Automated segmentation of basal ganglia and deep brain structures in MRI of Parkinson's disease.
Identifieur interne : 000950 ( PubMed/Checkpoint ); précédent : 000949; suivant : 000951Automated segmentation of basal ganglia and deep brain structures in MRI of Parkinson's disease.
Auteurs : Claire Haegelen [Canada] ; Pierrick Coupé ; Vladimir Fonov ; Nicolas Guizard ; Pierre Jannin ; Xavier Morandi ; D Louis CollinsSource :
- International journal of computer assisted radiology and surgery [ 1861-6429 ] ; 2013.
English descriptors
- KwdEn :
- Algorithms, Basal Ganglia (pathology), Female, Humans, Image Interpretation, Computer-Assisted (methods), Magnetic Resonance Imaging (methods), Male, Middle Aged, Parkinson Disease (diagnosis), Pattern Recognition, Automated (methods), Reproducibility of Results, Subthalamic Nucleus (pathology), Subtraction Technique.
- MESH :
- diagnosis : Parkinson Disease.
- methods : Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Pattern Recognition, Automated.
- pathology : Basal Ganglia, Subthalamic Nucleus.
- Algorithms, Female, Humans, Male, Middle Aged, Reproducibility of Results, Subtraction Technique.
Abstract
Template-based segmentation techniques have been developed to facilitate the accurate targeting of deep brain structures in patients with movement disorders. Three template-based brain MRI segmentation techniques were compared to determine the best strategy for segmenting the deep brain structures of patients with Parkinson's disease.
DOI: 10.1007/s11548-012-0675-8
PubMed: 22426551
Affiliations:
Links toward previous steps (curation, corpus...)
Links to Exploration step
pubmed:22426551Le document en format XML
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<author><name sortKey="Haegelen, Claire" sort="Haegelen, Claire" uniqKey="Haegelen C" first="Claire" last="Haegelen">Claire Haegelen</name>
<affiliation wicri:level="1"><nlm:affiliation>McConnell Brain Imaging Centre, Montreal Neurological Institute, 3801 University Street, Montreal, QC, H3A 2B4, Canada. Claire.HAEGELEN@chu-rennes.fr</nlm:affiliation>
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<series><title level="j">International journal of computer assisted radiology and surgery</title>
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<profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Algorithms</term>
<term>Basal Ganglia (pathology)</term>
<term>Female</term>
<term>Humans</term>
<term>Image Interpretation, Computer-Assisted (methods)</term>
<term>Magnetic Resonance Imaging (methods)</term>
<term>Male</term>
<term>Middle Aged</term>
<term>Parkinson Disease (diagnosis)</term>
<term>Pattern Recognition, Automated (methods)</term>
<term>Reproducibility of Results</term>
<term>Subthalamic Nucleus (pathology)</term>
<term>Subtraction Technique</term>
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<keywords scheme="MESH" qualifier="diagnosis" xml:lang="en"><term>Parkinson Disease</term>
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<keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Image Interpretation, Computer-Assisted</term>
<term>Magnetic Resonance Imaging</term>
<term>Pattern Recognition, Automated</term>
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<term>Subthalamic Nucleus</term>
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<term>Female</term>
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<front><div type="abstract" xml:lang="en">Template-based segmentation techniques have been developed to facilitate the accurate targeting of deep brain structures in patients with movement disorders. Three template-based brain MRI segmentation techniques were compared to determine the best strategy for segmenting the deep brain structures of patients with Parkinson's disease.</div>
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<Day>20</Day>
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<Title>International journal of computer assisted radiology and surgery</Title>
<ISOAbbreviation>Int J Comput Assist Radiol Surg</ISOAbbreviation>
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<ArticleTitle>Automated segmentation of basal ganglia and deep brain structures in MRI of Parkinson's disease.</ArticleTitle>
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<ELocationID EIdType="doi" ValidYN="Y">10.1007/s11548-012-0675-8</ELocationID>
<Abstract><AbstractText Label="PURPOSE" NlmCategory="OBJECTIVE">Template-based segmentation techniques have been developed to facilitate the accurate targeting of deep brain structures in patients with movement disorders. Three template-based brain MRI segmentation techniques were compared to determine the best strategy for segmenting the deep brain structures of patients with Parkinson's disease.</AbstractText>
<AbstractText Label="METHODS" NlmCategory="METHODS">T1-weighted and T2-weighted magnetic resonance (MR) image templates were created by averaging MR images of 57 patients with Parkinson's disease. Twenty-four deep brain structures were manually segmented on the templates. To validate the template-based segmentation, 14 of the 24 deep brain structures from the templates were manually segmented on 10 MR scans of Parkinson's patients as a gold standard. We compared the manual segmentations with three methods of automated segmentation: two registration-based approaches, automatic nonlinear image matching and anatomical labeling (ANIMAL) and symmetric image normalization (SyN), and one patch-label fusion technique. The automated labels were then compared with the manual labels using a Dice-kappa metric and center of gravity. A Friedman test was used to compare the Dice-kappa values and paired t tests for the center of gravity.</AbstractText>
<AbstractText Label="RESULTS" NlmCategory="RESULTS">The Friedman test showed a significant difference between the three methods for both thalami (p < 0.05) and not for the subthalamic nuclei. Registration with ANIMAL was better than with SyN for the left thalamus and was better than the patch-based method for the right thalamus.</AbstractText>
<AbstractText Label="CONCLUSION" NlmCategory="CONCLUSIONS">Although template-based approaches are the most used techniques to segment basal ganglia by warping onto MR images, we found that the patch-based method provided similar results and was less time-consuming. Patch-based method may be preferable for the subthalamic nucleus segmentation in patients with Parkinson's disease.</AbstractText>
</Abstract>
<AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Haegelen</LastName>
<ForeName>Claire</ForeName>
<Initials>C</Initials>
<AffiliationInfo><Affiliation>McConnell Brain Imaging Centre, Montreal Neurological Institute, 3801 University Street, Montreal, QC, H3A 2B4, Canada. Claire.HAEGELEN@chu-rennes.fr</Affiliation>
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<ForeName>Pierrick</ForeName>
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<Author ValidYN="Y"><LastName>Fonov</LastName>
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<Author ValidYN="Y"><LastName>Morandi</LastName>
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<Author ValidYN="Y"><LastName>Collins</LastName>
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<MeshHeadingList><MeshHeading><DescriptorName UI="D000465" MajorTopicYN="N">Algorithms</DescriptorName>
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