La maladie de Parkinson au Canada (serveur d'exploration)

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Automated segmentation of basal ganglia and deep brain structures in MRI of Parkinson's disease.

Identifieur interne : 000950 ( PubMed/Checkpoint ); précédent : 000949; suivant : 000951

Automated 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 Collins

Source :

RBID : pubmed:22426551

English descriptors

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:


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pubmed:22426551

Le document en format XML

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<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>
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<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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<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>
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