Fast and efficient root phenotyping via pose estimation.
Fast and efficient root phenotyping via pose estimation.
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通过姿态估计快速有效地进行根表型分析。
DOI：
10.1101/2023.11.20.567949
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Talgo,Ave
中科院分区：
文献类型：
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作者：
Berrigan,ElizabethM;Wang,Lin;Carrillo,Hannah;Echegoyen,Kimberly;Kappes,Mikayla;Torres,Jorge;Ai-Perreira,Angel;McCoy,Erica;Shane,Emily;Copeland,CharlesD;Ragel,Lauren;Georgousakis,Charidimos;Lee,Sanghwa;Reynolds,Dawn;Talgo,Ave
图像分割通常用于估计植物及其外部结构的位置和形状。然后使用分割模板来定位感兴趣的地标，并计算与植物表型相对应的其他几何特征。尽管基于分割的方法很流行，但它是费力的(需要大量的注释来训练)，而且容易出错(派生的几何特征对实例掩码完整性很敏感)。在这里，我们提出了一种无需分割的方法，它利用基于深度学习的地标检测和分组，也称为姿势估计。我们使用一种最初为动物动作捕捉开发的工具，名为Sleap(Social Leap Estiments Animal Posts)，用于自动检测植物根上不同的形态地标。通过使用多物种凝胶柱体成像系统，我们的方法可以可靠有效地恢复根系拓扑结构，精度高，注释样本少，速度快于基于分割的方法。为了利用这种基于里程碑的表示法来进行根表型分析，我们开发了一个可直接与现有的基于分段的分析软件相媲美的用于特征提取的Python库(Sleap-Roots)。我们表明，来自里程碑的根性状是高度准确的，并且可以用于常见的下游任务，包括基因型分类和非监督性状定位。综上所述，这项工作确立了基于姿态估计的植物表型的有效性和优势。为了促进这一易于使用的工具的采用并鼓励进一步的发展，我们在https://github.com/talmolab/sleap-roots和https://osf.io/k7j9g/.上提供Sleap-Root、所有培训数据、模型和特征提取代码
Image segmentation is commonly used to estimate the location and shape of plants and their external structures. Segmentation masks are then used to localize landmarks of interest and compute other geometric features that correspond to the plant’s phenotype. Despite its prevalence, segmentation-based approaches are laborious (requiring extensive annotation to train), and error-prone (derived geometric features are sensitive to instance mask integrity). Here we present a segmentation-free approach which leverages deep learning-based landmark detection and grouping, also known as pose estimation. We use a tool originally developed for animal motion capture called SLEAP (Social LEAP Estimates Animal Poses) to automate the detection of distinct morphological landmarks on plant roots. Using a gel cylinder imaging system across multiple species, we show that our approach can reliably and efficiently recover root system topology at high accuracy, few annotated samples, and faster speed than segmentation-based approaches. In order to make use of this landmark-based representation for root phenotyping, we developed a Python library (sleap-roots) for trait extraction directly comparable to existing segmentation-based analysis software. We show that landmark-derived root traits are highly accurate and can be used for common downstream tasks including genotype classification and unsupervised trait mapping. Altogether, this work establishes the validity and advantages of pose estimation-based plant phenotyping. To facilitate adoption of this easy-to-use tool and to encourage further development, we make sleap-roots, all training data, models, and trait extraction code available at: https://github.com/talmolab/sleap-roots and https://osf.io/k7j9g/.