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Comparing manual and automatic segmentation of hippocampal volumes: reliability and validity issues in younger and older brains.

ABSTRACT: We compared hippocampal volume measures obtained by manual tracing to automatic segmentation with FreeSurfer in 44 younger (20-30 years) and 47 older (60-70 years) adults, each measured with magnetic resonance imaging (MRI) over three successive time points, separated by four months. Retest correlations over time were very high for both manual and FreeSurfer segmentations. With FreeSurfer, correlations over time were significantly lower in the older than in the younger age group, which was not the case with manual segmentation. Pearson correlations between manual and FreeSurfer estimates were sufficiently high, numerically even higher in the younger group, whereas intra-class correlation coefficient (ICC) estimates were lower in the younger than in the older group. FreeSurfer yielded higher volume estimates than manual segmentation, particularly in the younger age group. Importantly, FreeSurfer consistently overestimated hippocampal volumes independently of manually assessed volume in the younger age group, but overestimated larger volumes in the older age group to a less extent, introducing a systematic age bias in the data. Age differences in hippocampal volumes were significant with FreeSurfer, but not with manual tracing. Manual tracing resulted in a significant difference between left and right hippocampus (right > left), whereas this asymmetry effect was considerably smaller with FreeSurfer estimates. We conclude that FreeSurfer constitutes a feasible method to assess differences in hippocampal volume in young adults. FreeSurfer estimates in older age groups should, however, be interpreted with care until the automatic segmentation pipeline has been further optimized to increase validity and reliability in this age group.

SUBMITTER: Wenger E

PROVIDER: S-EPMC6869097 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:PurposePrecise and reproducible hippocampus outlining is important to quantify hippocampal atrophy caused by neurodegenerative diseases and to spare the hippocampus in whole brain radiation therapy when performing prophylactic cranial irradiation or treating brain metastases. This study aimed to quantify systematic differences between methods by comparing regional volume and outline reproducibility of manual, FSL-FIRST and FreeSurfer hippocampus segmentations.Materials and methodsThis study used a dataset from ADNI (Alzheimer's Disease Neuroimaging Initiative), including 20 healthy controls, 40 patients with mild cognitive impairment (MCI), and 20 patients with Alzheimer's disease (AD). For each subject back-to-back (BTB) T1-weighted 3D MPRAGE images were acquired at time-point baseline (BL) and 12 months later (M12). Hippocampi segmentations of all methods were converted into triangulated meshes, regional volumes were extracted and regional Jaccard indices were computed between the hippocampi meshes of paired BTB scans to evaluate reproducibility. Regional volumes and Jaccard indices were modelled as a function of group (G), method (M), hemisphere (H), time-point (T), region (R) and interactions.ResultsFor the volume data the model selection procedure yielded the following significant main effects G, M, H, T and R and interaction effects G-R and M-R. The same model was found for the BTB scans. For all methods volumes reduces with the severity of disease. Significant fixed effects for the regional Jaccard index data were M, R and the interaction M-R. For all methods the middle region was most reproducible, independent of diagnostic group. FSL-FIRST was most and FreeSurfer least reproducible.Discussion/conclusionA novel method to perform detailed analysis of subtle differences in hippocampus segmentation is proposed. The method showed that hippocampal segmentation reproducibility was best for FSL-FIRST and worst for Freesurfer. We also found systematic regional differences in hippocampal segmentation between different methods reinforcing the need of adopting harmonized protocols.

Project description:The developmental pattern of the amygdala throughout childhood and adolescence has been inconsistently reported in previous neuroimaging studies. Given the relatively small size of the amygdala on full brain MRI scans, discrepancies may be partly due to methodological differences in amygdalar segmentation. To investigate the impact of volume extraction methods on amygdala volume, we compared FreeSurfer, FSL and volBrain segmentation measurements with those obtained by manual tracing. The manual tracing method, which we used as the 'gold standard', exhibited almost perfect intra- and inter-rater reliability. We observed systematic differences in amygdala volumes between automatic (FreeSurfer and volBrain) and manual methods. Specifically, compared with the manual tracing, FreeSurfer estimated larger amygdalae, and volBrain produced smaller amygdalae while FSL demonstrated a mixed pattern. The tracing bias was not uniform, but higher for smaller amygdalae. We further modeled amygdalar growth curves using accelerated longitudinal cohort data from the Chinese Color Nest Project (http://deepneuro.bnu.edu.cn/?p=163). Trajectory modeling and statistical assessments of the manually traced amygdalae revealed linearly increasing and parallel developmental patterns for both girls and boys, although the amygdalae of boys were larger than those of girls. Compared to these trajectories, the shapes of developmental curves were similar when using the volBrain derived volumes. FreeSurfer derived trajectories had more nonlinearities and appeared flatter. FSL derived trajectories demonstrated an inverted U shape and were significantly different from those derived from manual tracing method. The use of amygdala volumes adjusted for total gray-matter volumes, but not intracranial volumes, resolved the shape discrepancies and led to reproducible growth curves between manual tracing and the automatic methods (except FSL). Our findings revealed steady growth of the human amygdala, mirroring its functional development across the school age. Methodological improvements are warranted for current automatic tools to achieve more accurate amygdala structure at school age, calling for next generation tools.

Project description:ObjectiveTo develop and validate a tool for automatic selection of the slice of minimal hiatal dimensions (SMHD) and segmentation of the urogenital hiatus (UH) in transperineal ultrasound (TPUS) volumes.MethodsManual selection of the SMHD and segmentation of the UH was performed in TPUS volumes of 116 women with symptomatic pelvic organ prolapse (POP). These data were used to train two deep-learning algorithms. The first algorithm was trained to provide an estimation of the position of the SMHD. Based on this estimation, a slice was selected and fed into the second algorithm, which performed automatic segmentation of the UH. From this segmentation, measurements of the UH area (UHA), anteroposterior diameter (APD) and coronal diameter (CD) were computed automatically. The mean absolute distance between manually and automatically selected SMHD, the overlap (dice similarity index (DSI)) between manual and automatic UH segmentation and the intraclass correlation coefficient (ICC) between manual and automatic UH measurements were assessed on a test set of 30 TPUS volumes.ResultsThe mean absolute distance between manually and automatically selected SMHD was 0.20 cm. All DSI values between manual and automatic UH segmentations were above 0.85. The ICC values between manual and automatic UH measurements were 0.94 (95% CI, 0.87-0.97) for UHA, 0.92 (95% CI, 0.78-0.97) for APD and 0.82 (95% CI, 0.66-0.91) for CD, demonstrating excellent agreement.ConclusionsOur deep-learning algorithms allowed reliable automatic selection of the SMHD and UH segmentation in TPUS volumes of women with symptomatic POP. These algorithms can be implemented in the software of TPUS machines, thus reducing clinical analysis time and simplifying the examination of TPUS data for research and clinical purposes. © 2021 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

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Comparing manual and automatic segmentation of hippocampal volumes: reliability and validity issues in younger and older brains.

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