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 The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.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 The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.