Automated segmentation of basal ganglia and deep brain structures in MRI of Parkinson’s disease
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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.
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.
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.
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.
KeywordsBasal ganglia MRI template Patch-based method Parkinson’s disease Segmentation
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- 6.Bardinet E, Bhattacharjee M, Dormont D, Pidoux B, Malandain G, Schüpbach M, Ayache N, Cornu P, Agid Y, Yelnik J (2009) A three-dimensional histological atlas of the human basal ganglia. II. Atlas deformation strategy and evaluation in deep brain stimulation for Parkinson disease. J Neurosurg 110: 208–219PubMedCrossRefGoogle Scholar
- 9.Schaltenbrand G, Wahren W (1977) Atlas for stereotaxy of the human brain. Thieme, StuttgartGoogle Scholar
- 10.Talairach J, Tournoux P (1988) Co-planar stereotaxic atlas of the human brain. Georg Thieme Verlag, StuttgartGoogle Scholar
- 11.Yelnik J, Bardinet E, Dormont D, Malandain G, Ourselin S, Tandé D, Karachi K, Ayache N, Cornu P, Agid Y (2007) A three-dimensional, histological and deformable atlas of the human basal ganglia. I. Atlas construction based on immunohistochemical and MRI data. Neuroimage 34: 618–638PubMedCrossRefGoogle Scholar
- 12.D’Haese PF, Pallavaram S, Li R, Remple MS, Kao C, Neimat JS, Konrad PE, Dawant BM (2010) CranialVault and its CRAVE tools: a clinical computer assistance system for deep brain stimulation (DBS) therapy. Med Image Anal. doi:10.1016/j.media.2010.07.009
- 19.Neelin P (1998) The MINC file format:from bytes to brains. NeuroImage 7(4): 786Google Scholar
- 25.Mai JK, Paxinos G, Voss T (2008) Atlas of the human brain. Elsevier Inc, AmsterdamGoogle Scholar
- 30.Bhattachargee M, Pitiot A, Roche A, Dormont D, Bardinet E (2008) Anatomy-preserving nonlinear registration of deep brain ROIs using confidence-based block-matching. In: LNCS MICCAI, part II, Springer, vol 5242, pp 956–963. doi:10.1007/978-3-540-85990-1_115
- 31.Bardinet E, Dormont D, Malandain G, Bhattachargee M, Pidoux B, Saleh C, Cornu P, Ayache N, Agid Y, Yelnik J (2005) Retrospective cross-evaluation of an histological and deformable 3D atlas of the basal ganglia on series of Parkinsonian patients treated by deep brain stimulation. In: LNCS MICCAI, Springer, vol 3750, pp 385–393. doi:10.11007/11566489_48
- 32.Chakravarty MM, Sadikot AF, Germann J, Hellier P, Bertrand G, Collins DL (2009) Comparison of piece-wise linear, linear, and nonlinear atlas-to-patient warping techniques: analysis of the labelling of subcortical nuclei for functional neurosurgical applications. Hum Brain Mapp 30: 3574–3595PubMedCrossRefGoogle Scholar
- 33.Klein A, Andersson J, Ardekani BA, Ashburner J, Avants B, Chiang MC, Christensen GE, Collins DL, Gee J, Hellier P, Song JH, Jenkinson M, Lepage C, Rueckert D, Thompson P, Vercauteren T, Woods RP, Mann JJ, Parsey RV (2009) Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration. Neuroimage 46: 786–802PubMedCrossRefGoogle Scholar
- 37.Antonini A, Isaias IU, Rodolfi G, Landi A, Natuzzi F, Siri C, Pezzoli G (2011) A 5-year prospective assessment of advanced Parkinson disease patients treated with subcutaneous apomor- phine infusion or deep brain stimulation. J Neurol 258: 579–585. doi:10.1007/s00415-010-5793-z PubMedCrossRefGoogle Scholar