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Automated volumetry and regional thickness analysis of hippocampal subfields and medial temporal cortical structures in mild cognitive impairment

Yushkevich, Paul A ; Pluta, John B ; Wang, Hongzhi ; Xie, Long ; Ding, Song-Lin ; Gertje, Eske C LU orcid ; Mancuso, Lauren ; Kliot, Daria ; Das, Sandhitsu R and Wolk, David A (2015) In Human Brain Mapping 36(1). p.87-258
Abstract

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 mm(3) 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... (More)

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 mm(3) 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.

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author
; ; ; ; ; ; ; ; and
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Algorithms, Brain Mapping, Cognitive Dysfunction/pathology, Electronic Data Processing, Female, Hippocampus/pathology, Humans, Image Processing, Computer-Assisted, Learning/physiology, Magnetic Resonance Imaging, Male, Temporal Lobe/pathology
in
Human Brain Mapping
volume
36
issue
1
pages
87 - 258
publisher
Wiley-Liss Inc.
external identifiers
  • scopus:84916925772
  • pmid:25181316
ISSN
1065-9471
DOI
10.1002/hbm.22627
language
English
LU publication?
no
additional info
© 2014 Wiley Periodicals, Inc.
id
c051d9f2-07e1-42de-b249-0f02d7b1f853
date added to LUP
2025-03-05 16:36:26
date last changed
2025-08-21 18:45:52
@article{c051d9f2-07e1-42de-b249-0f02d7b1f853,
  abstract     = {{<p>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 mm(3) 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.</p>}},
  author       = {{Yushkevich, Paul A and Pluta, John B and Wang, Hongzhi and Xie, Long and Ding, Song-Lin and Gertje, Eske C and Mancuso, Lauren and Kliot, Daria and Das, Sandhitsu R and Wolk, David A}},
  issn         = {{1065-9471}},
  keywords     = {{Algorithms; Brain Mapping; Cognitive Dysfunction/pathology; Electronic Data Processing; Female; Hippocampus/pathology; Humans; Image Processing, Computer-Assisted; Learning/physiology; Magnetic Resonance Imaging; Male; Temporal Lobe/pathology}},
  language     = {{eng}},
  number       = {{1}},
  pages        = {{87--258}},
  publisher    = {{Wiley-Liss Inc.}},
  series       = {{Human Brain Mapping}},
  title        = {{Automated volumetry and regional thickness analysis of hippocampal subfields and medial temporal cortical structures in mild cognitive impairment}},
  url          = {{http://dx.doi.org/10.1002/hbm.22627}},
  doi          = {{10.1002/hbm.22627}},
  volume       = {{36}},
  year         = {{2015}},
}