ArticlePlant methods2021
An improved method for the segmentation of roots from X-ray computed tomography 3D images: Rootine v.2.
Article in Plant methods, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed.
- Convolutional neural networks combined with conventional filtering to semantically segment plant roots in rapidly scanned X-ray computed tomography volumes with high noise levels.Plant methods · 2024Article
- How to make sense of 3D representations for plant phenotyping: a compendium of processing and analysis techniques.Plant methods · 2023Review
- 3D segmentation of plant root systems using spatial pyramid pooling and locally adaptive field-of-view inference.Frontiers in plant science · 2023Article
- Improving the efficiency of plant root system phenotyping through digitization and automation.Breeding science · 2022Article
- 4DRoot: Root phenotyping software for temporal 3D scans by X-ray computed tomography.Frontiers in plant science · 2022Article
- Cover crop influence on pore size distribution and biopore dynamics: Enumerating root and soil faunal effects.Frontiers in plant science · 2022Article
- TopoRoot: a method for computing hierarchy and fine-grained traits of maize roots from 3D imaging.Plant methods · 2021Article
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4 authors.
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Abstract
backgroundX-ray computed tomography is acknowledged as a powerful tool for the study of root system architecture of plants growing in soil. In this paper, we improved the original root segmentation algorithm "Rootine" and present its succeeding version "Rootine v.2". In addition to gray value information, Rootine algorithms are based on shape detection of cylindrical roots. Both algorithms are macros for the ImageJ software and are made freely available to the public. New features in Rootine v.2 are (i) a pot wall detection and removal step to avoid segmentation artefacts for roots growing along the pot wall, (ii) a calculation of the root average gray value based on a histogram analysis, (iii) an automatic calculation of thresholds for hysteresis thresholding of the tubeness image to reduce the number of parameters and (iv) a false negatives recovery based on shape criteria to increase root recovery. We compare the segmentation results of Rootine v.1 and Rootine v.2 with the results of root washing and subsequent analysis with WinRhizo. We use a benchmark dataset of maize roots (Zea mays L. cv. B73) grown in repacked soil for two scenarios with differing soil heterogeneity and image quality.
resultsWe demonstrate that Rootine v.2 outperforms its preceding version in terms of root recovery and enables to match better the root diameter distribution data obtained with root washing. Despite a longer processing time, Rootine v.2 comprises less user-defined parameters and shows an overall greater usability.
conclusionThe proposed method facilitates higher root detection accuracy than its predecessor and has the potential for improving high-throughput root phenotyping procedures based on X-ray computed tomography data analysis.
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