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Proceedings Paper

Automatic tissue segmentation of neonate brain MR Images with subject-specific atlases
Author(s): Marie Cherel; Francois Budin; Marcel Prastawa; Guido Gerig; Kevin Lee; Claudia Buss; Amanda Lyall; Kirsten Zaldarriaga Consing; Martin Styner
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Paper 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.

Paper Details

Date Published: 20 March 2015
PDF: 11 pages
Proc. SPIE 9413, Medical Imaging 2015: Image Processing, 941311 (20 March 2015); doi: 10.1117/12.2082209
Show Author Affiliations
Marie Cherel, The Univ. of North Carolina at Chapel Hill (United States)
Francois Budin, The Univ. of North Carolina at Chapel Hill (United States)
Marcel Prastawa, GE Global Research (United States)
Guido Gerig, Univ. of Utah (United States)
Kevin Lee, The Univ. of North Carolina at Chapel Hill (United States)
Claudia Buss, Charité Univ. Medicine Berlin (Germany)
Univ. of California, Irvine (United States)
Amanda Lyall, Psychiatry Neuroimagining Lab. (United States)
Kirsten Zaldarriaga Consing, The Univ. of North Carolina at Chapel Hill (United States)
Martin Styner, The Univ. of North Carolina at Chapel Hill (United States)

Published in SPIE Proceedings Vol. 9413:
Medical Imaging 2015: Image Processing
Sébastien Ourselin; Martin A. Styner, Editor(s)

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