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

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

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 and Affiliations

  1. McConnell Brain Imaging Centre, Montreal Neurological Institute, 3801 University Street, Montreal, QC, H3A 2B4, Canada

    Claire Haegelen, Pierrick Coupé, Vladimir Fonov, Nicolas Guizard & D. Louis Collins

  2. Faculty of Medicine, U746, INSERM, Rennes Cedex, France

    Claire Haegelen, Pierre Jannin & Xavier Morandi

  3. VisAGeS Unit/Project, INRIA, Rennes Cedex, France

    Claire Haegelen, Pierre Jannin & Xavier Morandi

  4. UMR 06074, IRISA, CNRS, University of Rennes 1, CS 34317, 35043, Rennes Cedex, France

    Claire Haegelen, Pierre Jannin & Xavier Morandi

  5. Service de Neurochirurgie, Hôpital Pontchaillou, rue Henri Le Guilloux, 35033, Rennes Cedex 9, France

    Claire Haegelen

  6. LaBRI CNRS, UMR 5800, University of Bordeaux, 351 cours de la Liberation, 33045, Talence Cedex, France

    Pierrick Coupé

Authors
  1. Claire Haegelen
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  2. Pierrick Coupé
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  3. Vladimir Fonov
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  4. Nicolas Guizard
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  6. Xavier Morandi
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  7. D. Louis Collins
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Correspondence to Claire Haegelen.

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Haegelen, C., Coupé, P., Fonov, V. et al. Automated segmentation of basal ganglia and deep brain structures in MRI of Parkinson’s disease. Int J CARS 8, 99–110 (2013). https://doi.org/10.1007/s11548-012-0675-8

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  • DOI: https://doi.org/10.1007/s11548-012-0675-8

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