| Automated segmentation of basal ganglia and deep brain structures in MRI of Parkinson's disease. | |
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MedLine Citation:
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PMID: 22426551 Owner: NLM Status: Publisher |
Abstract/OtherAbstract:
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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. |
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Authors:
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Claire Haegelen; Pierrick Coupé; Vladimir Fonov; Nicolas Guizard; Pierre Jannin; Xavier Morandi; D Louis Collins |
Publication Detail:
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Type: JOURNAL ARTICLE Date: 2012-3-18 |
Journal Detail:
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Title: International journal of computer assisted radiology and surgery Volume: - ISSN: 1861-6429 ISO Abbreviation: - Publication Date: 2012 Mar |
Date Detail:
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Created Date: 2012-3-19 Completed Date: - Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 101499225 Medline TA: Int J Comput Assist Radiol Surg Country: - |
Other Details:
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Languages: ENG Pagination: - Citation Subset: - |
Affiliation:
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McConnell Brain Imaging Centre, Montreal Neurological Institute, 3801 University Street, Montreal, QC, H3A 2B4, Canada, Claire.HAEGELEN@chu-rennes.fr. |
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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