Automated and accurate segmentation of leaf venation networks via deep learning

Journal article

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 multi‐scale statistics from subsequent network graph representations. Here we develop 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 6 independently trained CNNs were used to segment networks from larger leaf regions (~100 mm2). 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. Multi‐scale statistics then enabled identification of previously undescribed variation in network architecture across species. We provide a LeafVeinCNN software package to enable multi‐scale quantification of leaf vein networks, facilitating comparison across species and exploration of the functional significance of different leaf vein architectures.

Authors: Xu, H; Blonder, B; Jodra, M; Malhi, Y; Fricker, MD

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Wiley
• Journal: New Phytologist
• Volume: 229
• Issue: 1
• Pages: 631-648
• Publication date: 2020-10-10
• Acceptance date: 2020-09-10
• DOI: 10.1101/2020.07.19.206631
• EISSN: 1469-8137
• ISSN: 0028-646X
• Language: English
• Keywords: leaf venation network; deep learning; leaf trait; convolutional neural network; FFR; biological network analysis; spatial transportation network; network scaling; hierarchical loop decomposition