Background Micro-computed tomography (μCT) bring a new opportunity to accurately quantify micro phenotypic traits of maize stem, also provide comparable benchmark to evaluate its dynamic development at the different growth stages. The progressive accumulation of stem biomass brings manifest structure changes of maize stem and vascular bundles, which are closely related with maize varietal characteristics and growth stages. Thus, micro-phenotyping (μPhenotyping) of maize stems is not only valuable to evaluate bio-mechanics and water-transport performance of maize, but also yield growth-based traits for quantitative traits loci (QTL) and functional genes location in molecular breeding. Result In this study, maize stems of 20 maize cultivars and two growth stages were imaged using μCT scanning technology. According to the observable differences of maize stems from the elongation and tasseling stages, function zones of maize stem were firstly defined to describe the substance accumulation of maize stems. And then a set of image-based μPhenotyping pipelines were implemented to quantify maize stem and vascular bundles at the two stages. The coefficient of determination (R2) of counting vascular bundles was higher than 0.95. Based on the uniform contour representation, intensity-related, geometry-related and distribution-related traits of vascular bundles were respectively evaluated in function zones and structure layers. And growth-related traits of the slice, epidermis, periphery and inner zones were also used to describe the dynamic growth of maize stem. Statistical analysis demonstrated the presented method was suitable to the phenotyping analysis of maize stem for multiple growth stages. Conclusions The novel descriptors of function zones provide effective phenotypic references to quantify the differences between growth stages; and the detection and identification of vascular bundles based on function zones are more robust to determine the adaptive image analysis pipeline. Developing robust and effective image-based phenotyping method to assess the traits of stem and vascular bundles, is highly relevant for understanding the relationship between maize phenomics and genomics.

中文翻译：

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在伸长和抽穗阶段使用微型计算机断层扫描对玉米茎进行表型分析。

背景微计算机断层扫描（μCT）为准确量化玉米茎的微观表型性状提供了新的机会，也为评估其在不同生长阶段的动态发展提供了可比的基准。茎秆生物量的逐步积累带来了玉米茎秆和维管束结构的明显变化，这些变化与玉米品种特征和生长阶段密切相关。因此，玉米茎的微表型分析（μPhenotyping）不仅对评估玉米的生物力学和水分运输性能具有重要价值，而且对分子育种中数量性状基因座（QTL）和功能基因定位的产量性状具有重要意义。结果本研究采用μCT扫描技术对20个玉米品种和两个生长阶段的玉米茎秆进行成像。根据玉米茎秆在伸长和抽穗阶段可观察到的差异，首先定义了玉米茎秆功能区来描述玉米茎秆的物质积累。然后实施了一组基于图像的 μPhenotyping 管道来量化两个阶段的玉米茎和维管束。计数维管束的决定系数（R2）高于0.95。基于统一的轮廓表示，分别在功能区和结构层评估维管束的强度相关、几何相关和分布相关特征。切片、表皮、外围和内部区域的生长相关性状也被用来描述玉米茎的动态生长。统计分析表明，该方法适用于玉米茎的多个生长阶段的表型分析。结论 新的功能区描述符为量化生长阶段之间的差异提供了有效的表型参考；并且基于功能区的血管束检测和识别对于确定自适应图像分析流水线具有更强的鲁棒性。开发稳健有效的基于图像的表型方法来评估茎和维管束的性状，对于理解玉米表型​​组学和基因组学之间的关系高度相关。并且基于功能区的血管束检测和识别对于确定自适应图像分析流水线具有更强的鲁棒性。开发稳健有效的基于图像的表型方法来评估茎和维管束的性状，对于理解玉米表型​​组学和基因组学之间的关系高度相关。并且基于功能区的血管束检测和识别对于确定自适应图像分析流水线具有更强的鲁棒性。开发稳健有效的基于图像的表型方法来评估茎和维管束的性状，对于理解玉米表型​​组学和基因组学之间的关系高度相关。