FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3042778999> ?p ?o ?g. }

• W3042778999 abstract "Summary 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 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. 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." @default.
• W3042778999 created "2020-07-23" @default.
• W3042778999 creator A5018160834 @default.
• W3042778999 creator A5020683576 @default.
• W3042778999 creator A5026839832 @default.
• W3042778999 creator A5032838018 @default.
• W3042778999 creator A5086539185 @default.
• W3042778999 date "2020-07-19" @default.
• W3042778999 modified "2023-10-06" @default.
• W3042778999 title "Automated and accurate segmentation of leaf venation networks via deep learning" @default.
• W3042778999 cites W1412973526 @default.
• W3042778999 cites W1524686487 @default.
• W3042778999 cites W1597338246 @default.
• W3042778999 cites W1897507705 @default.
• W3042778999 cites W1901129140 @default.
• W3042778999 cites W1903029394 @default.
• W3042778999 cites W1966716734 @default.
• W3042778999 cites W1970854707 @default.
• W3042778999 cites W1976030512 @default.
• W3042778999 cites W1996070955 @default.
• W3042778999 cites W1998849017 @default.
• W3042778999 cites W2001412060 @default.
• W3042778999 cites W2009042255 @default.
• W3042778999 cites W2013687876 @default.
• W3042778999 cites W2039367092 @default.
• W3042778999 cites W2057382575 @default.
• W3042778999 cites W2065435946 @default.
• W3042778999 cites W2071096658 @default.
• W3042778999 cites W2086377778 @default.
• W3042778999 cites W2086712816 @default.
• W3042778999 cites W2086935100 @default.
• W3042778999 cites W2087573070 @default.
• W3042778999 cites W2092184130 @default.
• W3042778999 cites W2101020813 @default.
• W3042778999 cites W2102852568 @default.
• W3042778999 cites W2103504761 @default.
• W3042778999 cites W2103589278 @default.
• W3042778999 cites W2103891192 @default.
• W3042778999 cites W2109633310 @default.
• W3042778999 cites W2109768110 @default.
• W3042778999 cites W2110107677 @default.
• W3042778999 cites W2112839390 @default.
• W3042778999 cites W2115413365 @default.
• W3042778999 cites W2119610202 @default.
• W3042778999 cites W2120369890 @default.
• W3042778999 cites W2123029610 @default.
• W3042778999 cites W2124351162 @default.
• W3042778999 cites W2125610532 @default.
• W3042778999 cites W2127932091 @default.
• W3042778999 cites W2128051865 @default.
• W3042778999 cites W2128060444 @default.
• W3042778999 cites W2129534965 @default.
• W3042778999 cites W2134159742 @default.
• W3042778999 cites W2135293965 @default.
• W3042778999 cites W2144664753 @default.
• W3042778999 cites W2147639276 @default.
• W3042778999 cites W2150098405 @default.
• W3042778999 cites W2151217944 @default.
• W3042778999 cites W2153946082 @default.
• W3042778999 cites W2161026278 @default.
• W3042778999 cites W2166800206 @default.
• W3042778999 cites W2169837286 @default.
• W3042778999 cites W2171330332 @default.
• W3042778999 cites W2172079090 @default.
• W3042778999 cites W2342079005 @default.
• W3042778999 cites W2470803522 @default.
• W3042778999 cites W2586941808 @default.
• W3042778999 cites W2588308167 @default.
• W3042778999 cites W2612772464 @default.
• W3042778999 cites W2762200112 @default.
• W3042778999 cites W2782336599 @default.
• W3042778999 cites W2786190529 @default.
• W3042778999 cites W2787964457 @default.
• W3042778999 cites W2788856218 @default.
• W3042778999 cites W2806049004 @default.
• W3042778999 cites W2891660952 @default.
• W3042778999 cites W2897610078 @default.
• W3042778999 cites W2897920536 @default.
• W3042778999 cites W2918085083 @default.
• W3042778999 cites W2919115771 @default.
• W3042778999 cites W2923662737 @default.
• W3042778999 cites W2963471237 @default.
• W3042778999 cites W2963600518 @default.
• W3042778999 cites W3044860211 @default.
• W3042778999 cites W4205294293 @default.
• W3042778999 cites W4239373821 @default.
• W3042778999 cites W78410244 @default.
• W3042778999 doi "https://doi.org/10.1101/2020.07.19.206631" @default.
• W3042778999 hasPublicationYear "2020" @default.
• W3042778999 type Work @default.
• W3042778999 sameAs 3042778999 @default.
• W3042778999 citedByCount "2" @default.
• W3042778999 countsByYear W30427789992020 @default.
• W3042778999 countsByYear W30427789992022 @default.
• W3042778999 crossrefType "posted-content" @default.
• W3042778999 hasAuthorship W3042778999A5018160834 @default.
• W3042778999 hasAuthorship W3042778999A5020683576 @default.
• W3042778999 hasAuthorship W3042778999A5026839832 @default.
• W3042778999 hasAuthorship W3042778999A5032838018 @default.
• W3042778999 hasAuthorship W3042778999A5086539185 @default.

• next