Dataset Information

Performance of semi-automated hippocampal subfield segmentation methods across ages in a pediatric sample.

ABSTRACT: Episodic memory function has been shown to depend critically on the hippocampus. This region is made up of a number of subfields, which differ in both cytoarchitectural features and functional roles in the mature brain. Recent neuroimaging work in children and adolescents has suggested that these regions may undergo different developmental trajectories-a fact that has important implications for how we think about learning and memory processes in these populations. Despite the growing research interest in hippocampal structure and function at the subfield level in healthy young adults, comparatively fewer studies have been carried out looking at subfield development. One barrier to studying these questions has been that manual segmentation of hippocampal subfields-considered by many to be the best available approach for defining these regions-is laborious and can be infeasible for large cross-sectional or longitudinal studies of cognitive development. Moreover, manual segmentation requires some subjectivity and is not impervious to bias or error. In a developmental sample of individuals spanning 6-30 years, we assessed the degree to which two semi-automated segmentation approaches-one approach based on Automated Segmentation of Hippocampal Subfields (ASHS) and another utilizing Advanced Normalization Tools (ANTs)-approximated manual subfield delineation on each individual by a single expert rater. Our main question was whether performance varied as a function of age group. Across several quantitative metrics, we found negligible differences in subfield validity across the child, adolescent, and adult age groups, suggesting that these methods can be reliably applied to developmental studies. We conclude that ASHS outperforms ANTs overall and is thus preferable for analyses carried out in individual subject space. However, we underscore that ANTs is also acceptable and may be well-suited for analyses requiring normalization to a single group template (e.g., voxelwise analyses across a wide age range). Previous work has supported the use of such methods in healthy young adults, as well as several special populations such as older adults and those suffering from mild cognitive impairment. Our results extend these previous findings to show that ASHS and ANTs can also be used in pediatric populations as young as six.

SUBMITTER: Schlichting ML

PROVIDER: S-EPMC6524646 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature

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Performance of semi-automated hippocampal subfield segmentation methods across ages in a pediatric sample.

Schlichting Margaret L ML Mack Michael L ML Guarino Katharine F KF Preston Alison R AR

NeuroImage 20190205

Episodic memory function has been shown to depend critically on the hippocampus. This region is made up of a number of subfields, which differ in both cytoarchitectural features and functional roles in the mature brain. Recent neuroimaging work in children and adolescents has suggested that these regions may undergo different developmental trajectories-a fact that has important implications for how we think about learning and memory processes in these populations. Despite the growing research in ...[more]

PMID: 30731245

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Project description:BACKGROUND AND PURPOSE:High resolution 7T MRI is increasingly used to investigate hippocampal subfields in vivo, but most studies rely on manual segmentation which is labor intensive. We aimed to evaluate an automated technique to segment hippocampal subfields and the entorhinal cortex at 7T MRI. MATERIALS AND METHODS:The cornu ammonis (CA)1, CA2, CA3, dentate gyrus, subiculum, and entorhinal cortex were manually segmented, covering most of the long axis of the hippocampus on 0.70-mm(3) T2-weighted 7T images of 26 participants (59 ± 9 years, 46% men). The automated segmentation of hippocampal subfields approach was applied and evaluated by using leave-one-out cross-validation. RESULTS:Comparison of automated segmentations with corresponding manual segmentations yielded a Dice similarity coefficient of >0.75 for CA1, the dentate gyrus, subiculum, and entorhinal cortex and >0.54 for CA2 and CA3. Intraclass correlation coefficients were >0.74 for CA1, the dentate gyrus, and subiculum; and >0.43 for CA2, CA3, and the entorhinal cortex. Restricting the comparison of the entorhinal cortex segmentation to a smaller range along the anteroposterior axis improved both intraclass correlation coefficients (left: 0.71; right: 0.82) and Dice similarity coefficients (left: 0.78; right: 0.77). The accuracy of the automated segmentation versus a manual rater was lower, though only slightly for most subfields, than the intrarater reliability of an expert manual rater, but it was similar to or slightly higher than the accuracy of an expert-versus-manual rater with ?170 hours of training for almost all subfields. CONCLUSIONS:This work demonstrates the feasibility of using a computational technique to automatically label hippocampal subfields and the entorhinal cortex at 7T MRI, with a high accuracy for most subfields that is competitive with the labor-intensive manual segmentation. The software and atlas are publicly available: http://www.nitrc.org/projects/ashs/.

