Hybrid U-Net-based deep learning model for volume segmentation of lung nodules in CT images.
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| Title: | Hybrid U-Net-based deep learning model for volume segmentation of lung nodules in CT images. |
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| Authors: | Wang Y; Department of Radiology, The University of Michigan, Ann Arbor, Michigan, USA., Zhou C; Department of Radiology, The University of Michigan, Ann Arbor, Michigan, USA., Chan HP; Department of Radiology, The University of Michigan, Ann Arbor, Michigan, USA., Hadjiiski LM; Department of Radiology, The University of Michigan, Ann Arbor, Michigan, USA., Chughtai A; Department of Radiology, The University of Michigan, Ann Arbor, Michigan, USA., Kazerooni EA; Department of Radiology, The University of Michigan, Ann Arbor, Michigan, USA. |
| Source: | Medical physics [Med Phys] 2022 Nov; Vol. 49 (11), pp. 7287-7302. Date of Electronic Publication: 2022 Aug 17. |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: John Wiley and Sons, Inc Country of Publication: United States NLM ID: 0425746 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2473-4209 (Electronic) Linking ISSN: 00942405 NLM ISO Abbreviation: Med Phys Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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| Header | DbId: mdl DbLabel: MEDLINE Ultimate An: 35717560 AccessLevel: 2 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Hybrid U-Net-based deep learning model for volume segmentation of lung nodules in CT images. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AU" term="%22Wang+Y%22">Wang Y</searchLink>; Department of Radiology, The University of Michigan, Ann Arbor, Michigan, USA.<br /><searchLink fieldCode="AU" term="%22Zhou+C%22">Zhou C</searchLink>; Department of Radiology, The University of Michigan, Ann Arbor, Michigan, USA.<br /><searchLink fieldCode="AU" term="%22Chan+HP%22">Chan HP</searchLink>; Department of Radiology, The University of Michigan, Ann Arbor, Michigan, USA.<br /><searchLink fieldCode="AU" term="%22Hadjiiski+LM%22">Hadjiiski LM</searchLink>; Department of Radiology, The University of Michigan, Ann Arbor, Michigan, USA.<br /><searchLink fieldCode="AU" term="%22Chughtai+A%22">Chughtai A</searchLink>; Department of Radiology, The University of Michigan, Ann Arbor, Michigan, USA.<br /><searchLink fieldCode="AU" term="%22Kazerooni+EA%22">Kazerooni EA</searchLink>; Department of Radiology, The University of Michigan, Ann Arbor, Michigan, USA. – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%220425746%22">Medical physics</searchLink> [Med Phys] 2022 Nov; Vol. 49 (11), pp. 7287-7302. <i>Date of Electronic Publication: </i>2022 Aug 17. – Name: TypePub Label: Publication Type Group: TypPub Data: Journal Article – Name: TitleSource Label: Journal Info Group: Src Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22John+Wiley+and+Sons%2C+Inc%22">John Wiley and Sons, Inc </searchLink><i>Country of Publication: </i>United States <i>NLM ID: </i>0425746 <i>Publication Model: </i>Print-Electronic <i>Cited Medium: </i>Internet <i>ISSN: </i>2473-4209 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2200942405%22">00942405 </searchLink><i>NLM ISO Abbreviation: </i>Med Phys <i>Subsets: </i>MEDLINE |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=mdl&AN=35717560 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1002/mp.15810 Languages: – Code: eng Text: English PhysicalDescription: Pagination: StartPage: 7287 Titles: – TitleFull: Hybrid U-Net-based deep learning model for volume segmentation of lung nodules in CT images. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Wang Y – PersonEntity: Name: NameFull: Zhou C – PersonEntity: Name: NameFull: Chan HP – PersonEntity: Name: NameFull: Hadjiiski LM – PersonEntity: Name: NameFull: Chughtai A – PersonEntity: Name: NameFull: Kazerooni EA IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 11 Text: 2022 Nov Type: published Y: 2022 Identifiers: – Type: issn-electronic Value: 2473-4209 Numbering: – Type: volume Value: 49 – Type: issue Value: 11 Titles: – TitleFull: Medical physics Type: main |
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