Semi-automated Root Image Analysis (saRIA)

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

doi:10.1038/s41598-019-55876-3

Narendra Narisetti, Astrid Junker + Show 5 more

Open Access

https://doi.org/10.1038/s41598-019-55876-3

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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.

Highlights

Plant roots are key drivers of plant development and growth
The majority of software for root image analysis is rather tailored to artificial setups such as transparent growing media that cannot be applied to the analysis of heterogeneous and noisy soil-root images
We present a graphic user interface (GUI)-based handy tool for semi-automated root image analysis which enables rapid segmentation of diverse 2D root images including potting soil and artificial media setups in a high-through manner

Summary

Introduction

Plant roots are key drivers of plant development and growth. They absorb the water and inorganic nutrients from the soil[1,2,3] and provide anchoring of the plant body[4,5]. In the case of roots, the relevant traits include descriptors of global and local root morphology (like total length, area, volume, and diameter, or lateral branching, the direction of a tangent, etc.)[9,10,11,12] Monitoring of these traits enables conclusions about the ability of plants to respond to variable environmental factors such as drought, cold, starvation, etc.[13]. To analyze a large number of root images in an automated high-throughput manner, a number of software tools are available Most of these tools were, designed to extract RSA traits from specific imaging systems, e.g., images from minirhizotron[25] and images of roots grown in agar[27]. We present a GUI-based handy tool for semi-automated root image analysis (saRIA) which enables rapid segmentation of diverse 2D root images including potting soil and artificial media setups in a high-through manner. Based on a combination of adaptive image enhancement, adjustable thresholding and filtering as well as optional manual correction, saRIA represents a broadly applicable tool for quantitative analysis of diverse root image modalities as well as generation of quality ground truth reference images for the training of advanced machine learning/deep learning algorithms

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