title: Automatic Tissue Segmentation of Neonate Brain MR Images with Subject-specific Atlases.
abstract: Automatic tissue segmentation of the neonate brain using Magnetic Resonance Images (MRI) is extremely important to study brain development and perform early diagnostics but is challenging due to high variability and inhomogeneity in contrast throughout the image due to incomplete myelination of the white matter tracts. For these reasons, current methods often totally fail or give unsatisfying results. Furthermore, most of the subcortical midbrain structures are misclassified due to a lack of contrast in these regions. We have developed a novel method that creates a probabilistic subject-specific atlas based on a population atlas currently containing a number of manually segmented cases. The generated subject-specific atlas is sharp and adapted to the subject that is being processed. We then segment brain tissue classes using the newly created atlas with a single-atlas expectation maximization based method. Our proposed method leads to a much lower failure rate in our experiments. The overall segmentation results are considerably improved when compared to using a non-subject-specific, population average atlas. Additionally, we have incorporated diffusion information obtained from Diffusion Tensor Images (DTI) to improve the detection of white matter that is not visible at this early age in structural MRI (sMRI) due to a lack of myelination. Although this necessitates the acquisition of an additional sequence, the diffusion information improves the white matter segmentation throughout the brain, especially for the mid-brain structures such as the corpus callosum and the internal capsule.
authors.affiliation: Department of Psychiatry, University of North Carolina, Chapel Hill, NC 27599, USA.
authors.forename: Marie
authors.initials: M
authors.lastname: Cherel
authors.affiliation: Department of Psychiatry, University of North Carolina, Chapel Hill, NC 27599, USA.
authors.forename: Francois
authors.initials: F
authors.lastname: Budin
authors.affiliation: GE Global Research, Albany, NY 12309 USA.
authors.forename: Marcel
authors.initials: M
authors.lastname: Prastawa
authors.affiliation: School of Computing, University of Utah, Salt Lake City, UT 84112 USA ; Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT 84112 USA.
authors.forename: Guido
authors.initials: G
authors.lastname: Gerig
authors.affiliation: Department of Psychiatry, University of North Carolina, Chapel Hill, NC 27599, USA.
authors.forename: Kevin
authors.initials: K
authors.lastname: Lee
authors.affiliation: Charité University Medicine Berlin, 10117 Berlin, Germany ; Development, Health and Disease Research Program, University of California, Irvine, CA 92697, USA.
authors.forename: Claudia
authors.initials: C
authors.lastname: Buss
authors.affiliation: Psychiatry Neuroimaging Laboratory, Harvard, Boston, MA 02215 USA.
authors.forename: Amanda
authors.initials: A
authors.lastname: Lyall
authors.affiliation: Department of Psychiatry, University of North Carolina, Chapel Hill, NC 27599, USA.
authors.forename: Kirsten Zaldarriaga
authors.initials: KZ
authors.lastname: Consing
authors.affiliation: Department of Psychiatry, University of North Carolina, Chapel Hill, NC 27599, USA ; Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599 USA.
authors.forename: Martin
authors.initials: M
authors.lastname: Styner
keywords: MRI; atlas; automatic; neonate; population; segmentation; subject-specific; tissue