Fully-automated root image analysis (faRIA)

NARISETTI, N., Michael HENKE, C. SEILER, A. JUNKER, J. OSTERMANN, T. ALTMANN and E. GLADILIN. Fully-automated root image analysis (faRIA). Nature Scientific Reports. London: NATURE RESEARCH, 2021, vol. 11, No 1, p. 16047-16061. ISSN 2045-2322. Available from: https://dx.doi.org/10.1038/s41598-021-95480-y.

Basic information

• Original name: Fully-automated root image analysis (faRIA)
• Authors: NARISETTI, N., Michael HENKE (276 Germany, guarantor, belonging to the institution), C. SEILER, A. JUNKER, J. OSTERMANN, T. ALTMANN and E. GLADILIN.
• Edition: Nature Scientific Reports, London, NATURE RESEARCH, 2021, 2045-2322.

Other information

• Original language: English
• Type of outcome: Article in a journal
• Field of Study: 10609 Biochemical research methods
• Country of publisher: Germany
• Confidentiality degree: is not subject to a state or trade secret
• WWW: URL
• Impact factor: Impact factor: 4.996
• RIV identification code: RIV/00216224:14740/21:00124293
• Organization unit: Central European Institute of Technology
• Doi: http://dx.doi.org/10.1038/s41598-021-95480-y
• UT WoS: 000684558900004
• Keywords in English: SEGMENTATIONARCHITECTUREGROWTHRHIZOTOOL
• Tags: rivok
• Tags: International impact, Reviewed

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.

Links

• EF16_026/0008446, research and development project: Name: Integrace signálu a epigenetické reprogramování pro produktivitu rostlin