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Local vs global descriptors of hippocampus shape evolution for Alzheimer's longitudinal population analysis

hal.structure.identifierCEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
dc.contributor.authorFiot, Jean-Baptiste*
hal.structure.identifier
dc.contributor.authorRisser, Laurent
HAL ID: 17551		*
hal.structure.identifierCEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
dc.contributor.authorCohen, Laurent D.
HAL ID: 738939		*
hal.structure.identifierCSIRO Information and Commuciation Technologies [CSIRO ICT Centre]
dc.contributor.authorFripp, Jürgen*
hal.structure.identifier
dc.contributor.authorVialard, François-Xavier*
dc.date.accessioned2012-12-19T14:42:30Z
dc.date.available2012-12-19T14:42:30Z
dc.date.issued2012
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/10748
dc.descriptionLNCS n°7570
dc.language.isoenen
dc.subjectBrain imaging
dc.subjectpopulation analysis
dc.subjectgeodesic shooting
dc.subjectAlzheimer’s disease
dc.subjecttime-series image data
dc.subjectKärcher mean
dc.subject.ddc006.3en
dc.titleLocal vs global descriptors of hippocampus shape evolution for Alzheimer's longitudinal population analysis
dc.typeCommunication / Conférence
dc.contributor.editoruniversityotherCSIRO Information and Commuciation Technologies (CSIRO ICT Centre) http://www3.ict.csiro.au/;France
dc.contributor.editoruniversityotherInstitut de Mathématiques de Toulouse (IMT) Université Paul Sabatier - Toulouse III – Université Toulouse le Mirail - Toulouse II – Université des Sciences Sociales - Toulouse I – Institut National des Sciences Appliquées de Toulouse – CNRS : UMR5219;France
dc.description.abstractenIn the context of Alzheimer's disease (AD), state-of-the-art methods separating normal control (NC) from AD patients or CN from progressive MCI (mild cognitive impairment patients converting to AD) achieve decent classification rates. However, they all perform poorly at separating stable MCI (MCI patients not converting to AD) and progressive MCI. Instead of using features extracted from a single temporal point, we address this problem using descriptors of the hippocampus evolutions between two time points. To encode the transformation, we use the framework of large deformations by diffeomorphisms that provides geodesic evolutions. To perform statistics on those local features in a common coordinate system, we introduce an extension of the K\"{a}rcher mean algorithm that defines the template modulo rigid registrations, and an initialization criterion that provides a final template leading to better matching with the patients. Finally, as local descriptors transported to this template do not directly perform as well as global descriptors (e.g. volume difference), we propose a novel strategy combining the use of initial momentum from geodesic shooting, extended K\"{a}rcher algorithm, density transport and integration on a hippocampus subregion, which is able to outperform global descriptors.
dc.identifier.citationpages13-24
dc.relation.ispartoftitleSpatio-temporal Image Analysis for Longitudinal and Time-Series Image Data Second International Workshop, STIA 2012, Held in Conjunction with MICCAI 2012, Nice, France, October 1, 2012. Proceedings
dc.relation.ispartoftitleSTIA 2012 - MICCAI 2012
dc.relation.ispartofeditorStanley Durrleman, Tom Fletcher, Guido Gerig, Mars Niethammer
dc.relation.ispartofpublnameSpringer
dc.relation.ispartofpublcityBerlin Heidelberg
dc.relation.ispartofdate2012
dc.relation.ispartofurl10.1007/978-3-642-33555-6
dc.identifier.urlsitehttps://hal.archives-ouvertes.fr/hal-00764163
dc.subject.ddclabelIntelligence artificielleen
dc.relation.ispartofisbn978-3-642-33554-9
dc.relation.confcountryFRANCE
dc.relation.forthcomingnonen
dc.identifier.doi10.1007/978-3-642-33555-6_2
dc.description.ssrncandidatenon
dc.description.halcandidateoui
dc.description.readershiprecherche
dc.description.audienceInternational
dc.date.updated2017-03-10T16:46:31Z
hal.author.functionaut
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hal.author.functionaut
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