title: CENTS: cortical enhanced neonatal tissue segmentation.
abstract: The acquisition of high-quality magnetic resonance (MR) images of neonatal brains is largely hampered by their characteristically small head size and insufficient tissue contrast. As a result, subsequent image processing and analysis, especially brain tissue segmentation, are often affected. To overcome this problem, a dedicated phased array neonatal head coil is utilized to improve MR image quality by augmenting signal-to-noise ratio and spatial resolution without lengthening data acquisition time. In addition, a specialized hybrid atlas-based tissue segmentation algorithm is developed for the delineation of fine structures in the acquired neonatal brain MR images. The proposed tissue segmentation method first enhances the sheet-like cortical gray matter (GM) structures in the to-be-segmented neonatal image with a Hessian filter for generation of a cortical GM confidence map. A neonatal population atlas is then generated by averaging the presegmented images of a population, weighted by their cortical GM similarity with respect to the to-be-segmented image. Finally, the neonatal population atlas is combined with the GM confidence map, and the resulting enhanced tissue probability maps for each tissue form a hybrid atlas is used for atlas-based segmentation. Various experiments are conducted to compare the segmentations of the proposed method with manual segmentation (on both images acquired with a dedicated phased array coil and a conventional volume coil), as well as with the segmentations of two population-atlas-based methods. Results show the proposed method is capable of segmenting the neonatal brain with the best accuracy, and also preserving the most structural details in the cortical regions.
authors.affiliation: IDEA Lab, Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599-7515, USA.
authors.forename: Feng
authors.initials: F
authors.lastname: Shi
authors.forename: Dinggang
authors.initials: D
authors.lastname: Shen
authors.forename: Pew-Thian
authors.initials: PT
authors.lastname: Yap
authors.forename: Yong
authors.initials: Y
authors.lastname: Fan
authors.forename: Jie-Zhi
authors.initials: JZ
authors.lastname: Cheng
authors.forename: Hongyu
authors.initials: H
authors.lastname: An
authors.forename: Lawrence L
authors.initials: LL
authors.lastname: Wald
authors.forename: Guido
authors.initials: G
authors.lastname: Gerig
authors.forename: John H
authors.initials: JH
authors.lastname: Gilmore
authors.forename: Weili
authors.initials: W
authors.lastname: Lin
mesh_terms: D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology* / Q000502:physiology*; D001931:Brain Mapping; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D007223:Infant; D007231:Infant, Newborn; D008279:Magnetic Resonance Imaging; D008297:Male; D010363:Pattern Recognition, Automated