Automated Volumetry and Regional Thickness Analysis of Hippocampal Subfields and Medial Temporal Cortical Structures in Mild Cognitive Impairment

By Paul Yushkevich, John Pluta, Hongzhi Wang, L. Xie, S. Ding, E. Gertje, L. Mancuso, Daria Kliot, Sandhitsu R. Das, D. Wolk. Human Brain Mapping, 2014

597 citations recorded by the source corpus.

We evaluate a fully automatic technique for labeling hippocampal subfields and cortical subregions in the medial temporal lobe in in vivo 3 Tesla MRI. The method performs segmentation on a T2‐weighted MRI scan with 0.4 × 0.4 × 2.0 mm3 resolution, partial brain coverage, and oblique orientation. Hippocampal subfields, entorhinal cortex, and perirhinal cortex are labeled using a pipeline that combines multi‐atlas label fusion and learning‐based error correction. In contrast to earlier work on automatic subfield segmentation in T2‐weighted MRI [Yushkevich et al., 2010], our approach requires no manual initialization, labels hippocampal subfields over a greater anterior‐posterior extent, and labels the perirhinal cortex, which is further subdivided into Brodmann areas 35 and 36. The accuracy of the automatic segmentation relative to manual segmentation is measured using cross‐validation in 29 subjects from a study of amnestic mild cognitive impairment (aMCI) and is highest for the dentate gyrus (Dice coefficient is 0.823), CA1 (0.803), perirhinal cortex (0.797), and entorhinal cortex (0.786) labels. A larger cohort of 83 subjects is used to examine the effects of aMCI in the hippocampal region using both subfield volume and regional subfield thickness maps. Most significant differences between aMCI and healthy aging are observed bilaterally in the CA1 subfield and in the left Brodmann area 35. Thickness analysis results are consistent with volumetry, but provide additional regional specificity and suggest nonuniformity in the effects of aMCI on hippocampal subfields and MTL cortical subregions. Hum Brain Mapp, 36:258–287, 2015. © 2014 Wiley Periodicals, Inc.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/c2d655354d43650620dcc5a278ded0c599db27fa

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Citations in the corpus

Age Differences in Hippocampal Subfield Volumes from Childhood to Late Adulthood (2015, background): “…al., 2007; Mueller and Weiner, 2009; Malykhin et al., 2010; Shing et al., 2011; Bender et al., 2013; Raz et al., 2014) and computerized (La Joie et al., 2010; Kerchner et al., 2014; Wisse et al., 2014; Yushkevich et al., 2014) segmentation have allowed studying human hippocampal subfields in vivo.”
Closed-loop stimulation of temporal cortex rescues functional networks and improves memory (2018, methodology): “Whole brain and high resolution medial temporal lobe volumetric segmentation was also performed using the T1-weighted scan and a dedicated hippocampal coronal T2-weighted scan with Advanced Normalization Tools (ANTS) [42] and Automatic Segmentation of Hippocampal Subfields (ASHS) multi-atlas segment”
A computational atlas of the hippocampal formation using ex vivo, ultra-high resolution MRI: Application to adaptive segmentation of in vivo MRI (2015, unknown): “The user needed to provide an initial partitioning of MRI slices into hippocampal head, body and tail (this requirement was dropped in their recent study, Yushkevich et al., 2015).”
Quantitative Comparison of 21 Protocols for Labeling Hippocampal Subfields and Parahippocampal Subregions in In Vivo MRI: Towards a Harmonized Segmentation Protocol (2015, methodology): “Likewise, since this effort includes all of the groupswho have developed automated tools for subfield segmentation (Van Leemput et al., 2009; Yushkevich et al., 2015; Pipitone et al., 2014), the harmonized protocol will be incorporated into these tools, particularly those made available to the large”