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Abstract #4631

Comparison and Reproducibility of Atlas-based Brain Parcellation Methods

Zhaoying Han 1 , Nils Daniel Forkert 1 , Julian Maclaren 1 , Nancy Fischbein 1 , and Roland Bammer 1

1 Department of Radiology, Stanford University, Stanford, California, United States

The automatic atlas-based brain parcellation is an important processing step for longitudinal and cross-sectional brain studies. The aim of this work is to evaluate the robustness of three non-linear registration frameworks (NiftyReg, FSL and ATNS), by applying them to 120 high-resolution T1-weighted datasets acquired multiple times from three healthy subjects. The MNI atlas was registered to each dataset and the resulting non-linear transformations were used to warp the Harvard-Oxford subcortical brain regions to each subject for regional volume determinations. All three registration methods lead to robust brain parcellation results with low standard deviations, but considerable differences between the methods.

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