RootNav 2.0: Deep learning for automatic navigation of complex plant root architectures
RootNav 2.0: Deep learning for automatic navigation of complex plant root architectures
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DOI：
10.1101/709147
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发表时间：
2019-07
期刊：
影响因子：
9.2
通讯作者：
R. Yasrab;J. Atkinson;D. Wells;A. French;T. Pridmore;Michael P. Pound
中科院分区：
文献类型：
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作者：
R. Yasrab;J. Atkinson;D. Wells;A. French;T. Pridmore;Michael P. Pound
我们提出了一种新的图像分析方法，可以在不同的成像设置下从一系列植物物种中自动提取复杂的根系结构。在现代深度学习方法的驱动下，RootNav 2.0用极深的多任务卷积神经网络架构取代了以前的手动和半自动特征提取。该网络旨在明确地将局部像素信息与全局场景信息相结合，以便准确地分割高分辨率图像中的小根特征。此外，该网络同时定位种子、一阶和二阶根尖，驱动搜索算法在整个图像中寻找最优路径，在没有用户交互的情况下提取准确的结构。该方法在小麦（Triticum aestivum L.）幼苗试验图像上进行了评价。结果与通过原始RootNav工具进行的半自动分析进行了比较，显示出相当的准确性，速度提高了10倍。然后，我们通过迁移学习证明了网络适应不同植物物种的能力，当转移到拟南芥平板实验时，提供了类似的准确性。我们最后一次转移到从水培试验的甘蓝型油菜图像，尽管训练图像少得多，但仍然表现出良好的准确性。该工具以广泛接受的RSML标准输出根体系结构，为此存在许多分析包（http://rootsystemml.github）。Io /)，以及与其他自动测量工具兼容的分段掩码。
We present a new image analysis approach that provides fully-automatic extraction of complex root system architectures from a range of plant species in varied imaging setups. Driven by modern deep-learning approaches, RootNav 2.0 replaces previously manual and semi-automatic feature extraction with an extremely deep multi-task Convolutional Neural Network architecture. The network has been designed to explicitly combine local pixel information with global scene information in order to accurately segment small root features across high-resolution images. In addition, the network simultaneously locates seeds, and first and second order root tips to drive a search algorithm seeking optimal paths throughout the image, extracting accurate architectures without user interaction. The proposed method is evaluated on images of wheat (Triticum aestivum L.) from a seedling assay. The results are compared with semi-automatic analysis via the original RootNav tool, demonstrating comparable accuracy, with a 10-fold increase in speed. We then demonstrate the ability of the network to adapt to different plant species via transfer learning, offering similar accuracy when transferred to an Arabidopsis thaliana plate assay. We transfer for a final time to images of Brassica napus from a hydroponic assay, and still demonstrate good accuracy despite many fewer training images. The tool outputs root architectures in the widely accepted RSML standard, for which numerous analysis packages exist (http://rootsystemml.github.io/), as well as segmentation masks compatible with other automated measurement tools.