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  • Behnke, Sven  (2)
  • Pohlmeier, Andreas  (2)
  • English  (2)
  • 1
    In: Vadose Zone Journal, Wiley, Vol. 12, No. 1 ( 2013-02), p. 1-9
    Abstract: An automated method for root system architecture reconstruction from three‐dimensional volume data sets obtained from magnetic resonance imaging (MRI) was developed and validated with a three‐dimensional semimanual reconstruction using virtual reality and a two‐dimensional reconstruction using SmartRoot. It was tested on the basis of an MRI image of a 25‐d‐old lupin ( Lupinus albus L.) grown in natural sand with a resolution of 0.39 by 0.39 by 1.1 mm. The automated reconstruction algorithm was inspired by methods for blood vessel detection in MRI images. It describes the root system by a hierarchical network of nodes, which are connected by segments of defined length and thickness, and also allows the calculation of root parameter profiles such as root length, surface, and apex density The obtained root system architecture (RSA) varied in number of branches, segments, and connectivity of the segments but did not vary in the average diameter of the segments (0.137 cm for semimanual and 0.143 cm for automatic RSA), total root surface (127 cm 2 for semimanual and 124 cm 2 for automatic RSA), total root length (293 cm for semimanual and 282 cm for automatic RSA), and total root volume (4.7 cm 3 for semimanual and 4.7 cm 3 for automatic RSA). The difference in performance of the automated and semimanual reconstructions was checked by using the root system as input for water uptake modeling with the Doussan model. Both systems worked well and allowed for continuous water flow. Slight differences in the connectivity appeared to be leading to locally different water flow velocities, which were 30% smaller for the semimanual method.
    Type of Medium: Online Resource
    ISSN: 1539-1663 , 1539-1663
    Language: English
    Publisher: Wiley
    Publication Date: 2013
    detail.hit.zdb_id: 2088189-7
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  • 2
    In: Plant Phenomics, American Association for the Advancement of Science (AAAS), Vol. 5 ( 2023-01)
    Abstract: Magnetic resonance imaging (MRI) is used to image root systems grown in opaque soil. However, reconstruction of root system architecture (RSA) from 3-dimensional (3D) MRI images is challenging. Low resolution and poor contrast-to-noise ratios (CNRs) hinder automated reconstruction. Hence, manual reconstruction is still widely used. Here, we evaluate a novel 2-step work flow for automated RSA reconstruction. In the first step, a 3D U-Net segments MRI images into root and soil in super-resolution. In the second step, an automated tracing algorithm reconstructs the root systems from the segmented images. We evaluated the merits of both steps for an MRI dataset of 8 lupine root systems, by comparing the automated reconstructions to manual reconstructions of unaltered and segmented MRI images derived with a novel virtual reality system. We found that the U-Net segmentation offers profound benefits in manual reconstruction: reconstruction speed was doubled (+97%) for images with low CNR and increased by 27% for images with high CNR. Reconstructed root lengths were increased by 20% and 3%, respectively. Therefore, we propose to use U-Net segmentation as a principal image preprocessing step in manual work flows. The root length derived by the tracing algorithm was lower than in both manual reconstruction methods, but segmentation allowed automated processing of otherwise not readily usable MRI images. Nonetheless, model-based functional root traits revealed similar hydraulic behavior of automated and manual reconstructions. Future studies will aim to establish a hybrid work flow that utilizes automated reconstructions as scaffolds that can be manually corrected.
    Type of Medium: Online Resource
    ISSN: 2643-6515
    Language: English
    Publisher: American Association for the Advancement of Science (AAAS)
    Publication Date: 2023
    detail.hit.zdb_id: 2968615-5
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