Automated and manual segmentation of the hippocampus in human infants

The hippocampus, critical for learning and memory, undergoes substantial
changes early in life. Investigating the developmental trajectory of
hippocampal structure and function requires an accurate method for
segmenting this region from anatomical MRI scans. Although manual
segmentation is regarded as the “gold standard” approach, it is laborious
and subjective. This has fueled the pursuit of automated segmentation
methods in adults. However, little is known about the reliability of these
automated protocols in infants, particularly when anatomical scan quality
is degraded by head motion or the use of shorter and quieter
infant-friendly sequences. During a task-based fMRI protocol, we collected
quiet T1-weighted anatomical scans from 42 sessions with awake infants
aged 4–23 months. Two expert tracers first segmented the hippocampus in
both hemispheres manually. The resulting inter-rater reliability (IRR) was
only moderate, reflecting the difficulty of infant segmentation. We then
used four protocols to predict these manual segmentations: average adult
template, average infanBt template, FreeSurfer software, and Automated
Segmentation of Hippocampal Subfields (ASHS) software. ASHS generated the
most reliable hippocampal segmentations in infants, exceeding the manual
IRR of experts. Automated methods thus provide robust hippocampal
segmentations of noisy T1-weighted infant scans, opening new possibilities
for interrogating early hippocampal development.