Dan Zeng1, Mao Li2, Ni Jiang2, Yiwen Ju3, Hannah Schreiber4, Erin Chambers4, David Letscher4, Tao Ju3, Christopher N Topp2. 1. Department of Computer Science and Engineering, Washington University in St. Louis, Saint Louis, MO, 63130, USA. danzeng8@gmail.com. 2. Donald Danforth Plant Science Center, Saint Louis, MO, 63132, USA. 3. Department of Computer Science and Engineering, Washington University in St. Louis, Saint Louis, MO, 63130, USA. 4. Department of Computer Science, Saint Louis University, Saint Louis, MO, 63103, USA.
Abstract
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. RESULTS: We 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. CONCLUSIONS: TopoRoot 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.
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. RESULTS: We 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. CONCLUSIONS: TopoRoot 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.
Authors: Andrea Schnepf; Daniel Leitner; Magdalena Landl; Guillaume Lobet; Trung Hieu Mai; Shehan Morandage; Cheng Sheng; Mirjam Zörner; Jan Vanderborght; Harry Vereecken Journal: Ann Bot Date: 2018-04-18 Impact factor: 4.357
Authors: Johannes A Postma; Christian Kuppe; Markus R Owen; Nathan Mellor; Marcus Griffiths; Malcolm J Bennett; Jonathan P Lynch; Michelle Watt Journal: New Phytol Date: 2017-06-27 Impact factor: 10.151