Abstract
Background3D 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.ResultsWe present TopoRoot, a high-throughput computational method that computes fine-grained architectural traits from 3D images of maize root crowns or root systems. 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 CT scans of excavated field-grown root crowns and simulated images of root systems, and in both cases, it was shown to improve the accuracy of traits over existing methods. TopoRoot runs within a few minutes on a desktop workstation for images at the resolution range of 400^3, with minimal need for human intervention in the form of setting three intensity thresholds per image.ConclusionsTopoRoot improves the state-of-the-art methods in obtaining more accurate and comprehensive fine-grained traits of maize roots from 3D imaging. The automation and efficiency make TopoRoot suitable for batch processing on large numbers 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.
Highlights
Roots are the primary means by which the plant absorbs water and nutrients, and they provide anchorage to the plant
Data preparation TopoRoot is tested on two sets of maize root images, one consisting of 45 X-ray Computed Tomography (CT) scans of excavated root crowns, and another consisting of 495 synthetic images of simulated maize root systems
Samples were clamped and placed on a turntable for imaging at a magnification of 1.17X and 10 frames per second, collecting 1800 16-bit digital radiographs over a 3 min scan time. efX-CT software was used to reconstruct the scan into a 3D image at 109 μm voxel resolution
Summary
Roots are the primary means by which the plant absorbs water and nutrients, and they provide anchorage to the plant. Most image-based root phenotyping methods only compute overall traits such as the volume, depth, convex hull volume, total root length, and root number [11,12,13,14]. Though useful, these traits which are aggregated over the whole root system do not capture the branching structure or the hierarchical organization of individual roots, which provide a much more comprehensive description of RSA. 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
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