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A comparison of automated segmentation and manual tracing in estimating hippocampal volume in ischemic stroke and healthy control participants.
Journal article

A comparison of automated segmentation and manual tracing in estimating hippocampal volume in ischemic stroke and healthy control participants.

  • Khlif MS The Florey Institute for Neuroscience and Mental Health, University of Melbourne, Melbourne, Australia. Electronic address: mohamed.khlif@florey.edu.au.
  • Egorova N The Florey Institute for Neuroscience and Mental Health, University of Melbourne, Melbourne, Australia.
  • Werden E The Florey Institute for Neuroscience and Mental Health, University of Melbourne, Melbourne, Australia.
  • Redolfi A Laboratory of Alzheimer Neuroimaging and Epidemiology, IRCCS S.Giovanni di Dio Fatebenefratelli, Brescia, Italy.
  • Boccardi M Laboratory of Neuroimaging of Aging, University of Geneva, Geneva, Switzerland.
  • DeCarli CS IDeA Laboratory, Department of Neurology, UC Davis School of Medicine, Davis, CA, USA.
  • Fletcher E IDeA Laboratory, Department of Neurology, UC Davis School of Medicine, Davis, CA, USA.
  • Singh B IDeA Laboratory, Department of Neurology, UC Davis School of Medicine, Davis, CA, USA.
  • Li Q The Florey Institute for Neuroscience and Mental Health, University of Melbourne, Melbourne, Australia.
  • Bird L The Florey Institute for Neuroscience and Mental Health, University of Melbourne, Melbourne, Australia.
  • Brodtmann A The Florey Institute for Neuroscience and Mental Health, University of Melbourne, Melbourne, Australia.
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  • 2019-01-05
Published in:
  • NeuroImage. Clinical. - 2019
Automatic segmentation
Hippocampus
Magnetic resonance imaging
Manual segmentation
Stroke
Algorithms
Alzheimer Disease
Brain
Brain Ischemia
Female
Hippocampus
Humans
Image Processing, Computer-Assisted
Male
Organ Size
Pattern Recognition, Automated
Stroke
English Manual quantification of the hippocampal atrophy state and rate is time consuming and prone to poor reproducibility, even when performed by neuroanatomical experts. The automation of hippocampal segmentation has been investigated in normal aging, epilepsy, and in Alzheimer's disease. Our first goal was to compare manual and automated hippocampal segmentation in ischemic stroke and to, secondly, study the impact of stroke lesion presence on hippocampal volume estimation. We used eight automated methods to segment T1-weighted MR images from 105 ischemic stroke patients and 39 age-matched controls sampled from the Cognition And Neocortical Volume After Stroke (CANVAS) study. The methods were: AdaBoost, Atlas-based Hippocampal Segmentation (ABHS) from the IDeALab, Computational Anatomy Toolbox (CAT) using 3 atlas variants (Hammers, LPBA40 and Neuromorphometics), FIRST, FreeSurfer v5.3, and FreeSurfer v6.0-Subfields. A number of these methods were employed to re-segment the T1 images for the stroke group after the stroke lesions were masked (i.e., removed). The automated methods were assessed on eight measures: process yield (i.e. segmentation success rate), correlation (Pearson's R and Shrout's ICC), concordance (Lin's RC and Kandall's W), slope 'a' of best-fit line from correlation plots, percentage of outliers from Bland-Altman plots, and significance of control-stroke difference. We eliminated the redundant measures after analysing between-measure correlations using Spearman's rank correlation. We ranked the automated methods based on the sum of the remaining non-redundant measures where each measure ranged between 0 and 1. Subfields attained an overall score of 96.3%, followed by AdaBoost (95.0%) and FIRST (94.7%). CAT using the LPBA40 atlas inflated hippocampal volumes the most, while the Hammers atlas returned the smallest volumes overall. FIRST (p = 0.014), FreeSurfer v5.3 (p = 0.007), manual tracing (p = 0.049), and CAT using the Neuromorphometics atlas (p = 0.017) all showed a significantly reduced hippocampal volume mean for the stroke group compared to control at three months. Moreover, masking of the stroke lesions prior to segmentation resulted in hippocampal volumes which agreed less with manual tracing. These findings recommend an automated segmentation without lesion masking as a more reliable procedure for the estimation of hippocampal volume in ischemic stroke.
Language
  • English
Open access status
gold
Identifiers
Persistent URL
https://sonar.ch/global/documents/103487
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