An improved method for the segmentation of roots from X-ray computed tomography 3D images: Rootine v.2

Abstract

Background X-ray computed tomography is acknowledged as a powerful tool for the study of root system architecture of plants growing in soil. In this paper, we improved the original root segmentation algorithm “Rootine” and present its succeeding version “Rootine v.2”. In addition to gray value information, Rootine algorithms are based on shape detection of cylindrical roots. Both algorithms are macros for the ImageJ software and are made freely available to the public. New features in Rootine v.2 are (i) a pot wall detection and removal step to avoid segmentation artefacts for roots growing along the pot wall, (ii) a calculation of the root average gray value based on a histogram analysis, (iii) an automatic calculation of thresholds for hysteresis thresholding of the tubeness image to reduce the number of parameters and (iv) a false negatives recovery based on shape criteria to increase root recovery. We compare the segmentation results of Rootine v.1 and Rootine v.2 with the results of root washing and subsequent analysis with WinRhizo. We use a benchmark dataset of maize roots (Zea mays L. cv. B73) grown in repacked soil for two scenarios with differing soil heterogeneity and image quality. Results We demonstrate that Rootine v.2 outperforms its preceding version in terms of root recovery and enables to match better the root diameter distribution data obtained with root washing. Despite a longer processing time, Rootine v.2 comprises less user-defined parameters and shows an overall greater usability. Conclusion The proposed method facilitates higher root detection accuracy than its predecessor and has the potential for improving high-throughput root phenotyping procedures based on X-ray computed tomography data analysis.

Cover crop influence on pore size distribution and biopore dynamics: Enumerating root and soil faunal effects

“…Roots and biopores were segmented according to the Rootine script ( Gao et al, 2019 ; Phalempin et al, 2021a ) for roots and Lucas et al (2019b) for biopores, respectively. Both scripts rely on the Tubeness plugin in Fiji to separate different sized tubular objects from the remaining irregularly shaped pore network.…”

“…These binaries were downscaled to 50 and 20% to apply Tubeness filters with σ -values between 1–4 and 2–30, respectively (step size =1). Misclassified objects were removed similar to Phalempin et al (2021a) after combining all elongated objects. A Distance Transform Watershed 3D operation was performed (MorphoLibJ, Version 1.4.3; Legland et al, 2016 ) on the tubeness result to separate root laterals from roots higher order before filtering misclassified, i.e., blob-like objects.…”

Soil texture and structure heterogeneity predominantly governs bulk density gradients around roots

“…Root segmentation was performed with a modified version of the root segmentation algorithm "Rootine v.2" (Phalempin et al, 2021). Rootine v.2 is a free macro available for the free image analysis software ImageJ (Schindelin et al, 2012).…”

“…After filtering, a step of edge enhancement was performed with the "Unsharp Mask" filter available in ImageJ. Then, a background removal step was applied via an "absolute difference transform" whose rationale and technical aspects are described by Phalempin et al (2021). The obtained images served as input for the root segmentation per se.…”

“…With the new approach, every image was visually analyzed and the diameter of the roots in the image was measured using the "Measure" tool available in ImageJ. The sigma values of the tubeness filter implemented in ImageJ were then calculated based on the measured root diameters according to the formalizing steps described elsewhere (Phalempin et al, 2021). The results of the tubeness filter were segmented using the "3D Hysteresis Thresholding" (Ollion et al, 2013) available in ImageJ.…”