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

植物表型--根据图像和传感器数据测量植物结构和功能的量化数据--是阻碍全球粮食安全努力的关键瓶颈；即为不断增长的人口提供足够的食物。粮食作物的根显然对作物本身的发展很重要，但根的表型尤其具有挑战性，因为根在土壤中生长。尽管对土壤中的根进行成像的方法正在涌现，但它们仍然缓慢且昂贵。大型实验仍在使用人工生长介质(凝胶、滤纸等)进行。这使得可以使用传统设备对牙根进行成像。对结果图像的分析需要将根部与其背景分开，并生成根部结构的结构描述并呈现给用户。但完全自动执行此操作是一项挑战，而且到目前为止编写的大多数软件工具都使用非常特定的图像集，如果在它们设计的场景之外使用，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，以构建更通用的软件工具。所谓的深度方法正在给图像分析带来革命性的变化，大公司开发类似的技术来分析其他图像集，例如用于诊断医疗条件，效果很好。Lemur(学习多分辨率图像分割)方法将利用根系图像分析任务的共同结构和深度机器学习的最新进展，产生一个灵活的植物根系表型工具，无需重写代码，即可轻松适应新的实验室环境和成像技术。首先，一个软件工具LeMuRoot，与传统工具的有限应用相比，它将被设计为开箱即用，可以在广泛的根系统数据集上工作。第二，一个软件框架(LeMuRLearn)，允许生物学家自己调整该工具，以适应更多的图像，而不是LeMuRoot设计的图像。通过提供他们自己的使用新的用户界面注释的图像数据集，生物学家将能够重新训练工具背后的核心模型，使他们能够提高他们特定数据的结果质量。在一个更新颖的过程中，生物学家将能够与社区无缝地分享他们新培训的工具，这反过来又可以作为进一步开发的基础。这将允许LeMuRLain随着时间的推移逐步改进，并使其对比最初发布时设想的更广泛的图像数据集使用不同的底层模型。这是令人兴奋的，主要有两个原因。首先，以前软件工具的开发必然局限于有能力的程序员的计算机科学家--在这里，我们将工具的继续开发交到生物界自己的手中。其次，通过共享该工具的核心模型(称为LeMuRNet)，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，扰乱当前图像分析工具开发和与植物科学(以及其他领域)使用的过程的可能性很高。

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.