TY - GEN
T1 - Brain tissue segmentation of neonatal MR images using a longitudinal subject-specific probabilistic atlas
AU - Shi, Feng
AU - Fan, Yong
AU - Tang, Songyuan
AU - Gilmore, John
AU - Lin, Weili
AU - Shen, Dinggang
N1 - Copyright:
Copyright 2009 Elsevier B.V., All rights reserved.
PY - 2009
Y1 - 2009
N2 - Brain tissue segmentation of neonate MR images is a challenging task in study of early brain development, due to low signal contrast among brain tissues and high intensity variability especially in white matter. Among various brain tissue segmentation algorithms, the atlas-based segmentation techniques can potentially produce reasonable segmentation results on neonatal brain images. However, their performance on the population-based atlas is still limited due to the high variability of brain structures across different individuals. Moreover, it may be impossible to generate a reasonable probabilistic atlas for neonates without tissue segmentation samples. To overcome these limitations, we present a neonatal brain tissue segmentation method by taking advantage of the longitudinal data available in our study to establish a subject-specific probabilistic atlas. In particular, tissue segmentation of the neonatal brain is formulated as two iterative steps of bias correction and probabilistic atlas based tissue segmentation, along with the guidance of brain tissue segmentation resulted from the later time images of the same subject which serve as a subject-specific probabilistic atlas. The proposed method has been evaluated qualitatively through visual inspection and quantitatively by comparing with manual delineation results. Experimental results show that the utilization of a subject-specific probabilistic atlas can substantially improve tissue segmentation of neonatal brain images.
AB - Brain tissue segmentation of neonate MR images is a challenging task in study of early brain development, due to low signal contrast among brain tissues and high intensity variability especially in white matter. Among various brain tissue segmentation algorithms, the atlas-based segmentation techniques can potentially produce reasonable segmentation results on neonatal brain images. However, their performance on the population-based atlas is still limited due to the high variability of brain structures across different individuals. Moreover, it may be impossible to generate a reasonable probabilistic atlas for neonates without tissue segmentation samples. To overcome these limitations, we present a neonatal brain tissue segmentation method by taking advantage of the longitudinal data available in our study to establish a subject-specific probabilistic atlas. In particular, tissue segmentation of the neonatal brain is formulated as two iterative steps of bias correction and probabilistic atlas based tissue segmentation, along with the guidance of brain tissue segmentation resulted from the later time images of the same subject which serve as a subject-specific probabilistic atlas. The proposed method has been evaluated qualitatively through visual inspection and quantitatively by comparing with manual delineation results. Experimental results show that the utilization of a subject-specific probabilistic atlas can substantially improve tissue segmentation of neonatal brain images.
KW - Neonate
KW - Probabilistic atlas
KW - Subject-specific atlas
KW - Tissue segmentation
UR - http://www.scopus.com/inward/record.url?scp=71649103501&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=71649103501&partnerID=8YFLogxK
U2 - 10.1117/12.811610
DO - 10.1117/12.811610
M3 - Conference contribution
AN - SCOPUS:71649103501
SN - 9780819475107
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Medical Imaging 2009 - Image Processing
T2 - Medical Imaging 2009 - Image Processing
Y2 - 8 February 2009 through 10 February 2009
ER -