Matches in SemOpenAlex for { <https://semopenalex.org/work/W4211021847> ?p ?o ?g. }
Showing items 1 to 82 of
82
with 100 items per page.
- W4211021847 abstract "Recently, deep learning has reached significant advancements in various image-related tasks, particularly in medical sciences. Deep neural networks have been used to facilitate diagnosing medical images generated from various observation techniques including CT (computed tomography) scans. As a non-destructive 3D imaging technique, CT scan has also been widely used in paleontological research, which provides the solid foundation for taxon identification, comparative anatomy, functional morphology, etc. However, the labeling and segmentation of CT images are often laborious, prone to error, and subject to researchers own judgements. It is essential to set a benchmark in CT imaging processing of fossils and reduce the time cost from manual processing. Since fossils from the same localities usually share similar sedimentary environments, we constructed a dataset comprising CT slices of protoceratopsian dinosaurs from the Gobi Desert, Mongolia. Here we tested the fossil segmentation performances of U-net, a classic deep neural network for image segmentation, and constructed a modified DeepLab v3+ network, which included MobileNet v1 as feature extractor and practiced an atrous convolutional method that can capture features from various scales. The results show that deep neural network can efficiently segment protoceratopsian dinosaur fossils, which can save significant time from current manual segmentation. But further test on a dataset generated by other vertebrate fossils, even from similar localities, is largely limited." @default.
- W4211021847 created "2022-02-13" @default.
- W4211021847 creator A5002085109 @default.
- W4211021847 creator A5003819876 @default.
- W4211021847 creator A5012752876 @default.
- W4211021847 creator A5039272604 @default.
- W4211021847 creator A5060002817 @default.
- W4211021847 date "2022-01-27" @default.
- W4211021847 modified "2023-09-27" @default.
- W4211021847 title "CT Segmentation of Dinosaur Fossils by Deep Learning" @default.
- W4211021847 cites W1966518362 @default.
- W4211021847 cites W2023928265 @default.
- W4211021847 cites W2095233827 @default.
- W4211021847 cites W2257979135 @default.
- W4211021847 cites W2533800772 @default.
- W4211021847 cites W2562469482 @default.
- W4211021847 cites W2581082771 @default.
- W4211021847 cites W2592929672 @default.
- W4211021847 cites W2766447205 @default.
- W4211021847 cites W2777186991 @default.
- W4211021847 cites W2797547336 @default.
- W4211021847 cites W2799723178 @default.
- W4211021847 cites W2903268430 @default.
- W4211021847 cites W2913323966 @default.
- W4211021847 cites W2923222994 @default.
- W4211021847 cites W2949889661 @default.
- W4211021847 cites W3016291243 @default.
- W4211021847 cites W3048571636 @default.
- W4211021847 cites W3102253946 @default.
- W4211021847 cites W3104370314 @default.
- W4211021847 cites W3132519602 @default.
- W4211021847 cites W3209296733 @default.
- W4211021847 doi "https://doi.org/10.3389/feart.2021.805271" @default.
- W4211021847 hasPublicationYear "2022" @default.
- W4211021847 type Work @default.
- W4211021847 citedByCount "3" @default.
- W4211021847 countsByYear W42110218472023 @default.
- W4211021847 crossrefType "journal-article" @default.
- W4211021847 hasAuthorship W4211021847A5002085109 @default.
- W4211021847 hasAuthorship W4211021847A5003819876 @default.
- W4211021847 hasAuthorship W4211021847A5012752876 @default.
- W4211021847 hasAuthorship W4211021847A5039272604 @default.
- W4211021847 hasAuthorship W4211021847A5060002817 @default.
- W4211021847 hasBestOaLocation W42110218471 @default.
- W4211021847 hasConcept C108583219 @default.
- W4211021847 hasConcept C138885662 @default.
- W4211021847 hasConcept C153180895 @default.
- W4211021847 hasConcept C154945302 @default.
- W4211021847 hasConcept C2776401178 @default.
- W4211021847 hasConcept C41008148 @default.
- W4211021847 hasConcept C41895202 @default.
- W4211021847 hasConcept C50644808 @default.
- W4211021847 hasConcept C81363708 @default.
- W4211021847 hasConcept C89600930 @default.
- W4211021847 hasConceptScore W4211021847C108583219 @default.
- W4211021847 hasConceptScore W4211021847C138885662 @default.
- W4211021847 hasConceptScore W4211021847C153180895 @default.
- W4211021847 hasConceptScore W4211021847C154945302 @default.
- W4211021847 hasConceptScore W4211021847C2776401178 @default.
- W4211021847 hasConceptScore W4211021847C41008148 @default.
- W4211021847 hasConceptScore W4211021847C41895202 @default.
- W4211021847 hasConceptScore W4211021847C50644808 @default.
- W4211021847 hasConceptScore W4211021847C81363708 @default.
- W4211021847 hasConceptScore W4211021847C89600930 @default.
- W4211021847 hasLocation W42110218471 @default.
- W4211021847 hasLocation W42110218472 @default.
- W4211021847 hasOpenAccess W4211021847 @default.
- W4211021847 hasPrimaryLocation W42110218471 @default.
- W4211021847 hasRelatedWork W2731899572 @default.
- W4211021847 hasRelatedWork W2790662084 @default.
- W4211021847 hasRelatedWork W2999805992 @default.
- W4211021847 hasRelatedWork W3011074480 @default.
- W4211021847 hasRelatedWork W3116150086 @default.
- W4211021847 hasRelatedWork W3133861977 @default.
- W4211021847 hasRelatedWork W4200173597 @default.
- W4211021847 hasRelatedWork W4291897433 @default.
- W4211021847 hasRelatedWork W4312417841 @default.
- W4211021847 hasRelatedWork W4321369474 @default.
- W4211021847 hasVolume "9" @default.
- W4211021847 isParatext "false" @default.
- W4211021847 isRetracted "false" @default.
- W4211021847 workType "article" @default.