Longitudinal deformation models, spatial regularizations and learning strategies to quantify Alzheimer's disease progression.
Identifieur interne : 003422 ( PubMed/Checkpoint ); précédent : 003421; suivant : 003423Longitudinal deformation models, spatial regularizations and learning strategies to quantify Alzheimer's disease progression.
Auteurs : Jean-Baptiste Fiot [France] ; Hugo Raguet [France] ; Laurent Risser [France] ; Laurent D. Cohen [France] ; Jurgen Fripp [Australie] ; François-Xavier Vialard [France]Source :
- NeuroImage. Clinical [ 2213-1582 ] ; 2014.
Descripteurs français
- KwdFr :
- Bases de données factuelles (), Dysfonctionnement cognitif (anatomopathologie), Dysfonctionnement cognitif (physiopathologie), Hippocampe (anatomopathologie), Hippocampe (physiopathologie), Humains, Imagerie par résonance magnétique, Maladie d'Alzheimer (anatomopathologie), Maladie d'Alzheimer (physiopathologie), Modèles logistiques, Modèles neurologiques, Traitement d'image par ordinateur, Évolution de la maladie.
- MESH :
- anatomopathologie : Dysfonctionnement cognitif, Hippocampe, Maladie d'Alzheimer.
- physiopathologie : Dysfonctionnement cognitif, Hippocampe, Maladie d'Alzheimer.
- Bases de données factuelles, Humains, Imagerie par résonance magnétique, Modèles logistiques, Modèles neurologiques, Traitement d'image par ordinateur, Évolution de la maladie.
English descriptors
- KwdEn :
- Alzheimer Disease (pathology), Alzheimer Disease (physiopathology), Cognitive Dysfunction (pathology), Cognitive Dysfunction (physiopathology), Databases, Factual (statistics & numerical data), Disease Progression, Hippocampus (pathology), Hippocampus (physiopathology), Humans, Image Processing, Computer-Assisted, Logistic Models, Magnetic Resonance Imaging, Models, Neurological.
- MESH :
- pathology : Alzheimer Disease, Cognitive Dysfunction, Hippocampus.
- physiopathology : Alzheimer Disease, Cognitive Dysfunction, Hippocampus.
- statistics & numerical data : Databases, Factual.
- Disease Progression, Humans, Image Processing, Computer-Assisted, Logistic Models, Magnetic Resonance Imaging, Models, Neurological.
Abstract
In the context of Alzheimer's disease, two challenging issues are (1) the characterization of local hippocampal shape changes specific to disease progression and (2) the identification of mild-cognitive impairment patients likely to convert. In the literature, (1) is usually solved first to detect areas potentially related to the disease. These areas are then considered as an input to solve (2). As an alternative to this sequential strategy, we investigate the use of a classification model using logistic regression to address both issues (1) and (2) simultaneously. The classification of the patients therefore does not require any a priori definition of the most representative hippocampal areas potentially related to the disease, as they are automatically detected. We first quantify deformations of patients' hippocampi between two time points using the large deformations by diffeomorphisms framework and transport these deformations to a common template. Since the deformations are expected to be spatially structured, we perform classification combining logistic loss and spatial regularization techniques, which have not been explored so far in this context, as far as we know. The main contribution of this paper is the comparison of regularization techniques enforcing the coefficient maps to be spatially smooth (Sobolev), piecewise constant (total variation) or sparse (fused LASSO) with standard regularization techniques which do not take into account the spatial structure (LASSO, ridge and ElasticNet). On a dataset of 103 patients out of ADNI, the techniques using spatial regularizations lead to the best classification rates. They also find coherent areas related to the disease progression.
