title: Adaptive prior probability and spatial temporal intensity change estimation for segmentation of the one-year-old human brain.
abstract: The degree of white matter (WM) myelination is rather inhomogeneous across the brain. White matter appears differently across the cortical lobes in MR images acquired during early postnatal development. Specifically at 1-year of age, the gray/white matter contrast of MR T1 and T2 weighted images in prefrontal and temporal lobes is reduced as compared to the rest of the brain, and thus, tissue segmentation results commonly show lower accuracy in these lobes. In this novel work, we propose the use of spatial intensity growth maps (IGM) for T1 and T2 weighted images to compensate for local appearance inhomogeneity. The IGM captures expected intensity changes from 1 to 2 years of age, as appearance homogeneity is greatly improved by the age of 24 months. The IGM was computed as the coefficient of a voxel-wise linear regression model between corresponding intensities at 1 and 2 years. The proposed IGM method revealed low regression values of 1-10% in GM and CSF regions, as well as in WM regions at maturation stage of myelination at 1 year. However, in the prefrontal and temporal lobes we observed regression values of 20-25%, indicating that the IGM appropriately captures the expected large intensity change in these lobes mainly due to myelination. The IGM is applied to cross-sectional MRI datasets of 1-year-old subjects via registration, correction and tissue segmentation of the IGM-corrected dataset. We validated our approach in a small leave-one-out study of images with known, manual 'ground truth' segmentations.
authors.affiliation: Department of Psychiatry, University of North Carolina at Chapel Hill, USA. shykim@email.unc.edu
authors.forename: Sun Hyung
authors.initials: SH
authors.lastname: Kim
authors.forename: Vladimir S
authors.initials: VS
authors.lastname: Fonov
authors.forename: Cheryl
authors.initials: C
authors.lastname: Dietrich
authors.forename: Clement
authors.initials: C
authors.lastname: Vachet
authors.forename: Heather C
authors.initials: HC
authors.lastname: Hazlett
authors.forename: Rachel G
authors.initials: RG
authors.lastname: Smith
authors.forename: Michael M
authors.initials: MM
authors.lastname: Graves
authors.forename: Joseph
authors.initials: J
authors.lastname: Piven
authors.forename: John H
authors.initials: JH
authors.lastname: Gilmore
authors.forename: Stephen R
authors.initials: SR
authors.lastname: Dager
authors.forename: Robert C
authors.initials: RC
authors.lastname: McKinstry
authors.forename: Sarah
authors.initials: S
authors.lastname: Paterson
authors.forename: Alan C
authors.initials: AC
authors.lastname: Evans
authors.forename: D Louis
authors.initials: DL
authors.lastname: Collins
authors.forename: Guido
authors.initials: G
authors.lastname: Gerig
authors.forename: Martin Andreas
authors.initials: MA
authors.lastname: Styner
mesh_terms: D000367:Age Factors; D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology* / Q000254:growth & development*; D001931:Brain Mapping; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D007223:Infant; D016014:Linear Models; D008279:Magnetic Resonance Imaging; D008297:Male; D008959:Models, Neurological; D009413:Nerve Fibers, Myelinated / Q000502:physiology; D011336:Probability