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

Khlif, Mohamed Salah; Egorova, Natalia; Werden, Emilio; Redolfi, Alberto; Boccardi, Marina; DeCarli, Charles S; Fletcher, Evan; Singh, Baljeet; Li, Qi; Bird, Laura; Brodtmann, Amy

doi:10.1016/j.nicl.2018.10.019

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Title		A comparison of automated segmentation and manual tracing in estimating hippocampal volume in ischemic stroke and healthy control participants
Authors		Khlif, Mohamed Salah Egorova, Natalia Werden, Emilio Redolfi, Alberto Boccardi, Marina DeCarli, Charles S Fletcher, Evan Singh, Baljeet Li, Qi Bird, Laura Brodtmann, Amy show all authors [1 - 11]
Published in		NeuroImage: Clinical. 2018, p. 101581
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.
Keywords		Hippocampus — Magnetic resonance Imaging — Automatic segmentation — Manual segmentation — Stroke
Identifiers		DOI: 10.1016/j.nicl.2018.10.019 PMID: 30606656
Full text		Article (Published version) (1.8 MB) - Free access
Structures		Faculté de médecine / Section de médecine clinique / Département de réadaptation et gériatrie
Research group		Troubles de mémoire et maladie d'Alzeimer (935)
Citation (ISO format)		KHLIF, Mohamed Salah et al. A comparison of automated segmentation and manual tracing in estimating hippocampal volume in ischemic stroke and healthy control participants. In: NeuroImage: Clinical, 2018, p. 101581. doi: 10.1016/j.nicl.2018.10.019 https://archive-ouverte.unige.ch/unige:112973