A comparison of accurate automatic hippocampal segmentation methods
A comparison of accurate automatic hippocampal segmentation methods
Date
2017
Authors
Editors
Journal ISSN
Electronic ISSN
ISBN
Bibliographical data
Publisher
Series
URI (citable link)
DOI (citable link)
International patent number
Link to the license
EU project number
Project
Open Access publication
Collections
Title in another language
Publication type
Journal article
Publication status
Published
Published in
NeuroImage ; 155 (2017). - pp. 383-393. - ISSN 1053-8119. - eISSN 1095-9572
Abstract
The hippocampus is one of the first brain structures affected by Alzheimer's disease (AD). While many automatic methods for hippocampal segmentation exist, few studies have compared them on the same data. In this study, we compare four fully automated hippocampal segmentation methods in terms of their conformity with manual segmentation and their ability to be used as an AD biomarker in clinical settings. We also apply error correction to the four automatic segmentation methods, and complete a comprehensive validation to investigate differences between the methods. The effect size and classification performance is measured for AD versus normal control (NC) groups and for stable mild cognitive impairment (sMCI) versus progressive mild cognitive impairment (pMCI) groups. Our study shows that the nonlinear patch-based segmentation method with error correction is the most accurate automatic segmentation method and yields the most conformity with manual segmentation (κ=0.894). The largest effect size between AD versus NC and sMCI versus pMCI is produced by FreeSurfer with error correction. We further show that, using only hippocampal volume, age, and sex as features, the area under the receiver operating characteristic curve reaches up to 0.8813 for AD versus NC and 0.6451 for sMCI versus pMCI. However, the automatic segmentation methods are not significantly different in their performance.
Summary in another language
Subject (DDC)
150 Psychology
Keywords
Hippocampal segmentation; Alzheimer's disease; Dice's κ; Cohen's d; Area under receiver operating characteristic curve
Conference
Review
undefined / . - undefined, undefined. - (undefined; undefined)
Cite This
ISO 690
ZANDIFAR, Azar, Vladimir FONOV, Pierrick COUPÉ, Jens C. PRUESSNER, D. Louis COLLINS, 2017. A comparison of accurate automatic hippocampal segmentation methods. In: NeuroImage. 155, pp. 383-393. ISSN 1053-8119. eISSN 1095-9572. Available under: doi: 10.1016/j.neuroimage.2017.04.018BibTex
@article{Zandifar2017-07-15compa-41155, year={2017}, doi={10.1016/j.neuroimage.2017.04.018}, title={A comparison of accurate automatic hippocampal segmentation methods}, volume={155}, issn={1053-8119}, journal={NeuroImage}, pages={383--393}, author={Zandifar, Azar and Fonov, Vladimir and Coupé, Pierrick and Pruessner, Jens C. and Collins, D. Louis} }
RDF
<rdf:RDF xmlns:dcterms="http://purl.org/dc/terms/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:bibo="http://purl.org/ontology/bibo/" xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#" xmlns:foaf="http://xmlns.com/foaf/0.1/" xmlns:void="http://rdfs.org/ns/void#" xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > <rdf:Description rdf:about="https://kops.uni-konstanz.de/server/rdf/resource/123456789/41155"> <dc:language>eng</dc:language> <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2018-01-25T10:13:48Z</dcterms:available> <dc:contributor>Collins, D. Louis</dc:contributor> <dcterms:title>A comparison of accurate automatic hippocampal segmentation methods</dcterms:title> <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/43"/> <dc:contributor>Fonov, Vladimir</dc:contributor> <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/41155/1/Zandifar_2-skvhn40sgzrz5.pdf"/> <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/41155/1/Zandifar_2-skvhn40sgzrz5.pdf"/> <dc:creator>Zandifar, Azar</dc:creator> <dc:creator>Fonov, Vladimir</dc:creator> <dc:rights>terms-of-use</dc:rights> <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/> <dcterms:issued>2017-07-15</dcterms:issued> <dc:contributor>Zandifar, Azar</dc:contributor> <dc:contributor>Pruessner, Jens C.</dc:contributor> <dc:creator>Pruessner, Jens C.</dc:creator> <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/41155"/> <dc:creator>Collins, D. Louis</dc:creator> <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/43"/> <foaf:homepage rdf:resource="http://localhost:8080/"/> <dc:creator>Coupé, Pierrick</dc:creator> <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2018-01-25T10:13:48Z</dc:date> <dc:contributor>Coupé, Pierrick</dc:contributor> <dcterms:abstract xml:lang="eng">The hippocampus is one of the first brain structures affected by Alzheimer's disease (AD). While many automatic methods for hippocampal segmentation exist, few studies have compared them on the same data. In this study, we compare four fully automated hippocampal segmentation methods in terms of their conformity with manual segmentation and their ability to be used as an AD biomarker in clinical settings. We also apply error correction to the four automatic segmentation methods, and complete a comprehensive validation to investigate differences between the methods. The effect size and classification performance is measured for AD versus normal control (NC) groups and for stable mild cognitive impairment (sMCI) versus progressive mild cognitive impairment (pMCI) groups. Our study shows that the nonlinear patch-based segmentation method with error correction is the most accurate automatic segmentation method and yields the most conformity with manual segmentation (κ=0.894). The largest effect size between AD versus NC and sMCI versus pMCI is produced by FreeSurfer with error correction. We further show that, using only hippocampal volume, age, and sex as features, the area under the receiver operating characteristic curve reaches up to 0.8813 for AD versus NC and 0.6451 for sMCI versus pMCI. However, the automatic segmentation methods are not significantly different in their performance.</dcterms:abstract> <dcterms:rights rdf:resource="https://rightsstatements.org/page/InC/1.0/"/> </rdf:Description> </rdf:RDF>
Internal note
xmlui.Submission.submit.DescribeStep.inputForms.label.kops_note_fromSubmitter
Examination date of dissertation
Method of financing
Comment on publication
Alliance license
Corresponding Authors der Uni Konstanz vorhanden
International Co-Authors
Bibliography of Konstanz
Yes