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

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 stru...

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Bibliographic Details
Published in:International journal for computer assisted radiology and surgery 2013-01, Vol.8 (1), p.99-110
Main Authors: Haegelen, Claire, Coupé, Pierrick, Fonov, Vladimir, Guizard, Nicolas, Jannin, Pierre, Morandi, Xavier, Collins, D. Louis
Format: Article
Language:English
Subjects:
Algorithms
Basal Ganglia - pathology
Bioengineering
Computer Imaging
Computer Science
Engineering Sciences
Female
Health Informatics
Humans
Image Interpretation, Computer-Assisted - methods
Image Processing
Imaging
Life Sciences
Magnetic Resonance Imaging - methods
Male
Medical Imaging
Medicine
Medicine & Public Health
Middle Aged
Original Article
Parkinson Disease - diagnosis
Pattern Recognition and Graphics
Pattern Recognition, Automated - methods
Radiology
Reproducibility of Results
Signal and Image processing
Subthalamic Nucleus - pathology
Subtraction Technique
Surgery
Vision
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Summary: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  
ISSN:1861-6410
1861-6429
DOI:10.1007/s11548-012-0675-8