Collaborative Research: ABI Innovation: Algorithms for recovering root architecture from 3D imaging

合作研究：ABI 创新：从 3D 成像恢复根结构的算法

• 批准号: 1759836
• 负责人: Tao Ju
• 金额: $ 26.41万
• 依托单位: Washington University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-07-01 至 2022-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1759836&HistoricalAwards=false
• 关键词: Collaborative; Research; ABI; Innovation; Algorithms

Roots, the "hidden" half of the plant, play many critical roles in the plant's development such as the uptake of water and nutrients, providing anchorage, and stabilizing the soil. These functions, in turn, are closely associated with the root architecture. Quantifying root architecture is not only a fundamental aspect of plant science but is a critical component in crop breeding for sustainable agriculture. Recent advances in 3D imaging (e.g., CT and MRI) have made it possible to capture 3D root structures in natural growing environments and monitor their growth over time. Unfortunately, the potential of the imaging techniques has been largely held back by the lack of effective computational tools for interpreting the images and distilling biological insights. This project, to be conducted by a group of computer scientists, mathematicians, and biologists across three institutes in the St. Louis region, aims at developing efficient and robust computational methods for automated analysis of root architecture from 3D images. The research looks a step ahead of the current cutting edge phenotype data collection, to how we will derive accurate representations of growing root systems, and therefore gain insight into the plant phenome. The team is committed to providing training to more than ten students over the course of the project, leveraging the existing NSF REU programs at two of the institutes. The team will pursue outreach activities not only within the research communities but also locally in the St. Louis area with a focus on grade schools.Deriving root architecture from 3D images involves a number of technically challenging tasks, including inferring individual roots from a segmented image, reconstructing their branching structure, and tracking the architecture in a time series of segmented images. This research draws from, and extends upon, methods from computer graphics and computational geometry to address these tasks. Specifically, the research will develop three novel classes of methods. Given a noise-ridden root segmentation, the first class of methods produces a curve skeleton that captures the topology and branching structure of the root system. The second class then uses the curve skeleton to automatically infer architectural components such as the root hierarchy and types. The third class improves the accuracy of the algorithms in the 1rst two classes by utilizing a sequence of segmentations and further annotates the root architecture with a time function. These algorithms enable the extraction of detailed root traits for root phenotyping, and both the algorithms and traits will be evaluated by a suite of representative real-world imaging data.Besides the design of automatic algorithms, a graphical software will be prototyped that offers fast and interactive means to inspect and edit the results produced by the algorithms. The software will be tested by biologists in the team and freely distributed to the research community.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

根是植物“隐藏”的一半，在植物的发育中发挥着许多关键作用，例如吸收水分和养分、提供锚定和稳定土壤。这些功能又与根架构密切相关。量化根结构不仅是植物科学的一个基本方面，也是可持续农业作物育种的关键组成部分。 3D 成像（例如 CT 和 MRI）的最新进展使得捕获自然生长环境中的 3D 根结构并监测其随时间的生长成为可能。不幸的是，由于缺乏有效的计算工具来解释图像和提炼生物学见解，成像技术的潜力在很大程度上受到限制。该项目由圣路易斯地区三个研究所的一组计算机科学家、数学家和生物学家进行，旨在开发高效且强大的计算方法，用于从 3D 图像自动分析根结构。这项研究在我们如何获得生长根系的准确表示，从而深入了解植物表型方面，比当前最前沿的表型数据收集领先了一步。该团队致力于利用其中两个研究所现有的 NSF REU 项目，在项目过程中为十多名学生提供培训。该团队不仅将在研究社区内开展推广活动，还将在圣路易斯地区开展推广活动，重点是小学。从 3D 图像导出根结构涉及许多技术上具有挑战性的任务，包括从分段图像推断单个根，重建它们的分支结构，并在分段图像的时间序列中跟踪架构。这项研究借鉴并扩展了计算机图形学和计算几何的方法来解决这些任务。具体来说，该研究将开发三类新颖的方法。给定充满噪声的根分割，第一类方法产生一个曲线骨架，捕获根系统的拓扑和分支结构。然后，第二个类使用曲线骨架自动推断架构组件，例如根层次结构和类型。第三类通过利用一系列分段来提高前两类算法的准确性，并进一步用时间函数注释根架构。这些算法能够提取详细的根性状以进行根表型分析，并且算法和性状都将通过一套具有代表性的真实世界成像数据进行评估。除了自动算法的设计之外，还将制作一个图形软件原型，该软件可以提供快速、准确的分析结果。交互式方式检查和编辑算法产生的结果。该软件将由团队中的生物学家进行测试，并免费分发给研究界。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Robust optimization for topological surface reconstruction

• DOI: 10.1145/3197517.3201348
• 发表时间: 2018-07
• 期刊: ACM Transactions on Graphics (TOG)
• 作者: R. Lazar;Nadav Dym;Y. Kushinsky;Zhiyang Huang;T. Ju;Y. Lipman

Comprehensive 3D phenotyping reveals continuous morphological variation across genetically diverse sorghum inflorescences

• DOI: 10.1111/nph.16533
• 发表时间: 2020-04-16
• 期刊: NEW PHYTOLOGIST
• 影响因子: 9.4
• 作者: Li, Mao;Shao, Mon-Ray;Topp, Christopher N.
• 通讯作者: Topp, Christopher N.

Characterizing 3D inflorescence architecture in grapevine using X-ray imaging and advanced morphometrics: implications for understanding cluster density

• DOI: 10.1093/jxb/erz394
• 发表时间: 2019-08
• 期刊: Journal of Experimental Botany
• 影响因子: 6.9
• 作者: Mao Li;Laura L. Klein;K. Duncan;N. Jiang;D. Chitwood;J. Londo;Allison J. Miller;C. Topp

Topological Simplification of Nested Shapes

• DOI: 10.1111/cgf.14611
• 发表时间: 2022-08
• 期刊: Computer Graphics Forum
• 影响因子: 2.5
• 作者: Dan Zeng;E. Chambers;D. Letscher;T. Ju

Some Heuristics for the Homological Simplification Problem

同调简化问题的一些启发式方法

• 发表时间: 2018
• 期刊: 30th Canadian Conference on Computational Geometry
• 作者: Chambers, Erin;Ju, Tao;Letscher, David;Li, Mao;Topp, Christopher;Yan, Yajie
• 通讯作者: Yan, Yajie