Semi-automated Root Image Analysis (saRIA).

Narendra Narisetti, Michael Henke, Christiane Seiler, Rongli Shi, Astrid Junker, Thomas Altmann, Evgeny Gladilin

Scientific reports 2019 Dec 23

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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.

Citation

Narendra Narisetti, Michael Henke, Christiane Seiler, Rongli Shi, Astrid Junker, Thomas Altmann, Evgeny Gladilin. Semi-automated Root Image Analysis (saRIA). Scientific reports. 2019 Dec 23;9(1):19674