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: 1759796
• 负责人: Christopher Topp
• 金额: $ 19.8万
• 依托单位: Donald Danforth Plant Science Center
• 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=1759796&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，在植物的发育中起着许多关键作用，如吸收水分和养分，提供锚定，稳定土壤。这些功能反过来又与根架构密切相关。根构型的量化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一个关键组成部分。3D成像的最新进展（例如，CT和MRI）使得在自然生长环境中捕获3D根部结构并监测其随时间的生长成为可能。不幸的是，成像技术的潜力在很大程度上受到了缺乏有效的计算工具来解释图像和提取生物学见解的阻碍。该项目将由圣路易斯地区三个研究所的计算机科学家，数学家和生物学家组成的小组进行，旨在开发高效和强大的计算方法，用于从3D图像中自动分析根结构。这项研究比目前最前沿的表型数据收集领先了一步，我们将如何获得生长根系的准确表示，从而深入了解植物表型。该团队致力于在项目过程中为十多名学生提供培训，利用两个研究所现有的NSF REU项目。该团队不仅将在研究社区内开展外展活动，还将在圣路易斯地区开展本地活动，重点是小学。从3D图像中提取根系结构涉及许多具有技术挑战性的任务，包括从分割图像中推断单个根系，重建其分支结构，并在分割图像的时间序列中跟踪结构。本研究借鉴并扩展了计算机图形学和计算几何学的方法来解决这些任务。具体来说，这项研究将开发三种新的方法。给定一个充满噪声的根分割，第一类方法产生一个曲线骨架，捕获根系的拓扑结构和分支结构。然后，第二个类使用曲线骨架自动推断架构组件，如根层次结构和类型。第三类改进了前两类算法的准确性，利用一系列的分割，并进一步注释的根结构与时间函数。这些算法能够提取详细的根系性状进行根系表型分析，算法和性状都将通过一套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外，还将开发一个图形软件原型，该软件提供快速和交互式的手段来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，并免费分发给研究社区。该奖项反映了NSF的法定使命，并被认为值得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来支持。

项目成果

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

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.

Constructing monotone homotopies and sweepouts

构造单调同伦和清除

• 发表时间: 2021
• 期刊: Journal of differential geometry
• 影响因子: 2.5
• 作者: Chambers, E.

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

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