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.
This paper proposes a brain image registration algorithm, called RABBIT, which achieves fast and accurate image
registration by using an intermediate template generated by a statistical shape deformation model during the image
registration procedure. The statistical brain shape deformation information is learned by means of principal component
analysis (PCA) from a set of training brain deformations, each of them linking a selected template to an individual brain
sample. Using the statistical deformation information, the template image can be registered to a new individual image by
optimizing a statistical deformation model with a small number of parameters, thus generating an intermediate template
very close to the individual brain image. The remaining shape difference between the intermediate template and the
individual brain is then minimized by a general registration algorithm, such as HAMMER. With the help of the
intermediate template, the registration between the template and individual brain images can be achieved fast and with
similar registration accuracy as HAMMER. The effectiveness of the RABBIT has been evaluated by using both
simulated atrophy data and real brain images. The experimental results show that RABBIT can achieve over five times
speedup, compared to HAMMER, without losing any registration accuracy or statistical power in detecting brain
atrophy.
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