Automated and manual segmentation of the hippocampus in human infants

Abstract

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 protocols in human infants, particularly when anatomical scan quality is low from increased head motion or shorter sequences that minimize head motion. During a task-based fMRI protocol, we collected quiet T1-weighted anatomical scans from 42 sessions with awake infants aged 4-23 months. We first had two expert tracers manually segment the hippocampus bilaterally and assess inter-rater reliability. We then attempted to predict these manual segmentations using four protocols: average adult template, average infant template, FreeSurfer software, and Automated Segmentation of Hippocampal Subfields (ASHS) software. ASHS generated the most reliable hippocampal segmentations in infants, exceeding manual inter-rater reliability of the experts. Automated methods can thus provide robust hippocampal segmentations of noisy T1-weighted infant scans, opening new possibilities for interrogating early hippocampal development. HighlightsO_LIInter-rater reliability of manual segmentation of infant hippocampus is moderate. C_LIO_LITemplate-based methods and FreeSurfer provide reasonably accurate segmentations. C_LIO_LIASHS produces highly accurate segmentations, exceeding manual inter-rater reliability. C_LI