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. Author manuscript; available in PMC: 2012 Jan 30.
Published in final edited form as: IEEE Trans Med Imaging. 2010 Jun 17;29(10):1714–1729. doi: 10.1109/TMI.2010.2050897

Fig. 7.

Fig. 7

The segmentations of the subject that Semi-local Weighted Fusion performed the worst on. Left to right: FreeSurfer, Global and Semi-local Weighted Fusion. Common mistakes (indicated by arrows): (A) Global Weighted Fusion tends to over-segment complex shapes like the cortex. (B) Semi-local Weighted Fusion does not encode topological information, as FreeSurfer does. Hence it may assign an “unknown” or “background” label (white) in between the pallidum (blue), putamen (pink), and white matter (green).