TY - JOUR
T1 - Adaptive prior probability and spatial temporal intensity change estimation for segmentation of the one-year-old human brain
AU - Kim, Sun Hyung
AU - Fonov, Vladimir S.
AU - Dietrich, Cheryl
AU - Vachet, Clement
AU - Hazlett, Heather C.
AU - Smith, Rachel G.
AU - Graves, Michael M.
AU - Piven, Joseph
AU - Gilmore, John H.
AU - Dager, Stephen R.
AU - McKinstry, Robert C.
AU - Paterson, Sarah
AU - Evans, Alan C.
AU - Collins, D. Louis
AU - Gerig, Guido
AU - Styner, Martin Andreas
N1 - Funding Information:
Additional support is provided by the following grants: NIH grants P50 MH 064065 (JHG, MAS), MH070890 (JHG, MAS), HD053000 (JHG), and UNC Intellectual and Developmental Disabilities Research Center P30 HD03110 (MAS, SJS), R01 MH091645 (MAS), NIH Roadmap Grant U54 EB005149-01 (MAS).
Funding Information:
Funding was provided primarily from the IBIS (Infant Brain Imaging Study) Network , an NIH funded Autism Center of Excellence ( HDO55741 ) that consists of a consortium of 7 Universities in the U.S. and Canada. Clinical Sites: University of North Carolina: J. Piven (IBIS Network PI), H.C. Hazlett, C. Chappell; University of Washington: S.R. Dager, A. Estes; Washington University: K. Botteron, R.C. McKinstry, J. Contstantino, L. Flake; Children's Hospital of Philadelphia: R. Schultz, S. Paterson; University of Alberta: L. Zwaigenbaum. Data Coordinating Center: Montreal Neurological Institute: A. Evans, L. Collins, B. Pike, V.S. Fonov, R. Aleong, S. Das. Image Processing Core: University of Utah: G. Gerig; University of North Carolina: M.A. Styner. Statistical Analysis Core: University of North Carolina: H. Gu. Genetics Analysis Core: University of North Carolina: P. Sullivan, F. Wright.
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
KW - Expectation Maximization algorithm
KW - Intensity growth map
KW - Myelination
KW - Partial volume estimation
KW - Tissue segmentation
UR - http://www.scopus.com/inward/record.url?scp=84867742702&partnerID=8YFLogxK
U2 - 10.1016/j.jneumeth.2012.09.018
DO - 10.1016/j.jneumeth.2012.09.018
M3 - Article
C2 - 23032117
AN - SCOPUS:84867742702
SN - 0165-0270
VL - 212
SP - 43
EP - 55
JO - Journal of Neuroscience Methods
JF - Journal of Neuroscience Methods
IS - 1
ER -