004
EID of the result in the Scopus database
2-s2.0-85112629309