Literature DB >> 24521851

Hippocampal volume change measurement: quantitative assessment of the reproducibility of expert manual outlining and the automated methods FreeSurfer and FIRST.

Emma R Mulder1, Remko A de Jong1, Dirk L Knol2, Ronald A van Schijndel3, Keith S Cover4, Pieter J Visser5, Frederik Barkhof1, Hugo Vrenken6.   

Abstract

BACKGROUND: To measure hippocampal volume change in Alzheimer's disease (AD) or mild cognitive impairment (MCI), expert manual delineation is often used because of its supposed accuracy. It has been suggested that expert outlining yields poorer reproducibility as compared to automated methods, but this has not been investigated. AIM: To determine the reproducibilities of expert manual outlining and two common automated methods for measuring hippocampal atrophy rates in healthy aging, MCI and AD.
METHODS: From the Alzheimer's Disease Neuroimaging Initiative (ADNI), 80 subjects were selected: 20 patients with AD, 40 patients with mild cognitive impairment (MCI) and 20 healthy controls (HCs). Left and right hippocampal volume change between baseline and month-12 visit was assessed by using expert manual delineation, and by the automated software packages FreeSurfer (longitudinal processing stream) and FIRST. To assess reproducibility of the measured hippocampal volume change, both back-to-back (BTB) MPRAGE scans available for each visit were analyzed. Hippocampal volume change was expressed in μL, and as a percentage of baseline volume. Reproducibility of the 1-year hippocampal volume change was estimated from the BTB measurements by using linear mixed model to calculate the limits of agreement (LoA) of each method, reflecting its measurement uncertainty. Using the delta method, approximate p-values were calculated for the pairwise comparisons between methods. Statistical analyses were performed both with inclusion and exclusion of visibly incorrect segmentations.
RESULTS: Visibly incorrect automated segmentation in either one or both scans of a longitudinal scan pair occurred in 7.5% of the hippocampi for FreeSurfer and in 6.9% of the hippocampi for FIRST. After excluding these failed cases, reproducibility analysis for 1-year percentage volume change yielded LoA of ±7.2% for FreeSurfer, ±9.7% for expert manual delineation, and ±10.0% for FIRST. Methods ranked the same for reproducibility of 1-year μL volume change, with LoA of ±218 μL for FreeSurfer, ±319 μL for expert manual delineation, and ±333 μL for FIRST. Approximate p-values indicated that reproducibility was better for FreeSurfer than for manual or FIRST, and that manual and FIRST did not differ. Inclusion of failed automated segmentations led to worsening of reproducibility of both automated methods for 1-year raw and percentage volume change.
CONCLUSION: Quantitative reproducibility values of 1-year microliter and percentage hippocampal volume change were roughly similar between expert manual outlining, FIRST and FreeSurfer, but FreeSurfer reproducibility was statistically significantly superior to both manual outlining and FIRST after exclusion of failed segmentations.
Copyright © 2014 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Alzheimer's disease; Automatic segmentation; Hippocampus; Magnetic resonance imaging; Manual segmentation; Mild cognitive impairment

Mesh:

Year:  2014        PMID: 24521851     DOI: 10.1016/j.neuroimage.2014.01.058

Source DB:  PubMed          Journal:  Neuroimage        ISSN: 1053-8119            Impact factor:   6.556


  54 in total

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Authors:  Megan A Miller; Regina L Leckie; Shannon D Donofry; Peter J Gianaros; Kirk I Erickson; Stephen B Manuck; Kathryn A Roecklein
Journal:  Hippocampus       Date:  2015-01-20       Impact factor: 3.899

2.  Combining multiple anatomical MRI measures improves Alzheimer's disease classification.

Authors:  Frank de Vos; Tijn M Schouten; Anne Hafkemeijer; Elise G P Dopper; John C van Swieten; Mark de Rooij; Jeroen van der Grond; Serge A R B Rombouts
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Review 3.  Automated methods for hippocampus segmentation: the evolution and a review of the state of the art.

Authors:  Vanderson Dill; Alexandre Rosa Franco; Márcio Sarroglia Pinho
Journal:  Neuroinformatics       Date:  2015-04

4.  Visual Explanations From Deep 3D Convolutional Neural Networks for Alzheimer's Disease Classification.

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5.  Automatic Thalamus Segmentation on Unenhanced 3D T1 Weighted Images: Comparison of Publicly Available Segmentation Methods in a Pediatric Population.

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Journal:  Neuroinformatics       Date:  2019-07

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Journal:  J Cereb Blood Flow Metab       Date:  2015-06-03       Impact factor: 6.200

Review 7.  Recent publications from the Alzheimer's Disease Neuroimaging Initiative: Reviewing progress toward improved AD clinical trials.

Authors:  Michael W Weiner; Dallas P Veitch; Paul S Aisen; Laurel A Beckett; Nigel J Cairns; Robert C Green; Danielle Harvey; Clifford R Jack; William Jagust; John C Morris; Ronald C Petersen; Andrew J Saykin; Leslie M Shaw; Arthur W Toga; John Q Trojanowski
Journal:  Alzheimers Dement       Date:  2017-03-22       Impact factor: 21.566

8.  Brain atrophy and white-matter hyperintensities are not significantly associated with incidence and severity of postoperative delirium in older persons without dementia.

Authors:  Michele Cavallari; Tammy T Hshieh; Charles R G Guttmann; Long H Ngo; Dominik S Meier; Eva M Schmitt; Edward R Marcantonio; Richard N Jones; Cyrus M Kosar; Tamara G Fong; Daniel Press; Sharon K Inouye; David C Alsop
Journal:  Neurobiol Aging       Date:  2015-02-28       Impact factor: 4.673

9.  BDNF genotype is associated with hippocampal volume in mild traumatic brain injury.

Authors:  J P Hayes; A Reagan; M W Logue; S M Hayes; N Sadeh; D R Miller; M Verfaellie; E J Wolf; R E McGlinchey; W P Milberg; A Stone; S A Schichman; M W Miller
Journal:  Genes Brain Behav       Date:  2017-08-30       Impact factor: 3.449

10.  Impact of lifestyle dimensions on brain pathology and cognition.

Authors:  Stefanie Schreiber; Jacob Vogel; Henry D Schwimmer; Shawn M Marks; Frank Schreiber; William Jagust
Journal:  Neurobiol Aging       Date:  2016-01-30       Impact factor: 4.673

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