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

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

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dc.contributor.author Giraud, Rémi es_ES
dc.contributor.author Ta, Vinh-Thong es_ES
dc.contributor.author Papadakis, Nicolas es_ES
dc.contributor.author Manjón Herrera, José Vicente es_ES
dc.contributor.author Collins, Louis es_ES
dc.contributor.author Coupé Pierrick es_ES
dc.contributor.author Alzheimers Dis Neuroimaging Initia
dc.date.accessioned 2017-05-18T14:04:36Z
dc.date.available 2017-05-18T14:04:36Z
dc.date.issued 2016-01-01
dc.identifier.issn 1053-8119
dc.identifier.uri http://hdl.handle.net/10251/81421
dc.description.abstract Automatic 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. (C) 2015 Elsevier Inc. All rights reserved. es_ES
dc.description.sponsorship This study has been carried out with financial support from the French State, managed by the French National Research Agency (ANR) in the frame of the Investments for the future Program IdEx Bordeaux (ANR-10-IDEX-03-02), Cluster of excellence CPU and TRAIL (HR-DTI ANR-10-LABX-57). We also thank Tong Tong and Daniel Rueckert for providing us complete results of the methods proposed in Tong et al. (2013), Sonia Tangaro and Marina Boccardi for providing us complete results of the method proposed in Tangaro et al. (2014), and Katherine Gray and Robin Wolz for providing us complete results of the LEAP method proposed in Gray et al. (2014). Data collection and sharing for this project were funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904). The ADNI is funded by the National Institute on Aging and the National Institute of Biomedical Imaging and Bioengineering and through generous contributions from the following: Abbott, AstraZeneca AB, Bayer Schering Pharma AG, Bristol-Myers Squibb, Eisai Global Clinical Development, Elan Corporation, Genentech, GE Healthcare, GlaxoSmithKline, Innogenetics NV, Johnson and Johnson, Eli Lilly and Co., Medpace, Inc., Merck and Co., Inc., Novartis AG, Pfizer Inc., F. Hoffmann-La Roche, Schering-Plough, Synarc Inc., as well as nonprofit partners, the Alzheimer's Association and Alzheimer's Drug Discovery Foundation, with participation from the U.S. Food and Drug Administration. Private sector contributions to the ADNI are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study was coordinated by the Alzheimer's Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of California, Los Angeles. This research was also supported by the Spanish grant TIN2013-43457-R from the Ministerio de Economia y competitividad, NIH grants P30AG010129, K01 AG030514 and the Dana Foundation. en_EN
dc.language Inglés es_ES
dc.publisher Elsevier es_ES
dc.relation French National Research Agency (ANR) es_ES
dc.relation Program IdEx Bordeaux (ANR-10-IDEX-03-02) es_ES
dc.relation Cluster of excellence CPU and TRAIL (HR-DTI ANR-10-LABX-57) es_ES
dc.relation Spanish Ministerio de Economia y competitividad grant TIN2013-43457-R es_ES
dc.relation NIH grants P30AG010129 es_ES
dc.relation K01 AG030514 es_ES
dc.relation Dana Foundation es_ES
dc.relation Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) es_ES
dc.relation.ispartof NeuroImage es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Patch matching es_ES
dc.subject Segmentation es_ES
dc.subject Late fusion es_ES
dc.subject Hippocampus es_ES
dc.subject Patch-based method es_ES
dc.subject.classification FISICA APLICADA es_ES
dc.title An Optimized PatchMatch for multi-scale and multi-feature label fusion es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1016/j.neuroimage.2015.07.076
dc.relation.projectID info:eu-repo/grantAgreement/NIH/NATIONAL INSTITUTE ON AGING/1U01AG024904-01/US en_EN
dc.relation.projectID info:eu-repo/grantAgreement/NIH/NATIONAL INSTITUTE ON AGING/5K01AG030514-02/US
dc.relation.projectID info:eu-repo/grantAgreement/NIH/NATIONAL INSTITUTE ON AGING/3P30AG010129-11S1/US
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Escola Tècnica Superior d'Enginyeria Informàtica es_ES
dc.description.bibliographicCitation Giraud, R.; Ta, V.; Papadakis, N.; Manjón Herrera, JV.; Collins, L.; Coupé Pierrick; Alzheimers Dis Neuroimaging Initia (2016). An Optimized PatchMatch for multi-scale and multi-feature label fusion. NeuroImage. 124(1):770-782. https://doi.org/10.1016/j.neuroimage.2015.07.076 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1016/j.neuroimage.2015.07.076 es_ES
dc.description.upvformatpinicio 770 es_ES
dc.description.upvformatpfin 782 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 124 es_ES
dc.description.issue 1 es_ES
dc.relation.senia 329837 es_ES


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