title: Automated segmentation of basal ganglia and deep brain structures in MRI of Parkinson's disease.
abstract: 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.
  
  
  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.
  
  
  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.
authors.affiliation: McConnell Brain Imaging Centre, Montreal Neurological Institute, 3801 University Street, Montreal, QC, H3A 2B4, Canada. Claire.HAEGELEN@chu-rennes.fr
authors.forename: Claire
authors.initials: C
authors.lastname: Haegelen
authors.forename: Pierrick
authors.initials: P
authors.lastname: Coupé
authors.forename: Vladimir
authors.initials: V
authors.lastname: Fonov
authors.forename: Nicolas
authors.initials: N
authors.lastname: Guizard
authors.forename: Pierre
authors.initials: P
authors.lastname: Jannin
authors.forename: Xavier
authors.initials: X
authors.lastname: Morandi
authors.forename: D Louis
authors.initials: DL
authors.lastname: Collins
mesh_terms: D000465:Algorithms; D001479:Basal Ganglia / Q000473:pathology*; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D010300:Parkinson Disease / Q000175:diagnosis*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D020531:Subthalamic Nucleus / Q000473:pathology*; D013382:Subtraction Technique