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Leaf Vein Network Geometry Can Predict Levels of Resource Transport, Defence, and 21 Mechanical Support That Operate at Different Spatial Scales

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Leaf Vein Network Geometry Can Predict Levels of Resource Transport, Defence, and 21 Mechanical Support That Operate at Different Spatial Scales bioRxiv preprint doi: https://doi.org/10.1101/2020.07.19.206631; this version posted July 19, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 1 Automated and accurate segmentation of leaf venation networks via deep 2 learning 3 4 H. Xu1+, B. Blonder2*+, M. Jodra2, Y. Malhi2, and M.D. Fricker3†+. 5 6 1Institute of Biomedical Engineering, Department of Engineering Science, Old Road Campus 7 Research Building, University of Oxford, Oxford, OX3 7DQ, UK 8 2Environmental Change Institute, School of Geography and the Environment, University of 9 Oxford, South Parks Road, Oxford, OX1 3QY, UK 10 3Department of Plant Sciences, University of Oxford, South Parks Road, Oxford, OX1 3RB, UK 11 12 *: Current address: Department of Environmental Science, Policy, and Management, University 13 of California, 120 Mulford Hall, Berkeley, California, 94720, USA 14 15 +: These authors contributed equally 16 †: Author for correspondence: [email protected] 17 Word count: Total 6,490 Introduction 1,176 Materials and Methods 3,147 Results 1,426 Discussion 662 Acknowledgements 79 Number of Figures: 9 Number of tables: 0 Number of supplementary 9 figures: Number of supplementary 7 tables: 18 1 bioRxiv preprint doi: https://doi.org/10.1101/2020.07.19.206631; this version posted July 19, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 19 Summary 20 • Leaf vein network geometry can predict levels of resource transport, defence, and 21 mechanical support that operate at different spatial scales. However, it is challenging to 22 quantify network architecture across scales, due to the difficulties both in segmenting 23 networks from images, and in extracting multi-scale statistics from subsequent network 24 graph representations. 25 • Here we develop deep learning algorithms using convolutional neural networks (CNNs) 26 to automatically segment leaf vein networks. Thirty-eight CNNs were trained on subsets 27 of manually-defined ground-truth regions from >700 leaves representing 50 southeast 28 Asian plant families. Ensembles of 6 independently trained CNNs were used to segment 29 networks from larger leaf regions (~100 mm2). Segmented networks were analysed using 30 hierarchical loop decomposition to extract a range of statistics describing scale transitions 31 in vein and areole geometry. 32 • The CNN approach gave a precision-recall harmonic mean of 94.5% ± 6%, 33 outperforming other current network extraction methods, and accurately described the 34 widths, angles, and connectivity of veins. Multi-scale statistics then enabled identification 35 of previously-undescribed variation in network architecture across species. 36 • We provide a LeafVeinCNN software package to enable multi-scale quantification of leaf 37 vein networks, facilitating comparison across species and exploration of the functional 38 significance of different leaf vein architectures. 39 Keywords 40 Biological network analysis; Convolutional neural network; Deep learning; Hierarchical loop 41 decomposition; Leaf trait; Leaf venation network; Network scaling; Spatial transportation 42 network. 43 44 2 bioRxiv preprint doi: https://doi.org/10.1101/2020.07.19.206631; this version posted July 19, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 45 Introduction 46 Plant leaves are structured by a vein network that ranges in geometry from dendritic fans with 47 few branches and no loops (e.g. Ginkgo biloba) to hierarchical forms with many loops (e.g. Acer 48 saccharum) (Trivett & Pigg, 1996; Roth-Nebelsick et al., 2001). The vein network has multiple 49 roles that include transport of water, nutrients and sugars through the xylem and phloem tissues 50 (Brodribb et al., 2010; Carvalho et al., 2018; Katifori, 2018), controlled deformation during bud 51 burst (Niklas, 1999), and mechanical support and resistance to damage in the mature leaf (Sack & 52 Scoffoni, 2013; Sharon & Sahaf, 2018). Measurements of vein architecture have provided 53 insights into leaf development (Kang & Dengler, 2004) and evolution (Boyce et al., 2009; 54 Brodribb & Feild, 2010), prediction of leaf carbon and water fluxes (Sack & Frole, 2006; 55 Brodribb et al., 2007; Brodribb et al., 2010), prediction of environmental and stress tolerances of 56 species (de Boer et al., 2012; Blonder & Enquist, 2014; Brodribb et al., 2016), and reconstruction 57 of paleo-environments from leaf fossils (Manze, 1967; Uhl & Mosbrugger, 1999; Blonder et al., 58 2014). Venation networks are also relevant to test theories of optimal branching structure and 59 transportation efficiency for different network architectures (Pelletier & Turcotte, 2000; Price et 60 al., 2012; Price et al., 2014; Price & Weitz, 2014), particularly in response to fluctuating loads or 61 robustness to damage (Dodds, 2010; Katifori et al., 2010; Katifori, 2018). 