title: Neonatal brain image segmentation in longitudinal MRI studies.
abstract: In the study of early brain development, tissue segmentation of neonatal brain MR images remains challenging because of the insufficient image quality due to the properties of developing tissues. Among various brain tissue segmentation algorithms, atlas-based brain image segmentation can potentially achieve good segmentation results on neonatal brain images. However, their performances rely on both the quality of the atlas and the spatial correspondence between the atlas and the to-be-segmented image. Moreover, it is difficult to build a population atlas for neonates due to the requirement of a large set of tissue-segmented neonatal brain images. To combat these obstacles, we present a longitudinal neonatal brain image segmentation framework by taking advantage of the longitudinal data acquired at late time-point to build a subject-specific tissue probabilistic atlas. Specifically, tissue segmentation of the neonatal brain is formulated as two iterative steps of bias correction and probabilistic-atlas-based tissue segmentation, along with the longitudinal atlas reconstructed by the late time image of the same subject. The proposed method has been evaluated qualitatively through visual inspection and quantitatively by comparing with manual delineations and two population-atlas-based segmentation methods. Experimental results show that the utilization of a subject-specific probabilistic atlas can substantially improve tissue segmentation of neonatal brain images.
authors: IDEA Lab, Department of Radiology and BRIC, University of North Carolina at Chapel Hill, 106 Mason Farm Road, Chapel Hill, NC 27599, USA.
authors: Feng
authors: F
authors: Shi
authors: Yong
authors: Y
authors: Fan
authors: Songyuan
authors: S
authors: Tang
authors: John H
authors: JH
authors: Gilmore
authors: Weili
authors: W
authors: Lin
authors: Dinggang
authors: D
authors: Shen
mesh_terms: D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology* / Q000254:growth & development*; D017143:Fuzzy Logic; D006801:Humans; D007091:Image Processing, Computer-Assisted; D007223:Infant; D007231:Infant, Newborn; D008137:Longitudinal Studies; D008279:Magnetic Resonance Imaging; D012984:Software