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Adaptive Prior Probability and Spatial Temporal Intensity Change Estimation for Segmentation of the One-Year-Old Human Brain

Institution:
1Department of Psychiatry, University of North Carolina at Chapel Hill, NC, USA. shykim@email.unc.edu
2McConnell Brain Imaging Center, Montreal Neurological Institute, Montreal, QC, Canada.
3Scientific Computing and Imaging Institute, School of Computing, University of Utah, Salt Lake City, UT, USA.
4Department of Computer Science, University of North Carolina at Chapel Hill, NC, USA.
5Department of Radiology, University of Washington, Seattle, USA.
6Department of Radiology, Washington University, St. Louis, USA.
7Department of Pediatrics, Children's Hospital of Philadelphia, Philadelphia, USA.
Publisher:
Elsevier Science
Publication Date:
Jan-2013
Journal:
J Neurosci Methods
Volume Number:
212
Issue Number:
1
Pages:
43-55
Citation:
J Neurosci Methods. 2013 Jan 15;212(1):43-55.
Links:
http://dx.doi.org/10.1016/j.jneumeth.2012.09.018
PubMed ID:
23032117
PMCID:
PMC3513941
Keywords:
Myelination, Expectation Maximization Algorithm, Tissue segmentation, Intensity growth map, Partial volume estimation
Appears in Collections:
NA-MIC
Sponsors:
R01 HD053000/HD/NICHD NIH HHS/United States
R01 HD055741/HD/NICHD NIH HHS/United States
R01 MH070890/MH/NIMH NIH HHS/United States
P30 HD003110/HD/NICHD NIH HHS/United States
R01 MH070890/MH/NIMH NIH HHS/United States
R01 MH091645/MH/NIMH NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
Generated Citation:
Kim S.H., Fonov V.S., Dietrich C., Vachet C., Hazlett H.C., Smith R.G., Graves M.M., Piven J., Gilmore J.H., Dager S.R., McKinstry R.C., Paterson S., Evans A.C., Collins D.L., Gerig G., Styner M. Adaptive Prior Probability and Spatial Temporal Intensity Change Estimation for Segmentation of the One-Year-Old Human Brain. J Neurosci Methods. 2013 Jan 15;212(1):43-55. PMID: 23032117. PMCID: PMC3513941.
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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.

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