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Simultaneous and consistent labeling of longitudinal dynamic developing cortical surfaces in infants

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dc.contributor.authorLi, Gang-
dc.contributor.authorWang, Li-
dc.contributor.authorShi, Feng-
dc.contributor.authorLin, Weili-
dc.contributor.authorShen, Dinggang-
dc.date.accessioned2021-09-05T02:41:48Z-
dc.date.available2021-09-05T02:41:48Z-
dc.date.created2021-06-15-
dc.date.issued2014-12-
dc.identifier.issn1361-8415-
dc.identifier.urihttps://scholar.korea.ac.kr/handle/2021.sw.korea/96711-
dc.description.abstractThe human cerebral cortex develops extremely dynamically in the first 2 years of life. Accurate and consistent parcellation of longitudinal dynamic cortical surfaces during this critical stage is essential to understand the early development of cortical structure and function in both normal and high-risk infant brains. However, directly applying the existing methods developed for the cross-sectional studies often generates longitudinally-inconsistent results, thus leading to inaccurate measurements of the cortex development. In this paper, we propose a new method for accurate, consistent, and simultaneous labeling of longitudinal cortical surfaces in the serial infant brain MR images. The proposed method is explicitly formulated as a minimization problem with an energy function that includes a data fitting term, a spatial smoothness term, and a temporal consistency term. Specifically, inspired by multi-atlas based label fusion, the data fitting term is designed to integrate the contributions from multi-atlas surfaces adaptively, according to the similarities of their local cortical folding with that of the subject cortical surface. The spatial smoothness term is then designed to adaptively encourage label smoothness based on the local cortical folding geometries, i.e., allowing label discontinuity at sulcal bottoms (which often are the boundaries of cytoarchitecturally and functionally distinct regions). The temporal consistency term is to adaptively encourage the label consistency among the temporally-corresponding vertices, based on their similarity of local cortical folding. Finally, the entire energy function is efficiently minimized by a graph cuts method. The proposed method has been applied to the parcellation of longitudinal cortical surfaces of 13 healthy infants, each with 6 serial MRI scans acquired at 0, 3, 6, 9, 12 and 18 months of age. Qualitative and quantitative evaluations demonstrated both accuracy and longitudinal consistency of the proposed method. By using our method, for the first time, we reveal several hitherto unseen properties of the dynamic and regionally heterogeneous development of the cortical surface area in the first 18 months of life. (C) 2014 Elsevier B.V. All rights reserved.-
dc.languageEnglish-
dc.language.isoen-
dc.publisherELSEVIER SCIENCE BV-
dc.subjectHUMAN CEREBRAL-CORTEX-
dc.subjectBRAIN MR-IMAGES-
dc.subjectATLAS-BASED SEGMENTATION-
dc.subjectDEFORMABLE REGISTRATION-
dc.subjectHEMISPHERIC ASYMMETRIES-
dc.subjectAUTOMATIC SEGMENTATION-
dc.subjectENERGY MINIMIZATION-
dc.subjectSULCAL PARCELLATION-
dc.subjectLEVEL SETS-
dc.subjectBIRTH-
dc.titleSimultaneous and consistent labeling of longitudinal dynamic developing cortical surfaces in infants-
dc.typeArticle-
dc.contributor.affiliatedAuthorShen, Dinggang-
dc.identifier.doi10.1016/j.media.2014.06.007-
dc.identifier.scopusid2-s2.0-84926277613-
dc.identifier.wosid000342277600003-
dc.identifier.bibliographicCitationMEDICAL IMAGE ANALYSIS, v.18, no.8, pp.1274 - 1289-
dc.relation.isPartOfMEDICAL IMAGE ANALYSIS-
dc.citation.titleMEDICAL IMAGE ANALYSIS-
dc.citation.volume18-
dc.citation.number8-
dc.citation.startPage1274-
dc.citation.endPage1289-
dc.type.rimsART-
dc.type.docTypeArticle-
dc.description.journalClass1-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaComputer Science-
dc.relation.journalResearchAreaEngineering-
dc.relation.journalResearchAreaRadiology, Nuclear Medicine & Medical Imaging-
dc.relation.journalWebOfScienceCategoryComputer Science, Artificial Intelligence-
dc.relation.journalWebOfScienceCategoryComputer Science, Interdisciplinary Applications-
dc.relation.journalWebOfScienceCategoryEngineering, Biomedical-
dc.relation.journalWebOfScienceCategoryRadiology, Nuclear Medicine & Medical Imaging-
dc.subject.keywordPlusHUMAN CEREBRAL-CORTEX-
dc.subject.keywordPlusBRAIN MR-IMAGES-
dc.subject.keywordPlusATLAS-BASED SEGMENTATION-
dc.subject.keywordPlusDEFORMABLE REGISTRATION-
dc.subject.keywordPlusHEMISPHERIC ASYMMETRIES-
dc.subject.keywordPlusAUTOMATIC SEGMENTATION-
dc.subject.keywordPlusENERGY MINIMIZATION-
dc.subject.keywordPlusSULCAL PARCELLATION-
dc.subject.keywordPlusLEVEL SETS-
dc.subject.keywordPlusBIRTH-
dc.subject.keywordAuthorCortical surface-
dc.subject.keywordAuthorParcellation-
dc.subject.keywordAuthorLongitudinal analysis-
dc.subject.keywordAuthorInfant-
dc.subject.keywordAuthorEarly brain development-
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