RSAtrace3D: robust vectorization software for measuring monocot root system architecture

Abstract Background The root distribution in the soil is one of the elements that comprise the root system architecture (RSA). In monocots, RSA comprises radicle and crown roots, each of which can be basically represented by a single curve with lateral root branches or approximated using a polyline. Moreover, RSA vectorization (polyline conversion) is useful for RSA phenotyping. However, a robust software that can enable RSA vectorization while using noisy three-dimensional (3D) volumes is unavailable. Results We developed RSAtrace3D, which is a robust 3D RSA vectorization software for monocot RSA phenotyping. It manages the single root (radicle or crown root) as a polyline (a vector), and the set of the polylines represents the entire RSA. RSAtrace3D vectorizes root segments between the two ends of a single root. By utilizing several base points on the root, RSAtrace3D suits noisy images if it is difficult to vectorize it using only two end nodes of the root. Additionally, by employing a simple tracking algorithm that uses the center of gravity (COG) of the root voxels to determine the tracking direction, RSAtrace3D efficiently vectorizes the roots. Thus, RSAtrace3D represents the single root shape more precisely than straight lines or spline curves. As a case study, rice (Oryza sativa) RSA was vectorized from X-ray computed tomography (CT) images, and RSA traits were calculated. In addition, varietal differences in RSA traits were observed. The vector data were 32,000 times more compact than raw X-ray CT images. Therefore, this makes it easier to share data and perform re-analyses. For example, using data from previously conducted studies. For monocot plants, the vectorization and phenotyping algorithm are extendable and suitable for numerous applications. Conclusions RSAtrace3D is an RSA vectorization software for 3D RSA phenotyping for monocots. Owing to the high expandability of the RSA vectorization and phenotyping algorithm, RSAtrace3D can be applied not only to rice in X-ray CT images but also to other monocots in various 3D images. Since this software is written in Python language, it can be easily modified and will be extensively applied by researchers in this field.

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High-throughput three-dimensional visualization of root system architecture of rice using X-ray computed tomography

10.21203/rs.2.18397/v2 ◽

2020 ◽

Author(s):

Shota Teramoto ◽

Satoko Takayasu ◽

Yuka Kitomi ◽

Yumiko Arai-Sanoh ◽

Takanari Tanabata ◽

...

Keyword(s):

High Throughput ◽

System Architecture ◽

Three Dimensional ◽

Ct Scanning ◽

Root System Architecture ◽

Visualization Method ◽

Process Flow ◽

X Ray ◽

X Ray Computed ◽

Crown Roots

Abstract Background: X-ray computed tomography (CT) allows us to visualize root system architecture (RSA) beneath the soil, non-destructively and in a three-dimensional (3-D) form. However, CT scanning, reconstruction processes, and root isolation from X-ray CT volumes, take considerable time. For genetic analyses, such as quantitative trait locus mapping, which require a large population size, a high-throughput RSA visualization method is required. Results: We have developed a high-throughput process flow for the 3-D visualization of rice (Oryza sativa) RSA (consisting of radicle and crown roots), using X-ray CT. The process flow includes use of a uniform particle size, calcined clay to reduce the possibility of visualizing non-root segments, use of a higher tube voltage and current in the X-ray CT scanning to increase root-to-soil contrast, and use of a 3-D median filter and edge detection algorithm to isolate root segments. Using high-performance computing technology, this analysis flow requires only 10 min (33 s, if a rough image is acceptable) for CT scanning and reconstruction, and 2 min for image processing, to visualize rice RSA. This reduced time allowed us to conduct the genetic analysis associated with 3-D RSA phenotyping. In 2-week-old seedlings, 85% and 100% of radicle and crown roots were detected, when 16 cm and 20 cm diameter pots were used, respectively. The X-ray dose per scan was estimated at < 0.09 Gy, which did not impede rice growth. Using the developed process flow, we were able to follow daily RSA development, i.e., 4-D RSA development, of an upland rice variety, over three weeks. Conclusions: We developed a high-throughput process flow for 3-D rice RSA visualization by X-ray CT. The X-ray dose assay on plant growth has shown that this methodology could be applicable for 4-D RSA phenotyping. We named the RSA visualization method ‘RSAvis3D’ and are confident that it represents a potentially efficient application for 3-D RSA phenotyping of various plant species.

