RSAtrace3D: robust vectorization software for measuring monocot root system architecture

Abstract

Background The root distribution in the soil is one of the elements that comprise the root system architecture (RSA). In monocots, RSA comprises radicle and crown roots, each of which can be basically represented by a single curve with lateral root branches or approximated using a polyline. Moreover, RSA vectorization (polyline conversion) is useful for RSA phenotyping. However, a robust software that can enable RSA vectorization while using noisy three-dimensional (3D) volumes is unavailable. Results We developed RSAtrace3D, which is a robust 3D RSA vectorization software for monocot RSA phenotyping. It manages the single root (radicle or crown root) as a polyline (a vector), and the set of the polylines represents the entire RSA. RSAtrace3D vectorizes root segments between the two ends of a single root. By utilizing several base points on the root, RSAtrace3D suits noisy images if it is difficult to vectorize it using only two end nodes of the root. Additionally, by employing a simple tracking algorithm that uses the center of gravity (COG) of the root voxels to determine the tracking direction, RSAtrace3D efficiently vectorizes the roots. Thus, RSAtrace3D represents the single root shape more precisely than straight lines or spline curves. As a case study, rice (Oryza sativa) RSA was vectorized from X-ray computed tomography (CT) images, and RSA traits were calculated. In addition, varietal differences in RSA traits were observed. The vector data were 32,000 times more compact than raw X-ray CT images. Therefore, this makes it easier to share data and perform re-analyses. For example, using data from previously conducted studies. For monocot plants, the vectorization and phenotyping algorithm are extendable and suitable for numerous applications. Conclusions RSAtrace3D is an RSA vectorization software for 3D RSA phenotyping for monocots. Owing to the high expandability of the RSA vectorization and phenotyping algorithm, RSAtrace3D can be applied not only to rice in X-ray CT images but also to other monocots in various 3D images. Since this software is written in Python language, it can be easily modified and will be extensively applied by researchers in this field.

Recent advances in methods for in situ root phenotyping

“…NMRooting achieves 3D visualization through Mayavi ( Ramachandran & Varoquaux, 2011 ). Teramoto, Tanabata & Uga (2021) recently developed RSAtrace3D, a robust 3D root architecture vectorization software for monocot root phenotyping. RSAtrace3D implements graphical user interface by Python and can be applied to analyze rice X-ray CT images and various 3D images of other monocots.…”

Improving the efficiency of plant root system phenotyping through digitization and automation

“…We also created RSAtrace3D, which uses a top-down approach to vectorize roots. Combined, we are able to measure RSA traits ( Teramoto et al 2020 , 2021 ). RSAvis3D is a software for visualizing RSA development of rice ( Teramoto et al 2020 ).…”

“…To avoid human intervention, we adopted a bottom-up approach and used RSAvis3D to rapidly visualize the root system. To quantify RSA, the root system in RSAvis3D was vectorized with RSAtrace3D ( Teramoto et al 2021 ). Vectorization requires a top-down approach and human intervention, however, more detailed measurements of the root system can be made because the shape and connection of the root system can be expressed numerically.…”

“…Among them, some bottom-up approaches enable fully automated phenotyping ( Gao et al 2019b , Phalempin et al 2021 , Teramoto et al 2020 ). Other software that is not fully automated is also used for different purposes, e.g., RSAtrace3D vectorizes RSA in X-ray CT images semi-automatically ( Teramoto et al 2021 ). This semi-automatic process is one bottleneck to full automation for analyzing block samples.…”

“…It is predicted, therefore, that the technology to measure unseparated roots using CNNs will be developed in the future. In block samples, semantic segmentation has been fully automated by using CNNs, but vectorization of the root systems, which is needed to measure more complex traits, has been mostly semi-automatic ( Teramoto et al 2021 ). In medical science, fully automated vectorization algorism such as Segmentation-Less, Automated, Vascular Vectorization for the neurovascular network has been developed ( Mihelic et al 2021 ).…”