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Quantitative assessment of myelination patterns in preterm neonates using T2-weighted MRI.
Sci. Rep. 9:12938 (2019)
Myelination is considered to be an important developmental process during human brain maturation and closely correlated with gestational age. Quantitative assessment of the myelination status requires dedicated imaging, but the conventional T2-weighted scans routinely acquired during clinical imaging of neonates carry signatures that are thought to be associated with myelination. In this work, we develop a quatitative marker of progressing myelination for assessment preterm neonatal brain maturation based on novel automatic segmentation method for myelin-like signals on T2-weighted magnetic resonance images. Firstly we define a segmentation protocol for myelin-like signals. We then develop an expectation-maximization framework to obtain the automatic segmentations of myelin-like signals with explicit class for partial volume voxels whose locations are configured in relation to the composing pure tissues via second-order Markov random fields. The proposed segmentation achieves high Dice overlaps of 0.83 with manual annotations. The automatic segmentations are then used to track volumes of myelinated tissues in the regions of the central brain structures and brainstem. Finally, we construct a spatio-temporal growth models for myelin-like signals, which allows us to predict gestational age at scan in preterm infants with root mean squared error 1.41 weeks.
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Publication type
Article: Journal article
Document type
Scientific Article
Language
english
Publication Year
2019
HGF-reported in Year
2019
ISSN (print) / ISBN
2045-2322
e-ISSN
2045-2322
Journal
Scientific Reports
Quellenangaben
Volume: 9,
Issue: 1,
Article Number: 12938
Publisher
Nature Publishing Group
Publishing Place
London
Reviewing status
Peer reviewed
Institute(s)
Institute for Machine Learning in Biomed Imaging (IML)
POF-Topic(s)
30205 - Bioengineering and Digital Health
Research field(s)
Enabling and Novel Technologies
PSP Element(s)
G-507100-001
Scopus ID
85072012167
Erfassungsdatum
2022-09-07