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High-throughput three-dimensional visualization of root system architecture of rice using X-ray computed tomography

Plant Methods ◽

10.1186/s13007-020-00612-6 ◽

2020 ◽

Vol 16 (1) ◽

Author(s):

Shota Teramoto ◽

Satoko Takayasu ◽

Yuka Kitomi ◽

Yumiko Arai-Sanoh ◽

Takanari Tanabata ◽

...

Keyword(s):

Computed Tomography ◽

Root System ◽

High Throughput ◽

System Architecture ◽

Three Dimensional ◽

Root System Architecture ◽

X Ray ◽

X Ray Computed

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In Situ Visualization and Quantification of Three-Dimensional Root System Architecture and Growth Using X-Ray Computed Tomography

Vadose Zone Journal ◽

10.2136/vzj2014.03.0024 ◽

2014 ◽

Vol 13 (8) ◽

pp. vzj2014.03.0024 ◽

Cited By ~ 27

Author(s):

Nicolai Koebernick ◽

Ulrich Weller ◽

Katrin Huber ◽

Steffen Schlüter ◽

Hans-Jörg Vogel ◽

...

Keyword(s):

Computed Tomography ◽

Root System ◽

System Architecture ◽

Three Dimensional ◽

Root System Architecture ◽

X Ray ◽

In Situ Visualization ◽

X Ray Computed

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TopoRoot: A method for computing hierarchy and fine-grained traits of maize roots from X-ray CT images

10.1101/2021.08.24.457522 ◽

2021 ◽

Author(s):

Dan Zeng ◽

Mao Li ◽

Ni Jiang ◽

Yiwen Ju ◽

Hannah Schreiber ◽

...

Keyword(s):

Root System ◽

System Architecture ◽

State Of The Art ◽

Root System Architecture ◽

Ct Images ◽

Maize Root ◽

Root Number ◽

X Ray ◽

Fine Grained ◽

Maize Roots

Background: 3D imaging, such as X-ray CT and MRI, has been widely deployed to study plant root structures. Many computational tools exist to extract coarse-grained features from 3D root images, such as total volume, root number and total root length. However, methods that can accurately and efficiently compute fine-grained root traits, such as root number and geometry at each hierarchy level, are still lacking. These traits would allow biologists to gain deeper insights into the root system architecture (RSA). Results: We present TopoRoot, a high-throughput computational method that computes fine-grained architectural traits from 3D X-ray CT images of field-excavated maize root crowns. These traits include the number, length, thickness, angle, tortuosity, and number of children for the roots at each level of the hierarchy. TopoRoot combines state-of-the-art algorithms in computer graphics, such as topological simplification and geometric skeletonization, with customized heuristics for robustly obtaining the branching structure and hierarchical information. TopoRoot is validated on both real and simulated root images, and in both cases it was shown to improve the accuracy of traits over existing methods. We also demonstrate TopoRoot in differentiating a maize root mutant from its wild type segregant using fine-grained traits. TopoRoot runs within a few minutes on a desktop workstation for volumes at the resolution range of 400^3, without need for human intervention. Conclusions: TopoRoot improves the state-of-the-art methods in obtaining more accurate and comprehensive fine-grained traits of maize roots from 3D CT images. The automation and efficiency makes TopoRoot suitable for batch processing on a large number of root images. Our method is thus useful for phenomic studies aimed at finding the genetic basis behind root system architecture and the subsequent development of more productive crops.

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High-throughput three-dimensional visualization of root system architecture of rice using X-ray computed tomography

10.21203/rs.2.18397/v1 ◽

2019 ◽

Author(s):

Shota Teramoto ◽

Satoko Takayasu ◽

Yuka Kitomi ◽

Yumiko Arai-Sanoh ◽

Takanari Tanabata ◽

...

