Paper Info
Title
Improving fluid registration through white matter segmentation in a twin study design
Abstract
Robust and automatic non-rigid registration depends on many parameters that have not yet been systematically explored. Here we determined how tissue classification influences non-linear fluid registration of brain MRI. Twin data is ideal for studying this question, as volumetric correlations between corresponding brain regions that are under genetic control should be higher in monozygotic twins (MZ) who share 100% of their genes when compared to dizygotic twins (DZ) who share half their genes on average. When these substructure volumes are quantified using tensor-based morphometry, improved registration can be defined based on which method gives higher MZ twin correlations when compared to DZs, as registration errors tend to deplete these correlations. In a study of 92 subjects, higher effect sizes were found in cumulative distribution functions derived from statistical maps when performing tissue classification before fluid registration, versus fluidly registering the raw images. This gives empirical evidence in favor of pre-segmenting images for tensor-based morphometry.
Year
DOI
Venue
2010
10.1117/12.843642
Proceedings of SPIE
Keywords
Field
DocType
tissue classification,registration,MRI,twin study
Computer vision,Brain mri,Tensor,White matter,Segmentation,Dizygotic twins,Cumulative distribution function,Artificial intelligence,Twin study,Physics
Conference
Volume
ISSN
Citations 
7623
0277-786X
0
PageRank 
References 
Authors
0.34
4
9
Name
Order
Citations
PageRank
Yi-Yu Chou129022.25
Natasha Lepore213415.80
c brun300.34
Marina Barysheva426922.02
Katie L. Mcmahon537433.49
Greig I. De Zubicaray649143.04
Margaret J. Wright743639.31
Arthur W. Toga83128261.46
Paul Thompson93860321.32