X‐Ray Computed Tomography of Crop Plant Root Systems Grown in Soil

Mairhofer, Stefan; Pridmore, Tony; Johnson, James A.; Wells, Darren M.; Bennett, Malcolm; Mooney, Sacha J.; Sturrock, Craig J.

doi:10.1002/cppb.20049

Cited by 32 publications

(22 citation statements)

References 16 publications

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“…A17 WT or cra2‐11 were grown in sieved (< 2 mm) sandy loam soil uniformly packed to a bulk density of 1.1 mg m −3 in a 68 mm (diameter) × 160 mm (height) cylindrical column made from high density polyethylene (Mairhofer et al , 2017) for 21 d in a Conviron A1000 growth chamber at 22°C, 60% humidity with a 16 h photoperiod. After 14 d or 21 d growth, each column was scanned using a GE v|tome|x M 240 kV X‐ray CT system at the Hounsfield Facility, University of Nottingham (UK).…”

Section: Methodsmentioning

confidence: 99%

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CEP receptor signalling controls root system architecture in Arabidopsis and Medicago

et al. 2020

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Root system architecture (RSA) influences the effectiveness of resources acquisition from soils but the genetic networks that control RSA remain largely unclear.We used rhizoboxes, X-ray computed tomography, grafting, auxin transport measurements and hormone quantification to demonstrate that Arabidopsis and Medicago CEP (C-TERMINALLY ENCODED PEPTIDE)-CEP RECEPTOR signalling controls RSA, the gravitropic set-point angle (GSA) of lateral roots (LRs), auxin levels and auxin transport.We showed that soil-grown Arabidopsis and Medicago CEP receptor mutants have a narrower RSA, which results from a steeper LR GSA. Grafting showed that CEPR1 in the shoot controls GSA. CEP receptor mutants exhibited an increase in rootward auxin transport and elevated shoot auxin levels. Consistently, the application of auxin to wild-type shoots induced a steeper GSA and auxin transport inhibitors counteracted the CEP receptor mutant's steep GSA phenotype. Concordantly, CEP peptides increased GSA and inhibited rootward auxin transport in wild-type but not in CEP receptor mutants.The results indicated that CEP-CEP receptor-dependent signalling outputs in Arabidopsis and Medicago control overall RSA, LR GSA, shoot auxin levels and rootward auxin transport. We propose that manipulating CEP signalling strength or CEP receptor downstream targets may provide means to alter RSA.

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Section: Methodsmentioning

confidence: 99%

“…Data were reconstructed and subjected to manual root segmentation techniques to digitally separate the roots from the soil. Extracted RSAs were quantified using R ooth software (Mairhofer et al , 2017) for total root length, branch structure and LR angle.…”

Section: Methodsmentioning

confidence: 99%

CEP receptor signalling controls root system architecture in Arabidopsis and Medicago

et al. 2020

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“…Visualization and quantification of root material in the reconstructed data were preformed using the method of Tracy et al (2012) , employing a combination of the VGStudioMAX v2.2 (Volume Graphics GmbH, Heidelberg, Germany) and RooTrak software ( Mairhofer et al , 2012 ). For the measurement of root angles, a bespoke software tool called RooTh was used ( Mairhofer, et al , 2017 ).…”

Section: Methodsmentioning

confidence: 99%

LARGE ROOT ANGLE1, encoding OsPIN2, is involved in root system architecture in rice

Wang

Guo

et al. 2017

Journal of Experimental Botany

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“…Nevertheless, additional techniques for identifying and removing false positives are an avenue for future work. In its current form, we found that it was possible to extract the primary root system for quantification using an existing tool [38]. This tool traverses the primary root system from detected tip locations to the seed, and fits smooth polynomial splines to major roots.…”

Section: Discussionmentioning

confidence: 99%

Three Dimensional Root CT Segmentation using Multi-Resolution Encoder-Decoder Networks

Soltaninejad

Sturrock

Griffiths

et al. 2019

Preprint

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We address the complex problem of reliably segmenting root structure from soil in X-ray Computed Tomography (CT) images. We utilise a deep learning approach, and propose a state-of-the-art multi-resolution architecture based on encoder-decoders. While previous work in encoder-decoders implies the use of multiple resolutions simply by downsampling and upsampling images, we make this process explicit, with branches of the network tasked separately with obtaining local high-resolution segmentation, and wider low-resolution contextual information. The complete network is a memory efficient implementation that is still able to resolve small root detail in large volumetric images. We evaluate our approach by comparing against a number of different encoder-decoder based architectures from the literature, as well as a popular existing image analysis tool designed for root CT segmentation. We show qualitatively and quantitatively that a multi-resolution approach offers substantial accuracy improvements over a both a small receptive field size in a deep network, or a larger receptive field in a shallower network. We obtain a Dice score of 0.59 compared with 0.41 for the closest competing method. We then further improve performance using an incremental learning approach, in which failures in the original network are used to generate harder negative training examples. Results of this process raise the precision of the network, and improve the Dice score to 0.66. Our proposed method requires no user interaction, is fully automatic, and identifies large and fine root material throughout the whole volume. The 3D segmented output of our method is well-connected, allowing the recovery of structured representations of root system architecture, and so may be successfully utilised in root phenotyping.

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X‐Ray Computed Tomography of Crop Plant Root Systems Grown in Soil

Cited by 32 publications

References 16 publications

CEP receptor signalling controls root system architecture in Arabidopsis and Medicago

CEP receptor signalling controls root system architecture in Arabidopsis and Medicago

LARGE ROOT ANGLE1, encoding OsPIN2, is involved in root system architecture in rice

Three Dimensional Root CT Segmentation using Multi-Resolution Encoder-Decoder Networks

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