Document Detail

Automated segmentation of basal ganglia and deep brain structures in MRI of Parkinson's disease.
MedLine Citation:
PMID:  22426551     Owner:  NLM     Status:  MEDLINE    
PURPOSE: 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.
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.
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.
CONCLUSION: 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.
Claire Haegelen; Pierrick Coupé; Vladimir Fonov; Nicolas Guizard; Pierre Jannin; Xavier Morandi; D Louis Collins
Publication Detail:
Type:  Comparative Study; Journal Article; Research Support, Non-U.S. Gov't     Date:  2012-03-18
Journal Detail:
Title:  International journal of computer assisted radiology and surgery     Volume:  8     ISSN:  1861-6429     ISO Abbreviation:  Int J Comput Assist Radiol Surg     Publication Date:  2013 Jan 
Date Detail:
Created Date:  2013-01-03     Completed Date:  2013-06-04     Revised Date:  2013-08-14    
Medline Journal Info:
Nlm Unique ID:  101499225     Medline TA:  Int J Comput Assist Radiol Surg     Country:  Germany    
Other Details:
Languages:  eng     Pagination:  99-110     Citation Subset:  IM    
McConnell Brain Imaging Centre, Montreal Neurological Institute, 3801 University Street, Montreal, QC, H3A 2B4, Canada.
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MeSH Terms
Basal Ganglia / pathology*
Image Interpretation, Computer-Assisted / methods*
Magnetic Resonance Imaging / methods*
Middle Aged
Parkinson Disease / diagnosis*
Pattern Recognition, Automated / methods*
Reproducibility of Results
Subthalamic Nucleus / pathology*
Subtraction Technique*

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

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