Keyword(s):

Image Processing ◽

High Throughput ◽

Large Scale ◽

Three Dimensional ◽

Ct Scanning ◽

Root System Architecture ◽

Ct Images ◽

X Ray ◽

X Ray Computed ◽

Crown Roots

Abstract Background: Plants adjust their root system architecture (RSA) against changing environments to optimize their growth. Nondestructive phenotyping of roots beneath the soil not only reveal the response of RSA against environmental stimuli but also allow for designing an ideal RSA for crop cultivation. Generally, roots beneath the soil surface are three-dimensionally visualized using X-ray computed tomography (CT). However, root isolation from X-ray CT images involves a longer time; in addition, CT scanning and reconstruction processes require longer periods. For large-scale root phenotyping, a shorter image acquisition time is required. Thus, the objective of this study is to develop a high-throughput pipeline to visualize rice RSA consisting of radicle and crown roots in the soil, from X-ray CT images.Results: We performed the following three processes to develop the pipeline. First, we used calcined clay with uniform soil particle size as the soil substrate. The size of voids between the soil particles was less than the scanning resolution, resulting in a clear root shape in the CT images. Second, we optimized the parameters for rapid X-ray CT scanning. Higher tube voltage and current produced the highest root-to-soil contrast images. Third, we used a 3-D median filter to reduce noise, and an edge detection alogism to isolate the root segments. The detection limits of the root diameters of the pots of diameters 16 cm and 20 cm were 0.2 mm and 0.3 mm, respectively. Because the crown root diameter of rice is generally higher than 0.2 mm, almost all crown roots could be visualized. Our condition allows for simultaneously performing CT scanning and reconstruction by a high-performance computing technology. Consequently, our pipeline visualizes rice RSA in the soil, requiring less than 10 min (33 s, if a rough image is acceptable) for CT scanning and reconstruction, and 2 min for image processing to visualize rice RSA. We scanned the roots of the upland rice (considered in this study) daily, and our pipeline successfully visualized the root development dynamics over three weeks. Conclusions: We developed a rapid three-dimensional visualization method to visualize rice RSA in the soil using X-ray CT and a fully automated-image processing method known as RSAvis3D. Our methodology allows for high-throughput measuring and requires no manual operators in image processing, thereby providing a potentially efficient large-scale root phenotyping.

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High-throughput three-dimensional visualization of root system architecture of rice using X-ray computed tomography

10.21203/rs.2.18397/v3 ◽

2020 ◽

Author(s):

Shota Teramoto ◽

Satoko Takayasu ◽

Yuka Kitomi ◽

Yumiko Arai-Sanoh ◽

Takanari Tanabata ◽

...

Keyword(s):

High Throughput ◽

System Architecture ◽

Three Dimensional ◽

Ct Scanning ◽

Root System Architecture ◽

Visualization Method ◽

Process Flow ◽

X Ray ◽

X Ray Computed ◽

Crown Roots

Abstract Background: X-ray computed tomography (CT) allows us to visualize root system architecture (RSA) beneath the soil, non-destructively and in a three-dimensional (3-D) form. However, CT scanning, reconstruction processes, and root isolation from X-ray CT volumes, take considerable time. For genetic analyses, such as quantitative trait locus mapping, which require a large population size, a high-throughput RSA visualization method is required.Results: We have developed a high-throughput process flow for the 3-D visualization of rice (Oryza sativa) RSA (consisting of radicle and crown roots), using X-ray CT. The process flow includes use of a uniform particle size, calcined clay to reduce the possibility of visualizing non-root segments, use of a higher tube voltage and current in the X-ray CT scanning to increase root-to-soil contrast, and use of a 3-D median filter and edge detection algorithm to isolate root segments. Using high-performance computing technology, this analysis flow requires only 10 min (33 s, if a rough image is acceptable) for CT scanning and reconstruction, and 2 min for image processing, to visualize rice RSA. This reduced time allowed us to conduct the genetic analysis associated with 3-D RSA phenotyping. In 2-week-old seedlings, 85% and 100% of radicle and crown roots were detected, when 16 cm and 20 cm diameter pots were used, respectively. The X-ray dose per scan was estimated at < 0.09 Gy, which did not impede rice growth. Using the developed process flow, we were able to follow daily RSA development, i.e., 4-D RSA development, of an upland rice variety, over three weeks. Conclusions: We developed a high-throughput process flow for 3-D rice RSA visualization by X-ray CT. The X-ray dose assay on plant growth has shown that this methodology could be applicable for 4-D RSA phenotyping. We named the RSA visualization method ‘RSAvis3D’ and are confident that it represents a potentially efficient application for 3-D RSA phenotyping of various plant species.

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