Comparison of piece‐wise linear, linear, and nonlinear atlas‐to‐patient warping techniques: Analysis of the labeling of subcortical nuclei for functional neurosurgical applications

Abstract

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.

Generation of Individualized Thalamus Target Maps by Using Statistical Shape Models and Thalamocortical Tractography

“…The estimation error was 1.96 mm when using an ACPC alignment strategy and 1.72-1.77 mm with nonlinear matching algorithms. A study by Chakravarty et al 34 revealed that the Talairach coordinate-based targeting misplaced the thalamus outlines by 2.44 Ϯ 0.68 mm. There is converging evidence that image-based atlas-to-patient alignment tools outperform methods like the ACPC alignment technique; this was also indicated by our postmortem evaluations.…”

Electrode Penetration of the Caudate Nucleus in Deep Brain Stimulation Surgery for Parkinson’s Disease

“…Decline in the entire DBS group (containing both CN and no CN penetration) was given (compared to the best medical treatment) without subdividing for CN penetration. Normalization of the data was applied, which facilitated comparisons between subjects; however, the individual interrelationship between DBS trajectory and CN was lost [9, 17]. Taking the findings of Isler et al [11] into account, these differences are possibly due to short-term follow-up.…”

Longitudinal four-dimensional mapping of subcortical anatomy in human development

“…To summarize the technique, 3D reconstruction of serial histological data for the striatum, pallidum, and thalamus were warped to an MRI-based template (19). The atlases were then customized to a subset of the dataset (30 randomly selected subjects) using a nonlinear transformation estimated in a region-of-interest defined around the subcortical structures (17,19,74,75). This set of subjects then acts as a set of templates to which all other scans are warped.…”