The gross anatomy of the infant brain at term is fairly similar to that
of the adult brain, but structures are immature, and the brain undergoes
rapid growth during the first 2 years of life. Neonate magnetic
resonance (MR) images have different contrasts compared to adult images,
and automated segmentation of brain magnetic resonance imaging (MRI)
can thus be considered challenging as less software options are
available. Despite this, most anatomical regions are identifiable and
thus amenable to manual segmentation. In the current study, we developed
a protocol for segmenting the amygdala and hippocampus in T2-weighted
neonatal MR images. The participants were 31 healthy infants between 2
and 5 weeks of age. Intra-rater reliability was measured in 12 randomly
selected MR images, where 6 MR images were segmented at 1-month
intervals between the delineations, and another 6 MR images at 6-month
intervals. The protocol was also tested by two independent raters in 20
randomly selected T2-weighted images, and finally with T1 images.
Intraclass correlation coefficient (ICC) and Dice similarity coefficient
(DSC) for intra-rater, inter-rater, and T1 vs. T2 comparisons were
computed. Moreover, manual segmentations were compared to automated
segmentations performed by iBEAT toolbox in 10 T2-weighted MR images.
The intra-rater reliability was high ICC ≥ 0.91, DSC ≥ 0.89, the
inter-rater reliabilities were satisfactory ICC ≥ 0.90, DSC ≥ 0.75 for
hippocampus and DSC ≥ 0.52 for amygdalae. Segmentations for T1 vs.
T2-weighted images showed high consistency ICC ≥ 0.90, DSC ≥ 0.74. The
manual and iBEAT segmentations showed no agreement, DSC ≥ 0.39. In
conclusion, there is a clear need to improve and develop the procedures
for automated segmentation of infant brain MR images.