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

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.

An Improved Method for the Segmentation of Roots from X-ray Computed Tomography 3D Images: Rootine v.2

BackgroundX-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 grey 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 (1) a pot wall detection and removal step to avoid segmentation artefacts for roots growing along the pot wall, (2) a calculation of the root average grey value based on a histogram analysis, (3) an automatic calculation of thresholds for hysteresis thresholding of the tubeness image to reduce the number of parameters and (4) 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. ResultsWe 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. ConclusionThe 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 CT data analysis.

Development and Evaluation of a Large-volume Rotary Root Separator

Isolation of plant roots from soil or substrate for biomass measurement is time-consuming and can be a limiting factor influencing experimental designs, especially with mature woody plants. An electric-powered root separator was developed that sped sample preparation for root dry mass determination with a capacity of 40 L of container substrate or 32 kg of sandy soil. No water was required for machine operation and an estimated fourfold reduction in total processing time was achieved. Extent of root recovery was quantified by processing five woody plant species grown in two different container substrates and in soil, resulting in a minimum yield of 98%.