ArticleNeuroImage. Clinical2019
A comparison of automated segmentation and manual tracing in estimating hippocampal volume in ischemic stroke and healthy control participants.
Article in NeuroImage. Clinical, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 38 citations in OpenAlex.
- Seven tesla MRI reveals amygdala and hippocampal subfield atrophy in dementia with Lewy bodies.Alzheimer's research & therapy · 2026Article
- Post mortem cadaveric and imaging mapping analysis of the influence of cochlear implants on cMRI assessment regarding implant positioning and artifact formation.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2025Article
- Alterations of contralesional hippocampal subfield volumes and relations to cognitive functions in patients with unilateral stroke.Brain and behavior · 2024Article
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- Automated hippocampal segmentation algorithms evaluated in stroke patients.Scientific reports · 2023Article
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- FreeSurfer-based segmentation of hippocampal subfields: A review of methods and applications, with a novel quality control procedure for ENIGMA studies and other collaborative efforts.Human brain mapping · 2022Review
- The ENIGMA Stroke Recovery Working Group: Big data neuroimaging to study brain-behavior relationships after stroke.Human brain mapping · 2022Review
- Testing a convolutional neural network-based hippocampal segmentation method in a stroke population.Human brain mapping · 2022Article
- Grey and white matter atrophy 1 year after stroke aphasia.Brain communications · 2022Article
- Norms for Automatic Estimation of Hippocampal Atrophy and a Step Forward for Applicability to the Italian Population.Frontiers in neuroscience · 2021Article
- Hippocampal subfield volumes are associated with verbal memory after first-ever ischemic stroke.Alzheimer's & dementia (Amsterdam, Netherlands) · 2021Article
- Dose-dependent volume loss in subcortical deep grey matter structures after cranial radiotherapy.Clinical and translational radiation oncology · 2021Article
- Borderline Personality Traits Are Not Correlated With Brain Structure in Two Large Samples.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2020Article
- Machine Learning for Diagnosis of AD and Prediction of MCI Progression From Brain MRI Using Brain Anatomical Analysis Using Diffeomorphic Deformation.Frontiers in neurology · 2020Article
- Cortical Thickness Estimation in Individuals With Cerebral Small Vessel Disease, Focal Atrophy, and Chronic Stroke Lesions.Frontiers in neuroscience · 2020Article
- Longer Duration of Hypertension and MRI Microvascular Brain Alterations Are Associated with Lower Hippocampal Volumes in Older Individuals with Hypertension.Journal of Alzheimer's disease : JAD · 2020Article
- Assessment of longitudinal hippocampal atrophy in the first year after ischemic stroke using automatic segmentation techniques.NeuroImage. Clinical · 2019Article
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Authors and funding
11 authors at 4 institutions in 4 countries.
Funding
Abstract
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.
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