DOI: 10.1016/j.nicl.2014.02.002
PubMed: 24936423
Affiliations:
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pubmed:24936423Le document en format XML
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Clinical</title><idno type="ISSN">2213-1582</idno><imprint><date when="2014" type="published">2014</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Alzheimer Disease (pathology)</term><term>Alzheimer Disease (physiopathology)</term><term>Cognitive Dysfunction (pathology)</term><term>Cognitive Dysfunction (physiopathology)</term><term>Databases, Factual (statistics & numerical data)</term><term>Disease Progression</term><term>Hippocampus (pathology)</term><term>Hippocampus (physiopathology)</term><term>Humans</term><term>Image Processing, Computer-Assisted</term><term>Logistic Models</term><term>Magnetic Resonance Imaging</term><term>Models, Neurological</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Bases de données factuelles ()</term><term>Dysfonctionnement cognitif (anatomopathologie)</term><term>Dysfonctionnement cognitif (physiopathologie)</term><term>Hippocampe (anatomopathologie)</term><term>Hippocampe (physiopathologie)</term><term>Humains</term><term>Imagerie par résonance magnétique</term><term>Maladie d'Alzheimer (anatomopathologie)</term><term>Maladie d'Alzheimer (physiopathologie)</term><term>Modèles logistiques</term><term>Modèles neurologiques</term><term>Traitement d'image par ordinateur</term><term>Évolution de la maladie</term></keywords><keywords scheme="MESH" qualifier="anatomopathologie" xml:lang="fr"><term>Dysfonctionnement cognitif</term><term>Hippocampe</term><term>Maladie d'Alzheimer</term></keywords><keywords scheme="MESH" qualifier="pathology" xml:lang="en"><term>Alzheimer Disease</term><term>Cognitive Dysfunction</term><term>Hippocampus</term></keywords><keywords scheme="MESH" qualifier="physiopathologie" xml:lang="fr"><term>Dysfonctionnement cognitif</term><term>Hippocampe</term><term>Maladie d'Alzheimer</term></keywords><keywords scheme="MESH" qualifier="physiopathology" xml:lang="en"><term>Alzheimer Disease</term><term>Cognitive Dysfunction</term><term>Hippocampus</term></keywords><keywords scheme="MESH" qualifier="statistics & numerical data" xml:lang="en"><term>Databases, Factual</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Disease Progression</term><term>Humans</term><term>Image Processing, Computer-Assisted</term><term>Logistic Models</term><term>Magnetic Resonance Imaging</term><term>Models, Neurological</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Bases de données factuelles</term><term>Humains</term><term>Imagerie par résonance magnétique</term><term>Modèles logistiques</term><term>Modèles neurologiques</term><term>Traitement d'image par ordinateur</term><term>Évolution de la maladie</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">In the context of Alzheimer's disease, two challenging issues are (1) the characterization of local hippocampal shape changes specific to disease progression and (2) the identification of mild-cognitive impairment patients likely to convert. In the literature, (1) is usually solved first to detect areas potentially related to the disease. These areas are then considered as an input to solve (2). As an alternative to this sequential strategy, we investigate the use of a classification model using logistic regression to address both issues (1) and (2) simultaneously. The classification of the patients therefore does not require any a priori definition of the most representative hippocampal areas potentially related to the disease, as they are automatically detected. We first quantify deformations of patients' hippocampi between two time points using the large deformations by diffeomorphisms framework and transport these deformations to a common template. Since the deformations are expected to be spatially structured, we perform classification combining logistic loss and spatial regularization techniques, which have not been explored so far in this context, as far as we know. The main contribution of this paper is the comparison of regularization techniques enforcing the coefficient maps to be spatially smooth (Sobolev), piecewise constant (total variation) or sparse (fused LASSO) with standard regularization techniques which do not take into account the spatial structure (LASSO, ridge and ElasticNet). On a dataset of 103 patients out of ADNI, the techniques using spatial regularizations lead to the best classification rates. They also find coherent areas related to the disease progression.