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Automatic Tissue Segmentation of Neonate Brain MR Images with Subject-specific Atlases

Institution:
Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA.
Publication Date:
Feb-2015
Journal:
Proc SPIE Int Soc Opt Eng
Volume Number:
9413
Citation:
Proc SPIE Int Soc Opt Eng. 2015 Feb 21;9413.
PubMed ID:
26089584
PMCID:
PMC446919
Keywords:
segmentation, population, neonate, MRI, automatic, atlas, tissue, subject-specific
Appears in Collections:
NA-MIC
Sponsors:
U54 EB005149/EB/NIBIB NIH HHS/United States
P50 MH100029/MH/NIMH NIH HHS/United States
R01 MH091645/MH/NIMH NIH HHS/United States
R01 MH091351/MH/NIMH NIH HHS/United States
R01 HD055741/HD/NICHD NIH HHS/United States
R01 HD059854/HD/NICHD NIH HHS/United States
R01 MH070890/MH/NIMH NIH HHS/United States
Generated Citation:
Cherel M., Budin F., Prastawa M., Gerig G., Lee K., Buss C., Lyall A., Consing K.Z., Styner M. Automatic Tissue Segmentation of Neonate Brain MR Images with Subject-specific Atlases. Proc SPIE Int Soc Opt Eng. 2015 Feb 21;9413. PMID: 26089584. PMCID: PMC446919.
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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.

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Cherel-SPIEIntSocOptEng2015-fig3.jpg (31.562kB)