Automated Segmentation of the Human Hippocampus Along Its Longitudinal Axis

Abstract

The human hippocampal formation is a crucial brain structure for memory and cognitive function that is closely related to other subcortical and cortical brain regions. Recent neuroimaging studies have revealed differences along the hippocampal longitudinal axis in terms of structure, connectivity, and function, stressing the importance of improving the reliability of the available segmentation methods that are typically used to divide the hippocampus into its anterior and posterior parts. However, current segmentation conventions present two main sources of variability related to manual operations intended to correct in-scanner head position across subjects and the selection of dividing planes along the longitudinal axis. Here, our aim was twofold: (1) to characterize inter- and intra-rater variability associated with these manual operations and compare manual (landmark based) and automatic (percentage based) hippocampal anterior–posterior segmentation procedures; and (2) to propose and test automated rotation methods based on approximating the hippocampal longitudinal axis to a straight line (estimated with principal component analysis, PCA) or a quadratic Bézier curve (fitted with numerical methods); as well as an automated anterior–posterior hippocampal segmentation procedure based on the percentage-based method. Our results reveal that automated rotation and segmentation procedures, used in combination or independently, minimize inconsistencies generated by the accumulation of manual operations while providing higher statistical power to detect well-known effects. A Matlab-based implementation of these procedures is made publicly available to the research community.