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

根是植物“隐藏”的那一半，在植物的发育中起着许多关键作用，如吸收水分和养分，提供锚定和稳定土壤。这些功能反过来又与根体系结构密切相关。量化根构型不仅是植物科学的一个基本方面，而且是可持续农业作物育种的关键组成部分。3D成像（如CT和MRI）的最新进展使得在自然生长环境中捕获3D根结构并随时间监测其生长成为可能。不幸的是，由于缺乏有效的计算工具来解释图像和提取生物学见解，成像技术的潜力在很大程度上受到了阻碍。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，旨在开发有效而强大的计算方法，用于从3D图像中自动分析根结构。这项研究看起来比目前最先进的表型数据收集领先一步，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10多名学生提供培训，利用现有的两个研究所的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的当地开展外展活动，重点是小学。从3D图像中导出根结构涉及许多技术上具有挑战性的任务，包括从分割图像中推断单个根，重建其分支结构，以及在分割图像的时间序列中跟踪体系结构。本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。具体而言，该研究将开发三种新型方法。给定一个带有噪声的根分割，第一类方法产生一个曲线骨架，该骨架捕获了根系统的拓扑结构和分支结构。第二个类然后使用曲线骨架来自动推断体系结构组件，如根层次结构和类型。第三类通过使用一系列分割来提高前两类算法的准确性，并进一步使用时间函数注释根结构。这些算法能够提取详细的根性状用于根表型，并且算法和性状将通过一套具有代表性的真实世界成像数据进行评估。除了设计自动算法外，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。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.