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">24936423</PMID><DateCreated><Year>2014</Year><Month>06</Month><Day>17</Day></DateCreated><DateCompleted><Year>2015</Year><Month>10</Month><Day>30</Day></DateCompleted><DateRevised><Year>2017</Year><Month>02</Month><Day>20</Day></DateRevised><Article PubModel="Electronic-eCollection"><Journal><ISSN IssnType="Print">2213-1582</ISSN><JournalIssue CitedMedium="Print"><Volume>4</Volume><PubDate><Year>2014</Year></PubDate></JournalIssue><Title>NeuroImage. Clinical</Title><ISOAbbreviation>Neuroimage Clin</ISOAbbreviation></Journal><ArticleTitle>Longitudinal deformation models, spatial regularizations and learning strategies to quantify Alzheimer's disease progression.</ArticleTitle><Pagination><MedlinePgn>718-29</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1016/j.nicl.2014.02.002</ELocationID><Abstract><AbstractText>In the context of Alzheimer's disease, two challenging issues are (1) the characterization of local hippocampal shape changes specific to disease progression and (2) the identification of mild-cognitive impairment patients likely to convert. In the literature, (1) is usually solved first to detect areas potentially related to the disease. These areas are then considered as an input to solve (2). As an alternative to this sequential strategy, we investigate the use of a classification model using logistic regression to address both issues (1) and (2) simultaneously. The classification of the patients therefore does not require any a priori definition of the most representative hippocampal areas potentially related to the disease, as they are automatically detected. We first quantify deformations of patients' hippocampi between two time points using the large deformations by diffeomorphisms framework and transport these deformations to a common template. Since the deformations are expected to be spatially structured, we perform classification combining logistic loss and spatial regularization techniques, which have not been explored so far in this context, as far as we know. The main contribution of this paper is the comparison of regularization techniques enforcing the coefficient maps to be spatially smooth (Sobolev), piecewise constant (total variation) or sparse (fused LASSO) with standard regularization techniques which do not take into account the spatial structure (LASSO, ridge and ElasticNet). On a dataset of 103 patients out of ADNI, the techniques using spatial regularizations lead to the best classification rates. They also find coherent areas related to the disease progression.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Fiot</LastName><ForeName>Jean-Baptiste</ForeName><Initials>JB</Initials><AffiliationInfo><Affiliation>IBM Research, Smarter Cities Technology Centre, Damastown, Dublin 15, Ireland ; CEREMADE, UMR 7534 CNRS, Université Paris Dauphine, PSL★, France.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Raguet</LastName><ForeName>Hugo</ForeName><Initials>H</Initials><AffiliationInfo><Affiliation>CEREMADE, UMR 7534 CNRS, Université Paris Dauphine, PSL★, France.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Risser</LastName><ForeName>Laurent</ForeName><Initials>L</Initials><AffiliationInfo><Affiliation>CNRS, Institut de Mathématiques de Toulouse, UMR 5219, France.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Cohen</LastName><ForeName>Laurent D</ForeName><Initials>LD</Initials><AffiliationInfo><Affiliation>CEREMADE, UMR 7534 CNRS, Université Paris Dauphine, PSL★, France.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Fripp</LastName><ForeName>Jurgen</ForeName><Initials>J</Initials><AffiliationInfo><Affiliation>CSIRO Preventative Health National Research Flagship ICTC, The Australian e-Health Research Centre - BioMedIA, Royal Brisbane and Women's Hospital, Herston, QLD, Australia.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Vialard</LastName><ForeName>François-Xavier</ForeName><Initials>FX</Initials><AffiliationInfo><Affiliation>CEREMADE, UMR 7534 CNRS, Université Paris Dauphine, PSL★, France.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><CollectiveName>Alzheimer's Disease Neuroimaging Initiative</CollectiveName></Author></AuthorList><Language>eng</Language><GrantList CompleteYN="Y"><Grant><GrantID>U01 AG024904</GrantID><Acronym>AG</Acronym><Agency>NIA NIH HHS</Agency><Country>United States</Country></Grant></GrantList><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D052061">Research Support, N.I.H., Extramural</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2014</Year><Month>04</Month><Day>01</Day></ArticleDate></Article><MedlineJournalInfo><Country>Netherlands</Country><MedlineTA>Neuroimage Clin</MedlineTA><NlmUniqueID>101597070</NlmUniqueID><ISSNLinking>2213-1582</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><CommentsCorrectionsList><CommentsCorrections RefType="Cites"><RefSource>Neuroimage. 