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An Optimized PatchMatch for multi-scale and multi-feature label fusion

hal.structure.identifierLaboratoire Bordelais de Recherche en Informatique [LaBRI]
hal.structure.identifierInstitut Polytechnique de Bordeaux [Bordeaux INP]
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
dc.contributor.authorGIRAUD, Rémi
hal.structure.identifierLaboratoire Bordelais de Recherche en Informatique [LaBRI]
hal.structure.identifierInstitut Polytechnique de Bordeaux [Bordeaux INP]
dc.contributor.authorTA, Vinh-Thong
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
dc.contributor.authorPAPADAKIS, Nicolas
hal.structure.identifierITACA
dc.contributor.authorMANJON, Jose Vicente
hal.structure.identifierMcConnell Brain Imaging Centre [MNI]
dc.contributor.authorLOUIS COLLINS, D
hal.structure.identifierLaboratoire Bordelais de Recherche en Informatique [LaBRI]
dc.contributor.authorCOUPÉ, Pierrick
dc.date.accessioned2024-04-04T03:17:40Z
dc.date.available2024-04-04T03:17:40Z
dc.date.issued2016
dc.identifier.issn1053-8119
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/194326
dc.description.abstractEnAutomatic segmentation methods are important tools for quantitative analysis of Magnetic Resonance Images (MRI). Recently, patch-based label fusion approaches have demonstrated state-of-the-art segmentation accuracy. In this paper, we introduce a new patch-based label fusion framework to perform segmentation of anatomical structures. The proposed approach uses an Optimized PAtchMatch Label fusion (OPAL) strategy that drastically reduces the computation time required for the search of similar patches. The reduced computation time of OPAL opens the way for new strategies and facilitates processing on large databases. In this paper, we investigate new perspectives offered by OPAL, by introducing a new multi-scale and multi-feature framework. During our validation on hippocampus segmentation we use two datasets: young adults in the ICBM cohort and elderly adults in the EADC-ADNI dataset. For both, OPAL is compared to state-of-the-art methods. Results show that OPAL obtained the highest median Dice coefficient (89.9% for ICBM and 90.1% for EADC-ADNI). Moreover, in both cases, OPAL produced a segmentation accuracy similar to inter-expert variability. On the EADC-ADNI dataset, we compare the hippocampal volumes obtained by manual and automatic segmentation. The volumes appear to be highly correlated that enables to perform more accurate separation of pathological populations.
dc.description.sponsorshipInitiative d'excellence de l'Université de Bordeaux - ANR-10-IDEX-0003
dc.language.isoen
dc.publisherElsevier
dc.subject.enSegmentation
dc.subject.enPatch-Based Method
dc.subject.enPatch Matching
dc.subject.enLate Fusion
dc.subject.enHippocampus
dc.title.enAn Optimized PatchMatch for multi-scale and multi-feature label fusion
dc.typeArticle de revue
dc.identifier.doi10.1016/j.neuroimage.2015.07.076
dc.subject.halInformatique [cs]
dc.subject.halInformatique [cs]/Vision par ordinateur et reconnaissance de formes [cs.CV]
dc.subject.halInformatique [cs]/Traitement des images
dc.subject.halInformatique [cs]/Imagerie médicale
bordeaux.journalNeuroImage
bordeaux.page770-782
bordeaux.volume124
bordeaux.hal.laboratoriesInstitut de Mathématiques de Bordeaux (IMB) - UMR 5251*
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.peerReviewedoui
hal.identifierhal-01198703
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-01198703v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=NeuroImage&rft.date=2016&rft.volume=124&rft.spage=770-782&rft.epage=770-782&rft.eissn=1053-8119&rft.issn=1053-8119&rft.au=GIRAUD,%20R%C3%A9mi&TA,%20Vinh-Thong&PAPADAKIS,%20Nicolas&MANJON,%20Jose%20Vicente&LOUIS%20COLLINS,%20D&rft.genre=article


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