Semi-automated segmentation and visualization of complex undisturbed root systems with X-ray μCT

Maenhout, Peter; Sleutel, Steven; 徐, 会連; Hoorebeke, Luc Van; Cnudde, Veerle

doi:10.1016/j.still.2019.04.025

Cited by 21 publications

(8 citation statements)

References 37 publications

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“…We classify the visual description of the morphological feature model of plants, organs, or roots at a certain stage of the plant growth process into static/morphological description. For the research field of visualizing crop root morphology, the ability to extract large and complex root morphological features plays an important role in plant breeding research [18]. e combination type not only meets the needs of visual demonstration of plants but also needs to provide visual analysis and reference for the morphological changes of plants at different growth stages.…”

Section: Rq5: What Are the Categories Of 3d Plant Visualizationmentioning

confidence: 99%

Visualization Methods of 3D Plant Models: A Systematic Mapping Study

Cao

2021

Journal of Electrical and Computer Engineering

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Three-dimensional plant model visualization (3D-PMV) is based on plant biology and the plant structure construction model for virtual simulation three-dimensional display, reflecting plant structure characteristics and demonstrating the growth process. In this work, we used the method of systematic mapping study (SMS) to screen the relevant literature in order to explore and analyze the methods and goals of virtual plant visualization research and to provide assistance in future literature reviews. To this end, we conducted extensive searches to identify articles related to plant model visualization technology. We searched for papers from July 2010 to November 2020 from four mainstream databases, namely, ACM, IEEE Xplore, EI, and Web of Science, and found more than 2,900 papers on 3D-PMV. Finally, we selected 60 qualified papers. We mainly followed the SMS method to classify papers by answering seven questions, mainly extracting data for analysis on research types, research goals, model construction methods, visual model categories, number of publications, tools, and methods. The results show that solution proposals account for the largest proportion of research types, accounting for 75% of the total number of papers. This shows that the focus is on the improvement of original methods or the proposal of new methods in this field; research goals of research plant phenotypic characteristics account for the total 35%; 28% of the research objectives focus on showing the reflection of plant growth process, indicating that the research objective trends in this research field regard plant phenotypic characteristics and visualization of the growth process; static type model visualization also accounts for 83% of the total, indicating that the related research on visualization in this field is mostly static typing. Therefore, our work not only analyzes the challenges faced by 3D-PVM and the research directions that need more attention in the future but also provides some suggestions for researchers to find suitable research directions in this field. As the future direction of development, researchers need to pay more attention to the study of dynamic visualization methods of plant growth. We believe that for future research, it is indispensable to provide 3D-PMV with research methods, research goals, visualization model types, development, and other tools. This article analyzes the results of the extracted data based on the SMS method and makes an important contribution to the researcher's extensive understanding of the current status of 3D-PMV and the potential future research opportunities in the field.

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Section: Rq5: What Are the Categories Of 3d Plant Visualizationmentioning

confidence: 99%

Visualization Methods of 3D Plant Models: A Systematic Mapping Study

Cao

2021

Journal of Electrical and Computer Engineering

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“…For example, Proto et al (2020) demonstrated that an acoustic travel time tomography method from the ground surface was capable of depicting the lateral distribution of relatively thick roots, although it was unable to retrieve the vertical distribution. Maenhout et al (2019) and Zhao et al (2020) have shown that X-ray computed tomography (CT) can visualise tree root systems with a superior resolution that allows us to investigate the architecture in detail. However, the scanned volume needs to be destructively extracted although the root system can be kept undisturbed.…”

Section: Introductionmentioning

confidence: 99%

Ground-Penetrating Radar System, Measurement and Data Analysis for Investigating and Understanding Tree Root System Architecture

Takahashi¹,

Aoike²,

Kajino³

et al. 2022

Preprint

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Purpose: Ground-penetrating radar has been used for tree root mapping as a non-destructive technique. The conventional data collection is performed on a predefined grid, which is not very suited for tree roots. A method optimised for tree roots, a system that enables the method and data analyses are proposed in the paper. Methods: The performance of ground-penetrating radar depends on how clearly reflections are recorded and it can be optimised using a circular scan for tree roots, in contrast to the conventionally employed grid scan. A ground-penetrating radar system combined with a total station enabled us to record the accurate locations of the scans, thereby enabling the post-processing of circular scan data. Results: Data were acquired using circular scanning around a tree. The results clearly exhibited the presence of roots. Thus, the distribution and extension of the root system were mapped. Additionally, detailed root paths were reconstructed in a 3D space based on manually selected hyperbolic signatures on B-scans. The result corresponded excellently to the actual roots observed during the excavation. Conclusion: Tree root mapping by applying an imaging algorithm provided an easy and quick means of understanding the distribution. Further analysis enabled us to reconstruct the details of the tree root shape and distribution. Both data analyses required accurate positioning for improved results. The system and data analyses were very useful in understanding root architecture and studying the impact of subsurface conditions on the growth of tree roots.

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“…In the past years, many methods have been developed to segment and visualise roots in tomograms acquired with X-ray CT [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16]. Some algorithms rely on simple thresholding methods [8].…”

Section: Introductionmentioning

confidence: 99%

An Improved Method for the Segmentation of Roots from X-ray Computed Tomography 3D Images: Rootine v.2

Phalempin

Lippold

Vetterlein

et al. 2020

Preprint

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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 grey 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 (1) a pot wall detection and removal step to avoid segmentation artefacts for roots growing along the pot wall, (2) a calculation of the root average grey value based on a histogram analysis, (3) an automatic calculation of thresholds for hysteresis thresholding of the tubeness image to reduce the number of parameters and (4) 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 CT data analysis.

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Semi-automated segmentation and visualization of complex undisturbed root systems with X-ray μCT

Cited by 21 publications

References 37 publications

Visualization Methods of 3D Plant Models: A Systematic Mapping Study

Visualization Methods of 3D Plant Models: A Systematic Mapping Study

Ground-Penetrating Radar System, Measurement and Data Analysis for Investigating and Understanding Tree Root System Architecture

An Improved Method for the Segmentation of Roots from X-ray Computed Tomography 3D Images: Rootine v.2

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