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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