Adaptive prior probability and spatial temporal intensity change estimation for segmentation of the one-year-old human brain

► We propose the intensity growth maps (IGM) to perform segmentation of one-year old data. ► The IGM captured intensity changes of 20–25% in immature WM regions. ► We generate adaptive tissue probability map of one-year old data using IGM. ► IGM-EM has a dice error ratio, GM: 9.75 and WM: 12.66. ► The results of IGM-EM show good performance in temporal and prefrontal lobe areas. 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 2years of age, as appearance homogeneity is greatly improved by the age of 24months. The IGM was computed as the coefficient of a voxel-wise linear regression model between corresponding intensities at 1 and 2years. 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 1year. 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.