Phalempin Maxime, Lippold Eva, Vetterlein Doris, Schlüter Steffen
Department of Soil System Science, Helmholtz Centre for Environmental Research GmbH-UFZ, Halle, Germany.
Martin-Luther-University Halle-Wittenberg, Institute of Agricultural and Nutritional Sciences, Halle, Germany.
Plant Methods. 2021 Apr 8;17(1):39. doi: 10.1186/s13007-021-00735-4.
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.
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.
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.
X射线计算机断层扫描被公认为是研究生长在土壤中的植物根系结构的有力工具。在本文中，我们改进了原始的根系分割算法“Rootine”，并展示了其后续版本“Rootine v.2”。Rootine算法除了基于灰度值信息外，还基于圆柱状根系的形状检测。这两种算法都是ImageJ软件的宏，并且向公众免费提供。Rootine v.2的新特性包括：（i）一个盆壁检测和去除步骤，以避免对沿盆壁生长的根系产生分割伪像；（ii）基于直方图分析计算根系平均灰度值；（iii）自动计算用于管道图像滞后阈值处理的阈值，以减少参数数量；（iv）基于形状标准的假阴性恢复，以提高根系恢复率。我们将Rootine v.1和Rootine v.2的分割结果与根系冲洗及随后使用WinRhizo进行分析的结果进行比较。我们使用了在重新装填的土壤中生长的玉米根系（Zea mays L. cv. B73）的基准数据集，用于两种具有不同土壤异质性和图像质量的场景。
我们证明，Rootine v.2在根系恢复方面优于其先前版本，并且能够更好地匹配通过根系冲洗获得的根系直径分布数据。尽管处理时间更长，但Rootine v.2包含的用户定义参数更少，并且总体可用性更高。
所提出的方法比其前身具有更高的根系检测精度，并且具有基于X射线计算机断层扫描数据分析改进高通量根系表型分析程序的潜力。
