Abstract #1787
Improved deep gray matter segmentation using anatomical information from quantitative susceptibility maps
Ferdinand Schweser 1 , Xiang Feng 1 , Rosa Mach Batlle 1 , Daniel Gllmar 1 , Andreas Deistung 1 , Michael G Dwyer 2 , Robert Zivadinov 2,3 , and Jrgen R Reichenbach 1
1
Medical Physics Group, Institute of
Diagnostic and Interventional Radiology I, Jena
University Hospital - Friedrich Schiller University
Jena, Jena, Germany,
2
Buffalo
Neuroimaging Analysis Center, University at Buffalo SUNY,
Buffalo, New York, United States,
3
Jacobs
Neurological Institute, University at Buffalo SUNY,
Buffalo, New York, United States
Brain image segmentation followed by region-of-interest
(ROI)-based analyses is a way to quantify subtle
variations of MR image intensity. In this contribution
we present an approach to improve the automated
segmentation of deep gray matter with FIRST that relies
on the incorporation of prior anatomical information
from secondary image contrasts with high-contrast in the
critical brain regions. The proposed technique is solely
pre-processing-based and, thus, does not require
modification of the actual segmentation algorithm.
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