Semi-automated Root Image Analysis (saRIA).

Cliques: 315
ID: 72349
2019
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Ranked #460 of 1,628 articles by views in Scientific reports

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Resumo
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.
Chave de Referencia
narisetti2019semiautomatedscientific Use esta chave para citacao automatica no manuscrito ao usar SciMatic Gerenciador de Manuscritos ou Gerenciador de Teses
Autores Narisetti, Narendra;Henke, Michael;Seiler, Christiane;Shi, Rongli;Junker, Astrid;Altmann, Thomas;Gladilin, Evgeny;
Periodico Scientific reports
Ano 2019
DOI
10.1038/s41598-019-55876-3
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environmental economics green certificates property rights market “green” certificates of origin

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