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

Claire Haegelen 1, 2, 3, * Pierrick Coupé 3, 4 Vladimir Fonov 3 Nicolas Guizard 3 Pierre Jannin 1 Xavier Morandi 1 D Louis Collins 3
* Auteur correspondant
1 VisAGeS - Vision, Action et Gestion d'informations en Santé
INSERM - Institut National de la Santé et de la Recherche Médicale : U746, Inria Rennes – Bretagne Atlantique , IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
2 Service de neurochirurgie [Rennes]
3 MNI - McConnell Brain Imaging Centre
4 LaBRI - Laboratoire Bordelais de Recherche en Informatique
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.
Keywords : Basal ganglia MRI template Patch-based method Parkinson's disease Segmentation
Type de document :
Article dans une revue
International Journal of Computer Assisted Radiology and Surgery, Springer Verlag, 2013, 8 (1), pp.99-110. <10.1007/s11548-012-0675-8>
Liste complète des métadonnées
https://hal.archives-ouvertes.fr/hal-00683777
Contributeur : Pierrick Coupé <>
Soumis le : vendredi 30 mars 2012 - 11:21:18
Dernière modification le : jeudi 9 février 2017 - 15:35:18
Document(s) archivé(s) le : vendredi 31 mars 2017 - 03:49:50

Fichiers

hal-00683777_edited.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

Collections

INRIA | LABRI | UNIV-BORDEAUX | IRISA-D5 | IRISA | CENTRALESUPELEC | UR1-UFR-ISTIC

Citation

Claire Haegelen, Pierrick Coupé, Vladimir Fonov, Nicolas Guizard, Pierre Jannin, et al.. Automated segmentation of basal ganglia and deep brain structures in MRI of Parkinson's disease. International Journal of Computer Assisted Radiology and Surgery, Springer Verlag, 2013, 8 (1), pp.99-110. <10.1007/s11548-012-0675-8>. <hal-00683777v2>

Exporter

BibTeX TEI DC DCterms EndNote

Partager

Métriques

Consultations de
la notice

1189

Téléchargements du document

612

Contact

support.ccsd.cnrs.fr
hal.support@ccsd.cnrs.fr
Logo CCSD