title: Cortical Enhanced Tissue Segmentation of Neonatal Brain MR Images Acquired by a Dedicated Phased Array Coil.
abstract: The acquisition of high quality MR images of neonatal brains is largely hampered by their characteristically small head size and low tissue contrast. As a result, subsequent image processing and analysis, especially for brain tissue segmentation, are often hindered. To overcome this problem, a dedicated phased array neonatal head coil is utilized to improve MR image quality by effectively combing images obtained from 8 coil elements without lengthening data acquisition time. In addition, a subject-specific atlas based tissue segmentation algorithm is specifically 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 neonatal images with a Hessian filter for generation of cortical GM prior. Then, the prior is combined with our neonatal population atlas to form a cortical enhanced hybrid atlas, which we refer to as the subject-specific atlas. Various experiments are conducted to compare the proposed method with manual segmentation results, as well as with additional two population atlas based segmentation methods. Results show that the proposed method is capable of segmenting the neonatal brain with the highest accuracy, compared to other two methods.
authors.affiliation: IDEA Lab, Department of Radiology and BRIC, University of North Carolina at Chapel Hill.
authors.forename: Feng
authors.initials: F
authors.lastname: Shi
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: Lawrence L
authors.initials: LL
authors.lastname: Wald
authors.forename: Guido
authors.initials: G
authors.lastname: Gerig
authors.forename: Weili
authors.initials: W
authors.lastname: Lin
authors.forename: Dinggang
authors.initials: D
authors.lastname: Shen