Comparison of piece-wise linear, linear, and nonlinear atlas-to-patient warping techniques: analysis of the labeling of subcortical nuclei for functional neurosurgical applications.
Digital atlases are commonly used in pre-operative planning in functional neurosurgical procedures performed to minimize the symptoms of Parkinson's disease. These atlases can be customized to fit an individual patient's anatomy through atlas-to-patient warping procedures. Once fitted to pre-operative magnetic resonance imaging (MRI) data, the customized atlas can be used to plan and navigate surgical procedures. Linear, piece-wise linear and nonlinear registration methods have been used to customize different digital atlases with varying accuracies. Our goal was to evaluate eight different registration methods for atlas-to-patient customization of a new digital atlas of the basal ganglia and thalamus to demonstrate the value of nonlinear registration for automated atlas-based subcortical target identification in functional neurosurgery. In this work, we evaluate the accuracy of two automated linear techniques, two piece-wise linear techniques (requiring the identification of manually placed anatomical landmarks), and four different automated nonlinear atlas-to-patient warping techniques (where two of the four nonlinear techniques are variants of the ANIMAL algorithm). Since a gold standard of the subcortical anatomy is not available, manual segmentations of the striatum, globus pallidus, and thalamus are used to derive a silver standard for evaluation. Four different metrics, including the kappa statistic, the mean distance between the surfaces, the maximum distance between surfaces, and the total structure volume are used to compare the warping techniques. The results show that nonlinear techniques perform statistically better than linear and piece-wise linear techniques. In addition, the results demonstrate statistically significant differences between the nonlinear techniques, with the ANIMAL algorithm yielding better results.
Chakravarty
M Mallar
MM
McConnell Brain Imaging Center, Montréal Neurological Institute, McGill University, Quebec, Canada. mallar@bic.mni.mcgill.ca
Sadikot
Abbas F
AF
Germann
Jürgen
J
Hellier
Pierre
P
Bertrand
Gilles
G
Collins
D Louis
DL
D000465:Algorithms; D000704:Analysis of Variance; D000818:Animals; D001479:Basal Ganglia / Q000033:anatomy & histology / Q000473:pathology; D001931:Brain Mapping; D007839:Functional Laterality / Q000502:physiology; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods; D009422:Nervous System Diseases / Q000473:pathology*; D017711:Nonlinear Dynamics; D010363:Pattern Recognition, Automated / Q000379:methods*; D012189:Retrospective Studies; D013788:Thalamus / Q000033:anatomy & histology / Q000473:pathology