2010 May 15;51(1):488-99</RefSource><PMID Version="1">20083211</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Med Image Comput Comput Assist Interv. 2011;14(Pt 2):655-62</RefSource><PMID 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MajorTopicYN="Y">physiopathology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D060825" MajorTopicYN="N">Cognitive Dysfunction</DescriptorName><QualifierName UI="Q000473" MajorTopicYN="N">pathology</QualifierName><QualifierName UI="Q000503" MajorTopicYN="N">physiopathology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D016208" MajorTopicYN="N">Databases, Factual</DescriptorName><QualifierName UI="Q000706" MajorTopicYN="N">statistics & numerical data</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D018450" MajorTopicYN="N">Disease Progression</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D006624" MajorTopicYN="N">Hippocampus</DescriptorName><QualifierName UI="Q000473" MajorTopicYN="Y">pathology</QualifierName><QualifierName UI="Q000503" MajorTopicYN="Y">physiopathology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D007091" MajorTopicYN="N">Image Processing, Computer-Assisted</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D016015" MajorTopicYN="N">Logistic Models</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D008279" MajorTopicYN="N">Magnetic Resonance Imaging</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D008959" MajorTopicYN="Y">Models, Neurological</DescriptorName></MeshHeading></MeshHeadingList><OtherID Source="NLM">PMC4053641</OtherID><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">Alzheimer's disease</Keyword><Keyword MajorTopicYN="N">Brain imaging</Keyword><Keyword MajorTopicYN="N">Coefficient map</Keyword><Keyword MajorTopicYN="N">Deformation model</Keyword><Keyword MajorTopicYN="N">Disease progression</Keyword><Keyword MajorTopicYN="N">Karcher mean</Keyword><Keyword MajorTopicYN="N">LDDMM</Keyword><Keyword MajorTopicYN="N">Logistic regression</Keyword><Keyword MajorTopicYN="N">Spatial regularization</Keyword><Keyword MajorTopicYN="N">Transport</Keyword></KeywordList><GeneralNote Owner="NLM">Original DateCompleted: 20140617</GeneralNote></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2013</Year><Month>09</Month><Day>19</Day></PubMedPubDate><PubMedPubDate PubStatus="revised"><Year>2014</Year><Month>01</Month><Day>22</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2014</Year><Month>02</Month><Day>14</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2014</Year><Month>6</Month><Day>18</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2014</Year><Month>6</Month><Day>18</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2014</Year><Month>6</Month><Day>18</Day><Hour>6</Hour><Minute>1</Minute></PubMedPubDate></History><PublicationStatus>epublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">24936423</ArticleId><ArticleId IdType="doi">10.1016/j.nicl.2014.02.002</ArticleId><ArticleId IdType="pii">S2213-1582(14)00020-5</ArticleId><ArticleId IdType="pmc">PMC4053641</ArticleId></ArticleIdList></PubmedData></pubmed><affiliations><list><country><li>Australie</li><li>France</li></country></list><tree><country name="France"><noRegion><name sortKey="Fiot, Jean Baptiste" sort="Fiot, Jean Baptiste" uniqKey="Fiot J" first="Jean-Baptiste" last="Fiot">Jean-Baptiste Fiot</name></noRegion><name sortKey="Cohen, Laurent D" sort="Cohen, Laurent D" uniqKey="Cohen L" first="Laurent D" last="Cohen">Laurent D. Cohen</name><name sortKey="Raguet, Hugo" sort="Raguet, Hugo" uniqKey="Raguet H" first="Hugo" last="Raguet">Hugo Raguet</name><name sortKey="Risser, Laurent" sort="Risser, Laurent" uniqKey="Risser L" first="Laurent" last="Risser">Laurent Risser</name><name sortKey="Vialard, Francois Xavier" sort="Vialard, Francois Xavier" uniqKey="Vialard F" first="François-Xavier" last="Vialard">François-Xavier Vialard</name></country><country name="Australie"><noRegion><name sortKey="Fripp, Jurgen" sort="Fripp, Jurgen" uniqKey="Fripp J" first="Jurgen" last="Fripp">Jurgen Fripp</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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