Affiliation:
1. Instituto de Biofísica e Engenharia Biomédica, Faculdade de Ciências da Universidade de Lisboa, 1749-016 Lisbon, Portugal
2. Department of Neuroradiology, Karolinska University Hospital, SE-171 76 Stockholm, Sweden
3. Department of Women’s and Children’s Health, Uppsala University, SE-751 85 Uppsala, Sweden
Abstract
Magnetic resonance imaging (MRI) plays an important role in assessing early brain development and injury in neonates. When using an automated volumetric analysis, brain tissue segmentation is necessary, preceded by brain extraction (BE) to remove non-brain tissue. BE remains challenging in neonatal brain MRI, and despite the existence of several methods, manual segmentation is still considered the gold standard. Therefore, the purpose of this study was to assess different BE methods in the MRI of preterm neonates and their effects on the estimation of intracranial volumes (ICVs). This study included twenty-two premature neonates (mean gestational age ± standard deviation: 28.4 ± 2.1 weeks) with MRI brain scans acquired at term, without detectable lesions or congenital conditions. Manual segmentation was performed for T2-weighted scans to establish reference brain masks. Four automated BE methods were used: Brain Extraction Tool (BET2); Simple Watershed Scalping (SWS); HD Brain Extraction Tool (HD-BET); and SynthStrip. Regarding segmentation metrics, HD-BET outperformed the other methods with median improvements of +0.031 (BET2), +0.002 (SWS), and +0.011 (SynthStrip) points for the dice coefficient; and −0.786 (BET2), −0.055 (SWS), and −0.124 (SynthStrip) mm for the mean surface distance. Regarding ICVs, SWS and HD-BET provided acceptable levels of agreement with manual segmentation, with mean differences of −1.42% and 2.59%, respectively.
Funder
Fundação para a Ciência e a Tecnologia
Bolsa de Investigação para Doutoramento
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
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