Matches in SemOpenAlex for { <https://semopenalex.org/work/W2922342825> ?p ?o ?g. }
Showing items 1 to 84 of
84
with 100 items per page.
- W2922342825 endingPage "38" @default.
- W2922342825 startingPage "31" @default.
- W2922342825 abstract "Abstract Accurate pulmonary nodule segmentation, an essential pre-requisite in every computer-aided diagnosis (CAD) system, significantly helps in the risk assessment of lung cancer. In this paper, we propose a synergistic combination of deep learning and shape driven level sets for automated and accurate lung nodule segmentation. A coarse-to-fine solution is adopted, where, a deep fully convolutional network is employed to obtain coarse segmentation. To achieve fine segmentation, shape driven evolution of level sets is designed. The seed points for initializing the level sets are obtained from the coarse segmentation of deep network in an automated manner. Perimeter and circularity of the evolving contours are employed for guiding the evolution of level sets. Experiments on the publicly available LIDC/IDRI dataset clearly reveal that our method outperforms several state-of-the-art competitors as well as its constituent parts, i.e., deep network and level set, when applied in isolation." @default.
- W2922342825 created "2019-03-22" @default.
- W2922342825 creator A5017350792 @default.
- W2922342825 creator A5031703924 @default.
- W2922342825 creator A5049571472 @default.
- W2922342825 date "2019-05-01" @default.
- W2922342825 modified "2023-10-06" @default.
- W2922342825 title "A deep learning-shape driven level set synergism for pulmonary nodule segmentation" @default.
- W2922342825 cites W1979393293 @default.
- W2922342825 cites W1988117153 @default.
- W2922342825 cites W1991804665 @default.
- W2922342825 cites W1997709301 @default.
- W2922342825 cites W2012355247 @default.
- W2922342825 cites W2016895718 @default.
- W2922342825 cites W2067123389 @default.
- W2922342825 cites W2126446504 @default.
- W2922342825 cites W2129597285 @default.
- W2922342825 cites W2134108967 @default.
- W2922342825 cites W2136325898 @default.
- W2922342825 cites W2586834759 @default.
- W2922342825 cites W2607267653 @default.
- W2922342825 cites W2624881194 @default.
- W2922342825 cites W2703928793 @default.
- W2922342825 cites W2737373222 @default.
- W2922342825 doi "https://doi.org/10.1016/j.patrec.2019.03.004" @default.
- W2922342825 hasPublicationYear "2019" @default.
- W2922342825 type Work @default.
- W2922342825 sameAs 2922342825 @default.
- W2922342825 citedByCount "40" @default.
- W2922342825 countsByYear W29223428252019 @default.
- W2922342825 countsByYear W29223428252020 @default.
- W2922342825 countsByYear W29223428252021 @default.
- W2922342825 countsByYear W29223428252022 @default.
- W2922342825 countsByYear W29223428252023 @default.
- W2922342825 crossrefType "journal-article" @default.
- W2922342825 hasAuthorship W2922342825A5017350792 @default.
- W2922342825 hasAuthorship W2922342825A5031703924 @default.
- W2922342825 hasAuthorship W2922342825A5049571472 @default.
- W2922342825 hasConcept C108583219 @default.
- W2922342825 hasConcept C151730666 @default.
- W2922342825 hasConcept C153008295 @default.
- W2922342825 hasConcept C153180895 @default.
- W2922342825 hasConcept C154945302 @default.
- W2922342825 hasConcept C177264268 @default.
- W2922342825 hasConcept C199360897 @default.
- W2922342825 hasConcept C2776731575 @default.
- W2922342825 hasConcept C31972630 @default.
- W2922342825 hasConcept C33923547 @default.
- W2922342825 hasConcept C41008148 @default.
- W2922342825 hasConcept C86803240 @default.
- W2922342825 hasConcept C89600930 @default.
- W2922342825 hasConceptScore W2922342825C108583219 @default.
- W2922342825 hasConceptScore W2922342825C151730666 @default.
- W2922342825 hasConceptScore W2922342825C153008295 @default.
- W2922342825 hasConceptScore W2922342825C153180895 @default.
- W2922342825 hasConceptScore W2922342825C154945302 @default.
- W2922342825 hasConceptScore W2922342825C177264268 @default.
- W2922342825 hasConceptScore W2922342825C199360897 @default.
- W2922342825 hasConceptScore W2922342825C2776731575 @default.
- W2922342825 hasConceptScore W2922342825C31972630 @default.
- W2922342825 hasConceptScore W2922342825C33923547 @default.
- W2922342825 hasConceptScore W2922342825C41008148 @default.
- W2922342825 hasConceptScore W2922342825C86803240 @default.
- W2922342825 hasConceptScore W2922342825C89600930 @default.
- W2922342825 hasLocation W29223428251 @default.
- W2922342825 hasOpenAccess W2922342825 @default.
- W2922342825 hasPrimaryLocation W29223428251 @default.
- W2922342825 hasRelatedWork W1669643531 @default.
- W2922342825 hasRelatedWork W2008656436 @default.
- W2922342825 hasRelatedWork W2039154422 @default.
- W2922342825 hasRelatedWork W2134924024 @default.
- W2922342825 hasRelatedWork W2517104666 @default.
- W2922342825 hasRelatedWork W2790662084 @default.
- W2922342825 hasRelatedWork W2895616727 @default.
- W2922342825 hasRelatedWork W2948658236 @default.
- W2922342825 hasRelatedWork W4293211451 @default.
- W2922342825 hasRelatedWork W2182382398 @default.
- W2922342825 hasVolume "123" @default.
- W2922342825 isParatext "false" @default.
- W2922342825 isRetracted "false" @default.
- W2922342825 magId "2922342825" @default.
- W2922342825 workType "article" @default.