Automated and accurate segmentation of leaf venation networks via deep learning
Automated and accurate segmentation of leaf venation networks via deep learning
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DOI：
10.1111/nph.16923
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发表时间：
2020-10-10
期刊：
影响因子：
9.4
通讯作者：
Fricker, Mark
中科院分区：
文献类型：
--
作者：
Xu, Hao;Blonder, Benjamin;Fricker, Mark
叶脉网络的几何形状可以预测在不同空间尺度上运行的资源运输、防御和机械支持的水平。然而，由于从图像分割网络和从后续网络图表示中提取多尺度统计数据的困难，跨尺度量化网络架构具有挑战性。在这里，我们使用卷积神经网络（CNN）开发了深度学习算法来自动分割叶脉网络。38个CNN在来自代表50个东南亚植物家族的>700片叶子的手动定义的地面实况区域的子集上进行了训练。使用六个独立训练的CNN的集合来分割来自较大叶区域的网络（c. 100 mm（2））。分段网络进行了分析，使用分层循环分解，以提取一系列的统计描述规模的静脉和蜂窝几何形状的转变。CNN方法给出了94.5% +/-6%的精确召回调和平均值，优于其他当前的网络提取方法，并准确描述了静脉的宽度，角度和连通性。然后，多尺度统计使以前未描述的跨物种的网络结构的变化的识别。我们提供了一个LeafVeinCNN软件包，使多尺度量化的叶脉网络，促进跨物种的比较和不同的叶脉架构的功能意义的探索。
Leaf vein network geometry can predict levels of resource transport, defence and mechanical support that operate at different spatial scales. However, it is challenging to quantify network architecture across scales due to the difficulties both in segmenting networks from images and in extracting multiscale statistics from subsequent network graph representations. Here we developed deep learning algorithms using convolutional neural networks (CNNs) to automatically segment leaf vein networks. Thirty-eight CNNs were trained on subsets of manually defined ground-truth regions from >700 leaves representing 50 southeast Asian plant families. Ensembles of six independently trained CNNs were used to segment networks from larger leaf regions (c. 100 mm(2)). Segmented networks were analysed using hierarchical loop decomposition to extract a range of statistics describing scale transitions in vein and areole geometry. The CNN approach gave a precision-recall harmonic mean of 94.5% +/- 6%, outperforming other current network extraction methods, and accurately described the widths, angles and connectivity of veins. Multiscale statistics then enabled the identification of previously undescribed variation in network architecture across species. We provide aLeafVeinCNNsoftware package to enable multiscale quantification of leaf vein networks, facilitating the comparison across species and the exploration of the functional significance of different leaf vein architectures.