62 63 Prediction of function is based on statistics estimated from images of vein networks (Roth- 64 Nebelsick et al., 2001). Networks have veins of different orders, varying from primary veins 65 (attached to the petiole), to secondary (attached to the primary veins), and continuing until the 66 ultimate veins are reached. The vein density (vein length per unit area) at each order, or across all 67 orders, is therefore a key statistic (Uhl & Mosbrugger, 1999; Sack & Scoffoni, 2013). 68 Measurement of the distribution of vein radii is required to determine the vein order (Price et al., 69 2012), which is itself challenging. Alternatively vein orders can be extracted using hierarchical 70 vein classification methods (Gan et al., 2019). If vein radii can be extracted reliably, they can also 71 be used to estimate network construction costs (John et al., 2017), or to predict maximum fluxes 72 and resource flow rates (Brodribb et al., 2007; McKown et al., 2010). Many networks also 73 contain loops that partition the lamina into increasingly small areoles. For higher vein orders, 74 qualitative terms to describe patterning, such as craspedodromous or camptodromous, have 75 traditionally been used (Hickey, 1979; Ellis et al., 2009). Quantitatively, the number of areoles 76 per unit area provides a statistic for the amount of ‘loopiness’ (Blonder et al., 2011). More 77 detailed analysis of the nesting of loops within higher-order loops can be achieved using 78 hierarchical loop decomposition (HLD), which constructs a binary branching tree representing the 3 bioRxiv preprint doi: https://doi.org/10.1101/2020.07.19.206631; this version posted July 19, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 79 order in which adjacent areoles become connected as the intervening veins are removed (Katifori 80 et al., 2010; Katifori & Magnasco, 2012; Ronellenfitsch et al., 2015; Katifori, 2018). HLD 81 provides a range of metrics such as the Horton-Strahler index, bifurcation ratio and subtree 82 asymmetry by analogy to river network analysis (Katifori & Magnasco, 2012; Mileyko et al., 83 2012; Ronellenfitsch et al., 2015; Katifori, 2018). Some venation networks contain freely-ending 84 veins (FEVs) that do not form anastomoses with other veins, but which may help with efficient 85 supply of water (Fiorin et al., 2016), or possibly transport of sugars (Carvalho et al., 2018). As 86 such, density estimates of branching end-points or areole perimeter/area ratios (Kang & Dengler, 87 2004; Blonder et al., 2018) are also useful. Finally, , the distribution of branching angles and 88 radius or length ratios at vein branching points (Bohn et al., 2002), feed into theories and models 89 of flow efficiency, network development and architecture (Pelletier & Turcotte, 2000; Price et al., 90 2012; Price et al., 2014). 91 92 Calculating these metrics requires accurate segmentation of the venation network over a range of 93 scales. The finest veins are typically around 10-20 µm diameter, whilst the largest veins can reach 94 mm width in a lamina up to m2 in area. To capture the fine veins, networks are typically imaged 95 after chemical clearing and staining using light microscopy or flat-bed scanning (Pérez- 96 Harguindeguy et al., 2013). X-ray imaging can also achieve high resolution with no tissue 97 preparation, but requires access to suitable equipment (Wing, 1992; Blonder et al., 2012; 98 Schneider et al., 2018). Regardless of the imaging method, accurate segmentation of the network 99 from the image has been challenging. Images often have limited or uneven contrast, or contain 100 unavoidable artifacts caused by other tissues and cell types, such as trichomes or glands. 101 Furthermore, field material often includes damaged regions. As a result, simple intensity- 102 thresholding is rarely adequate to yield useful segmentations. Several computer programs have 103 addressed this challenge, typically using local or global enhancement filters
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