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

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 (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.

4DRoot: Root phenotyping software for temporal 3D scans by X-ray computed tomography

“…The methodology to compute the traits is based on transversal topdown sections of the root point cloud. Another software solution for root phenotyping by XCT data is TopoRoot (Zeng et al, 2021) that uses a stack of 2D image slices from mature maize root systems. It is based on computer graphics algorithms such as topological simplification and 3D skeletonization.…”

Comparison of open-source image-based reconstruction pipelines for 3D root phenotyping of field-grown maize

“…2D imaging approaches can only capture partial information from dense and highly occluded 3D maize root structures, however. As such, quantifying important traits such as crown root number and whorl number and the distance remains challenging [19]. 3D phenotyping methods are a promising option thanks to their ability to leverage multiple views of a given scene to resolve highly occluded structures [20] [21][22][23].…”

“…3D phenotyping methods are a promising option thanks to their ability to leverage multiple views of a given scene to resolve highly occluded structures [20] [21][22][23]. One of the key challenges in 3D root phenotyping method is to reconstruct a 3D representation of the root [19]. The available open-source image-based 3D reconstruction pipelines can process large sets of unordered and diverse images and generate a dense colored point cloud model or a triangulated textured mesh [24].…”