Model-Based Segmentation of Hippocampal Subfields in Ultra-High Resolution In Vivo MRI

Abstract

Abstract. Recent developments in MR data acquisition technology are starting to yield images that show anatomical features of the hippocampal formation at an unprecedented level of detail, providing the basis for hippocampal subfield measurement. Because of the role of the hippocampus in human memory and its implication in a variety of disorders and conditions, the ability to reliably and efficiently quantify its subfields through in vivo neuroimaging is of great interest to both basic neuroscience and clinical research. In this paper, we propose a fully-automated method for segmenting the hippocampal subfields in ultra-high resolution MRI data. Using a Bayesian approach, we build a computational model of how images around the hippocampal area are generated, and use this model to obtain automated segmentations. We validate the proposed technique by comparing our segmentation results with corresponding manual delineations in ultra-high resolution MRI scans of five individuals.

Regional Hippocampal Volumes and Development Predict Learning and Memory

“…Next, we performed hippocampal subfield segmentation using a new automated technique within the FreeSurfer suite [29,40]. The procedure uses Bayesian inference and a probabilistic atlas of the hippocampal formation based on manual delineations of subfields in ultra-high-resolution MRI scans [29].…”

A Critical Appraisal of the Hippocampal Subfield Segmentation Package in FreeSurfer

“…Several manual segmentation protocols have been developed for 3–7 T MRI (Mueller et al, 2007; Van Leemput et al, 2008; La Joie et al, 2010; Wisse et al, 2012), some of which are automated (Van Leemput et al, 2008; Yushkevich et al, 2009). One of these automated protocols (Van Leemput et al, 2008, 2009) has recently been implemented in FreeSurfer (Fischl, 2012), a freely available easy-to-use set of automated brain MRI analysis tools. This has made hippocampal subfield segmentation available to everyone with 1.5–3 T MRI data and the method is being used in an increasing number of studies (Teicher et al, 2012; Li et al, 2013; Pereira et al, 2014).…”

P2‐231: Systematic Comparison of Different Techniques to Measure Hippocampal Subfield Volumes in ADNI2

“…Output: label image and text file with subfield volumes. Parcellation scheme atlas: Identifies labels for fimbria, CA2-3, CA1, CA4 and dentate, presubiculum, subiculum, hippocampal fissure, hippocampus (tail) inferior lateral ventricle and choroid plexus based on information from an probabilistic atlas generated from the manual segmentations of the right hippocampus in 10 (6 young, 4 older) cognitively intact subjects (Van Leemput et al, 2009).…”

“…Of the currently available T1-based approaches, the Bayesian inference labeling as implemented in Freesurfer 5.1. (Van Leemput et al, 2009) and shape analysis based on Large Deformation Diffeomorphic Metric Mapping (Khan et al, 2008) were selected for this project. The former because the algorithm is publicly available and is frequently used, and the second because it was one of the earliest approaches for subfield volumetry that has been continuously refined and optimized for 3 T images.…”