FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4297519341> ?p ?o ?g. }

• W4297519341 abstract "In computed TEM tomography, image segmentation represents one of the most basic tasks with implications not only for 3D volume visualization, but more importantly for quantitative 3D analysis. In case of large and complex 3D data sets, segmentation can be an extremely difficult and laborious task, and thus has been one of the biggest hurdles for comprehensive 3D analysis. Heterogeneous catalysts have complex surface and bulk structures, and often sparse distribution of catalytic particles with relatively poor intrinsic contrast, which possess a unique challenge for image segmentation, including the current state-of-the-art deep learning methods. To tackle this problem, we apply a deep learning-based approach for the multi-class semantic segmentation of a γ-Alumina/Pt catalytic material in a class imbalance situation. Specifically, we used the weighted focal loss as a loss function and attached it to the U-Net's fully convolutional network architecture. We assessed the accuracy of our results using Dice similarity coefficient (DSC), recall, precision, and Hausdorff distance (HD) metrics on the overlap between the ground-truth and predicted segmentations. Our adopted U-Net model with the weighted focal loss function achieved an average DSC score of 0.96 ± 0.003 in the γ-Alumina support material and 0.84 ± 0.03 in the Pt NPs segmentation tasks. We report an average boundary-overlap error of less than 2 nm at the 90th percentile of HD for γ-Alumina and Pt NPs segmentations. The complex surface morphology of γ-Alumina and its relation to the Pt NPs were visualized in 3D by the deep learning-assisted automatic segmentation of a large data set of high-angle annular dark-field (HAADF) scanning transmission electron microscopy (STEM) tomography reconstructions." @default.
• W4297519341 created "2022-09-29" @default.
• W4297519341 creator A5013976369 @default.
• W4297519341 creator A5054836128 @default.
• W4297519341 creator A5059318117 @default.
• W4297519341 date "2022-09-28" @default.
• W4297519341 modified "2023-10-01" @default.
• W4297519341 title "A deep learning approach for semantic segmentation of unbalanced data in electron tomography of catalytic materials" @default.
• W4297519341 cites W1498712972 @default.
• W4297519341 cites W1677182931 @default.
• W4297519341 cites W1901129140 @default.
• W4297519341 cites W1903029394 @default.
• W4297519341 cites W1909740415 @default.
• W4297519341 cites W1965568992 @default.
• W4297519341 cites W1995277257 @default.
• W4297519341 cites W2013672061 @default.
• W4297519341 cites W2015248477 @default.
• W4297519341 cites W2025007543 @default.
• W4297519341 cites W2034293121 @default.
• W4297519341 cites W2062451405 @default.
• W4297519341 cites W2069629287 @default.
• W4297519341 cites W2160754664 @default.
• W4297519341 cites W2168561568 @default.
• W4297519341 cites W2170165449 @default.
• W4297519341 cites W2343687709 @default.
• W4297519341 cites W2345010043 @default.
• W4297519341 cites W2604468722 @default.
• W4297519341 cites W2613456556 @default.
• W4297519341 cites W2734349601 @default.
• W4297519341 cites W2754244961 @default.
• W4297519341 cites W2884561390 @default.
• W4297519341 cites W2885112059 @default.
• W4297519341 cites W2895509920 @default.
• W4297519341 cites W2913294258 @default.
• W4297519341 cites W2919115771 @default.
• W4297519341 cites W2962914239 @default.
• W4297519341 cites W2971847579 @default.
• W4297519341 cites W3045580189 @default.
• W4297519341 cites W3087891374 @default.
• W4297519341 cites W3091854526 @default.
• W4297519341 cites W3100230575 @default.
• W4297519341 cites W3112139896 @default.
• W4297519341 cites W3128281174 @default.
• W4297519341 cites W3160412534 @default.
• W4297519341 cites W3174513578 @default.
• W4297519341 cites W3183167267 @default.
• W4297519341 cites W3191107070 @default.
• W4297519341 cites W3214596602 @default.
• W4297519341 cites W4205138137 @default.
• W4297519341 doi "https://doi.org/10.1038/s41598-022-16429-3" @default.
• W4297519341 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36171204" @default.
• W4297519341 hasPublicationYear "2022" @default.
• W4297519341 type Work @default.
• W4297519341 citedByCount "1" @default.
• W4297519341 countsByYear W42975193412023 @default.
• W4297519341 crossrefType "journal-article" @default.
• W4297519341 hasAuthorship W4297519341A5013976369 @default.
• W4297519341 hasAuthorship W4297519341A5054836128 @default.
• W4297519341 hasAuthorship W4297519341A5059318117 @default.
• W4297519341 hasBestOaLocation W42975193413 @default.
• W4297519341 hasConcept C108583219 @default.
• W4297519341 hasConcept C124504099 @default.
• W4297519341 hasConcept C141898687 @default.
• W4297519341 hasConcept C146849305 @default.
• W4297519341 hasConcept C153180895 @default.
• W4297519341 hasConcept C154945302 @default.
• W4297519341 hasConcept C163892561 @default.
• W4297519341 hasConcept C41008148 @default.
• W4297519341 hasConcept C81363708 @default.
• W4297519341 hasConcept C89600930 @default.
• W4297519341 hasConceptScore W4297519341C108583219 @default.
• W4297519341 hasConceptScore W4297519341C124504099 @default.
• W4297519341 hasConceptScore W4297519341C141898687 @default.
• W4297519341 hasConceptScore W4297519341C146849305 @default.
• W4297519341 hasConceptScore W4297519341C153180895 @default.
• W4297519341 hasConceptScore W4297519341C154945302 @default.
• W4297519341 hasConceptScore W4297519341C163892561 @default.
• W4297519341 hasConceptScore W4297519341C41008148 @default.
• W4297519341 hasConceptScore W4297519341C81363708 @default.
• W4297519341 hasConceptScore W4297519341C89600930 @default.
• W4297519341 hasIssue "1" @default.
• W4297519341 hasLocation W42975193411 @default.
• W4297519341 hasLocation W42975193412 @default.
• W4297519341 hasLocation W42975193413 @default.
• W4297519341 hasLocation W42975193414 @default.
• W4297519341 hasLocation W42975193415 @default.
• W4297519341 hasOpenAccess W4297519341 @default.
• W4297519341 hasPrimaryLocation W42975193411 @default.
• W4297519341 hasRelatedWork W158826679 @default.
• W4297519341 hasRelatedWork W2769435486 @default.
• W4297519341 hasRelatedWork W2920218276 @default.
• W4297519341 hasRelatedWork W2969790209 @default.
• W4297519341 hasRelatedWork W3188463548 @default.
• W4297519341 hasRelatedWork W4200528772 @default.
• W4297519341 hasRelatedWork W4256106984 @default.
• W4297519341 hasRelatedWork W4285827401 @default.
• W4297519341 hasRelatedWork W4323022465 @default.
• W4297519341 hasRelatedWork W4363650189 @default.
• W4297519341 hasVolume "12" @default.
• W4297519341 isParatext "false" @default.

• next