Spatial intensity prior correction for tissue segmentation in the developing human brain

Update item information
Publication Type pre-print
School or College <blank>
Department <blank>
Creator Gerig, Guido
Other Author Kim, Sun Hyung; Fonov, Vladimir; Piven, Joe; Gilmore, John; Vachet, Clement; Collins, D. Louis; Styner, Martin
Title Spatial intensity prior correction for tissue segmentation in the developing human brain
Date 2011-01-01
Description The degree of white matter (WM) myelination is rather inhomogeneous across the brain. As a consequence, white matter appears differently across the cortical lobes in MR images acquired during early postnatal development. At 1 year old specifically, the gray/white matter contrast of MR images in prefrontal and temporal lobes is limited and thus tissue segmentation results show commonly reduce accuracy in these lobes. In this novel work, we propose the use of spatial intensity growth maps (IGM) for T1 and T2 weighted image to compensate for local appearance inhomogeneity. The IGM captures expected intensity changes from 1 to 2 years of age, as appearance inhomogeneity is highly reduced by the age of 24 months. For that purpose, we employ MRI data from a large dataset of longitudinal (12 and 24 month old subjects) MR study of Autism. The IGM creation is based on automatically co-registered images at 12 months, corresponding registered 24 months images, and a final registration of all image to a prior average template. In template space, voxelwise correspondence is thus achieved and the IGM is computed as the coefficient of a voxelwise linear regression model between corresponding intensities at 1-year and 2-years. The proposed IGM shows low regression values of 1-10% in GM and CSF regions, as well as in WM regions at advanced stage of myelination at 1-year. However, in the prefrontal and temporal lobe we observed regression values of 20-25%, indicating that the IGM appropriately captures the expected large intensity change in these lobes 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 corrected dataset. We validated our approach in a small study of images with known, manual "ground truth" segmentations. We furthermore present an EM-like optimization of adapting existing non-optimal prior atlas probability maps to fit known expert rater segmentations.
Type Text
Publisher Institute of Electrical and Electronics Engineers (IEEE)
Volume 2011
First Page 2049
Last Page 2052
Language eng
Bibliographic Citation Kim, H., Fonov, V., Piven, J., Gilmore, J., Vachet, C., Gerig, G., Collins, D. L., & Styner, M. (2011). Spatial intensity prior correction for tissue segmentation in the developing human brain. Proceedings of IEEE ISBI 2011, 2049-52.
Rights Management (c) 2011 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Format Medium application/pdf
Format Extent 540,842 bytes
Identifier uspace,19193
ARK ark:/87278/s6kw8r54
Setname ir_uspace
Date Created 2015-01-24
Date Modified 2021-05-06
ID 712784
Reference URL
Back to Search Results