LeMuR: Plant Root Phenotyping via Learned Multi-resolution Image Segmentation

LeMuR：通过学习的多分辨率图像分割进行植物根表型分析

• 批准号: BB/P026834/1
• 负责人: Michael Pound
• 金额: $ 18.25万
• 依托单位: University of Nottingham
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FP026834%2F1
• 关键词: LeMuR; Plant; Root; Phenotyping; via

Plant phenotyping - the measurement of quantitative data on plant structure and function from image and sensor data - is a key bottleneck holding back efforts towards global food security; that is, providing enough food for a growing population. The roots of food crops are clearly important for the development of the crop itself, yet root phenotyping is particularly challenging, as the roots grow in soil. Though methods of imaging roots in soil are emerging, they remain slow and expensive. Large-scale experiments are still performed using artificial growth media (gel, filter paper etc.) that allow the root to be imaged using conventional equipment. Analysis of the resulting images requires the root to be separated from its background and a structural description of the root architecture to be produced and presented to the user. But doing this fully automatically is a challenge, and most software tools written to date work with very specific sets of images, and tend to break if used outside of the scenarios they were designed for.In this proposal we will develop cutting-edge deep learning analysis approaches to build a much more general software tool. So-called deep approaches are revolutionising image analysis, with large companies developing similar techniques to analyse other image sets, such as for diagnosing medical conditions, to great effect. The proposed approach, LeMuR (Learned Multi-Resolution image segmentation), will exploit the common structure of root image analysis tasks, and recent advances in deep machine learning, to produce a flexible plant root phenotyping tool that can be easily adapted, without re-writing code, to new laboratory environments and imaging techniques.We propose two main developments. First, a software tool LeMuRoot which will be designed to work across a wide variety of root system data sets right out of the box, compared to the limited application of traditional tools. Second, a software framework (LeMuRLearn) to allow biologists themselves to adapt the tool to even more images beyond those that LeMuRoot was designed to work with. By supplying their own image data sets annotated using a novel user interface which will form part of LeMuRLearn, biologists will be able to re-train the core model underlying the tools, allowing them to improve the quality of results for their particular data. In a further novel process, biologists will be able to seamlessly share their newly trained tool with the community, which in turn can be used as a base for further development. This will allow LeMuRLearn to incrementally improve over time, and for it to use different underlying models for a wider variety of image data sets than was conceived of at initial release.This is exciting for two main reasons. First, previous development of software tools has by necessity been limited to computer scientists who are capable programmers - here we put the continued development of the tool in the hands of the biology community themselves. Second, by sharing the core model underlying the tool (called the LeMuRNet), biologists can share with the community without fear of sharing raw data or results.Combined with hiding the computational complexity of both the analysis and evolution process behind an accessible user interface, the potential to disrupt the current process for image analysis tool development and use with plant science (and beyond) is high.

植物表型——根据图像和传感器数据测量植物结构和功能的定量数据——是阻碍全球粮食安全努力的一个关键瓶颈；也就是说，为不断增长的人口提供足够的食物。粮食作物的根显然对于作物本身的发育很重要，但根表型分析尤其具有挑战性，因为根生长在土壤中。尽管对土壤中的根进行成像的方法正在出现，但它们仍然缓慢且昂贵。大规模实验仍然使用人工生长介质（凝胶、滤纸等）进行，这些介质允许使用传统设备对根部进行成像。对结果图像的分析需要将根与其背景分离，并生成根体系结构的结构描述并将其呈现给用户。但完全自动化地做到这一点是一个挑战，迄今为止编写的大多数软件工具都适用于非常特定的图像集，如果在其设计的场景之外使用，则往往会崩溃。在本提案中，我们将开发尖端的深度学习分析方法来构建更通用的软件工具。所谓的深度方法正在彻底改变图像分析，大公司开发类似的技术来分析其他图像集，例如诊断医疗状况，取得了巨大的效果。所提出的方法 LeMuR（学习型多分辨率图像分割）将利用根图像分析任务的通用结构以及深度机器学习的最新进展，来生成灵活的植物根表型分析工具，无需重新编写代码即可轻松适应新的实验室环境和成像技术。我们提出了两个主要发展方向。首先，软件工具 LeMuRoot 被设计为开箱即用，可以在各种根系统数据集上工作，而传统工具的应用有限。其次，软件框架 (LeMuRLearn) 允许生物学家自己调整该工具，以适应超出 LeMuRoot 设计处理范围的更多图像。通过提供自己的图像数据集，并使用新颖的用户界面（将构成 LeMuRLearn 的一部分）进行注释，生物学家将能够重新训练工具底层的核心模型，从而提高特定数据的结果质量。在进一步新颖的过程中，生物学家将能够与社区无缝分享他们新训练的工具，而这些工具又可以用作进一步开发的基础。这将使 LeMuRLearn 随着时间的推移逐步改进，并使其能够使用不同的底层模型来处理比最初发布时设想的更广泛的图像数据集。这令人兴奋的主要原因有两个。首先，以前软件工具的开发必然仅限于有能力的程序员的计算机科学家——在这里，我们将工具的持续开发交给了生物学界自己。其次，通过共享该工具（称为 LeMuRNet）背后的核心模型，生物学家可以与社区共享，而不必担心共享原始数据或结果。再加上将分析和进化过程的计算复杂性隐藏在可访问的用户界面后面，破坏当前图像分析工具开发和植物科学（及其他领域）使用过程的潜力很大。

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• 发表时间: 2022-03-15
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Predicting Plant Growth from Time-Series Data Using Deep Learning

• DOI: 10.3390/rs13030331
• 发表时间: 2021-02-01
• 期刊: REMOTE SENSING
• 影响因子: 5
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• DOI: 10.1101/709147
• 发表时间: 2019-07
• 期刊: GigaScience
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• DOI: 10.1016/j.copbio.2018.06.002
• 发表时间: 2019-03
• 期刊: Current opinion in biotechnology
• 影响因子: 7.7
• 作者: Atkinson JA;Pound MP;Bennett MJ;Wells DM
• 通讯作者: Wells DM