RootNav: Navigating Images of Complex Root Architectures
RootNav: Navigating Images of Complex Root Architectures
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DOI：
10.1104/pp.113.221531
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发表时间：
2013-08-01
期刊：
影响因子：
7.4
通讯作者：
Pridmore, Tony
中科院分区：
文献类型：
--
作者：
Pound, Michael P.;French, Andrew P.;Pridmore, Tony
我们提出了一种新的图像分析工具，该工具允许对一系列植物物种的复杂根系结构进行半自动量化，并以各种方式进行成像。RootNav的自动组件采用自顶向下的方法，利用强大的期望最大化分类算法检查输入图像的区域，计算给定像素对应于根的可能性。这些信息被用作根检测和量化的优化方法的基础，该方法有效地将根模型拟合到图像数据中。由此产生的用户体验类似于在驾驶员的卫星导航系统上定义路线：RootNav对从种子点到根顶点的路径进行初始优化估计，用户可以使用可视化方法轻松直观地改进结果。该方法在冬小麦（Triticum aestivum）图像上进行了评估（并在拟南芥（Arabidopsis thaliana）、甘蓝型油菜（Brassica napus）和水稻（Oryza sativa）上进行了验证），并将结果与人工分析进行了比较。对四个典型性状进行了计算，结果表明不同小麦品种间存在明显差异。然而，RootNav提供了所需的结构信息，以支持提取更广泛的生物学相关措施。提供了一个单独的查看器工具来从RootNav的核心表示中恢复一组丰富的体系结构特征。
We present a novel image analysis tool that allows the semiautomated quantification of complex root system architectures in a range of plant species grown and imaged in a variety of ways. The automatic component of RootNav takes a top-down approach, utilizing the powerful expectation maximization classification algorithm to examine regions of the input image, calculating the likelihood that given pixels correspond to roots. This information is used as the basis for an optimization approach to root detection and quantification, which effectively fits a root model to the image data. The resulting user experience is akin to defining routes on a motorist's satellite navigation system: RootNav makes an initial optimized estimate of paths from the seed point to root apices, and the user is able to easily and intuitively refine the results using a visual approach. The proposed method is evaluated on winter wheat (Triticum aestivum) images (and demonstrated on Arabidopsis [Arabidopsis thaliana], Brassica napus, and rice [Oryza sativa]), and results are compared with manual analysis. Four exemplar traits are calculated and show clear illustrative differences between some of the wheat accessions. RootNav, however, provides the structural information needed to support extraction of a wider variety of biologically relevant measures. A separate viewer tool is provided to recover a rich set of architectural traits from RootNav's core representation.