Calculating hippocampal volume from Magnetic Resonance (MR) images is an essential task in many studies of neurocognition in healthy and diseased populations. The `gold standard' method involves hand tracing, which is accurate but laborious, requiring expertly trained researchers and significant amounts of time. As such, segmenting large datasets with the standard method is impractical. Current automated pipelines are inaccurate at hippocampal demarcation and volumetry. We developed a semi-automated hippocampal segmentation pipeline based on the Advanced Normalization Tools (ANTs) suite of programs to segment the hippocampus. We applied the semi-automated segmentation pipeline to 70 participant scans (26 female) from groups that included participants diagnosed with autism spectrum disorder, healthy older adults (mean age 74) and healthy younger controls. We found that hippocampal segmentations obtained with the semi-automated pipeline more closely matched the segmentations of an expert rater than those obtained using FreeSurfer or the segmentations of novice raters. Further, we found that the pipeline performed best when including manually- placed landmarks and when using a template generated from a heterogeneous sample (that included the full variability of group assignments) than a template generated from more homogeneous samples (using only individuals within a given age or with a specific neuropsychiatric diagnosis). Additionally, the semi-automated pipeline required much less time (5 minutes per brain) than manual segmentation (30-60 minutes per brain) or FreeSurfer (8 hours per brain).



College and Department

Life Sciences; Physiology and Developmental Biology



Date Submitted


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hippocampus, segmentation, algorithm, autism, advanced normalization tools (ANTs)

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Physiology Commons