Centre for Plant Integrative Biology, University of Nottingham, Nottingham LE12 5RD, United Kingdom.
Plant Physiol. 2012 Feb;158(2):561-9. doi: 10.1104/pp.111.186221. Epub 2011 Dec 21.
X-ray microcomputed tomography (μCT) is an invaluable tool for visualizing plant root systems within their natural soil environment noninvasively. However, variations in the x-ray attenuation values of root material and the overlap in attenuation values between roots and soil caused by water and organic materials represent major challenges to data recovery. We report the development of automatic root segmentation methods and software that view μCT data as a sequence of images through which root objects appear to move as the x-y cross sections are traversed along the z axis of the image stack. Previous approaches have employed significant levels of user interaction and/or fixed criteria to distinguish root and nonroot material. RooTrak exploits multiple, local models of root appearance, each built while tracking a specific segment, to identify new root material. It requires minimal user interaction and is able to adapt to changing root density estimates. The model-guided search for root material arising from the adoption of a visual-tracking framework makes RooTrak less sensitive to the natural ambiguity of x-ray attenuation data. We demonstrate the utility of RooTrak using μCT scans of maize (Zea mays), wheat (Triticum aestivum), and tomato (Solanum lycopersicum) grown in a range of contrasting soil textures. Our results demonstrate that RooTrak can successfully extract a range of root architectures from the surrounding soil and promises to facilitate future root phenotyping efforts.
X 射线显微计算机断层扫描（μCT）是一种非常有价值的工具，可在不破坏植物根系自然土壤环境的情况下对其进行可视化。然而，由于水和有机物质的存在，根系材料的 X 射线衰减值的变化以及根系和土壤之间的衰减值的重叠，对数据恢复构成了重大挑战。我们报告了自动根系分割方法和软件的开发，该方法将 μCT 数据视为一系列图像，通过这些图像，随着沿图像堆栈的 z 轴遍历 x-y 横截面，根对象似乎在移动。以前的方法采用了大量的用户交互和/或固定标准来区分根和非根材料。RooTrak 利用了多个局部的根外观模型，每个模型都是在跟踪特定段的同时构建的，以识别新的根材料。它只需要最少的用户交互，并能够适应不断变化的根密度估计。采用视觉跟踪框架进行的根材料的模型引导搜索使 RooTrak 对 X 射线衰减数据的自然歧义不那么敏感。我们使用在一系列不同土壤质地中生长的玉米（Zea mays）、小麦（Triticum aestivum）和番茄（Solanum lycopersicum）的 μCT 扫描来演示 RooTrak 的实用性。我们的结果表明，RooTrak 可以成功地从周围土壤中提取出一系列根系结构，并有望促进未来的根系表型研究。
