Cai Jinhai, Zeng Zhanghui, Connor Jason N, Huang Chun Yuan, Melino Vanessa, Kumar Pankaj, Miklavcic Stanley J
Phenomics and Bioinformatics Research Centre, University of South Australia, Mawson Lakes SA 5095, Australia Australian Centre for Plant Functional Genomics, University of Adelaide, Hartley Grove, Urrbrae SA 5064, Australia.
Australian Centre for Plant Functional Genomics, University of Adelaide, Hartley Grove, Urrbrae SA 5064, Australia.
J Exp Bot. 2015 Nov;66(21):6551-62. doi: 10.1093/jxb/erv359. Epub 2015 Jul 29.
This paper outlines a numerical scheme for accurate, detailed, and high-throughput image analysis of plant roots. In contrast to existing root image analysis tools that focus on root system-average traits, a novel, fully automated and robust approach for the detailed characterization of root traits, based on a graph optimization process is presented. The scheme, firstly, distinguishes primary roots from lateral roots and, secondly, quantifies a broad spectrum of root traits for each identified primary and lateral root. Thirdly, it associates lateral roots and their properties with the specific primary root from which the laterals emerge. The performance of this approach was evaluated through comparisons with other automated and semi-automated software solutions as well as against results based on manual measurements. The comparisons and subsequent application of the algorithm to an array of experimental data demonstrate that this method outperforms existing methods in terms of accuracy, robustness, and the ability to process root images under high-throughput conditions.
本文概述了一种用于植物根系精确、详细且高通量图像分析的数值方案。与现有专注于根系平均特征的根系图像分析工具不同，本文提出了一种基于图形优化过程的新颖、全自动且稳健的方法，用于详细表征根系特征。该方案首先区分主根和侧根，其次对每个识别出的主根和侧根的广泛根系特征进行量化。第三，它将侧根及其属性与侧根所发出的特定主根相关联。通过与其他自动化和半自动化软件解决方案进行比较，并与基于手动测量的结果进行对比，评估了该方法的性能。算法与一系列实验数据的比较及后续应用表明，该方法在准确性、稳健性以及在高通量条件下处理根系图像的能力方面优于现有方法。