Project description:In this study, we developed a multi-scale Convolutional neural network based Automated hippocampal subfield Segmentation Toolbox (CAST) for automated segmentation of hippocampal subfields. Although training CAST required approximately three days on a single workstation with a high-quality GPU card, CAST can segment a new subject in less than 1 min even with GPU acceleration disabled, thus this method is more time efficient than current automated methods and manual segmentation. This toolbox is highly flexible with either a single modality or multiple modalities and can be easily set up to be trained with a researcher's unique data. A 3D multi-scale deep convolutional neural network is the key algorithm used in the toolbox. The main merit of multi-scale images is the capability to capture more global structural information from down-sampled images without dramatically increasing memory and computational burden. The original images capture more local information to refine the boundary between subfields. Residual learning is applied to alleviate the vanishing gradient problem and improve the performance with a deeper network. We applied CAST with the same settings on two datasets, one 7T dataset (the UMC dataset) with only the T2 image and one 3T dataset (the MNI dataset) with both T1 and T2 images available. The segmentation accuracy of both CAST and the state-of-the-art automated method ASHS, in terms of the dice similarity coefficient (DSC), were comparable. CAST significantly improved the reliability of segmenting small subfields, such as CA2, CA3, and the entorhinal cortex (ERC), in terms of the intraclass correlation coefficient (ICC). Both ASHS and manual segmentation process some subfields (e.g. CA2 and ERC) with high DSC values but low ICC values, consequently increasing the difficulty of judging segmentation quality. CAST produces very consistent DSC and ICC values, with a maximal discrepancy of 0.01 (DSC-ICC) across all subfields. The pre-trained model, source code, and settings for the CAST toolbox are publicly available.

Project description:The increased understanding that neuropathology begins decades before symptom onset, has led to the conceptualization and widespread utilization of Mild Cognitive Impairment (MCI) as an important transitional state between healthy aging and dementia. Further subcategorization to MCI subtype has led to more distinct prognoses and it is widely considered that amnestic and non-amnestic MCI (aMCI, naMCI) likely have distinct pathophysiologies. Yet, accurately classification remains contentious. Here, we differentiate hippocampal subfield volume between subtypes, diagnosed according to stringent clinical consensus criteria, where aMCI is characterized based on deficits in delayed recall (rather than encoding). We then identify memory performance correlates to subfield volume and associations with long-term cognitive performance and outcome. 3D T1-weighted structural MRI was acquired in 142 participants recruited from the Healthy Brain Aging (HBA) Clinic and diagnosed with aMCI (n = 38), naMCI (n = 84) or subjective memory complaints (SMC; n = 20). T1-weighted datasets were processed with the cortical and hippocampal subfield processing streams in FreeSurfer (v6.0). Subfield volumes, and associations with baseline and longitudinal objective memory scores were then examined. Subfield volumes were found to differentiate clinical profiles: subiculum, CA1, CA4 and dentate gyrus volumes were significantly reduced in aMCI compared to both naMCI and SMC. CA1 subfield volume was shown to predict concurrent memory performance in aMCI, while dentate gyrus volume significantly predicted longitudinal verbal learning and memory decline in the entire cohort. Our findings demonstrate that using a more stringent diagnostic approach to characterizing aMCI is well justified, as delayed recall deficits are strongly linked to underlying volumetric subfield reductions in CA1, CA4 and the dentate gyrus, subfields known to be associated with mnemonic processes. Further research is now warranted to replicate these findings in other MCI samples.

Dataset Information

Performance of semi-automated hippocampal subfield segmentation methods across ages in a pediatric sample.

Publications

Performance of semi-automated hippocampal subfield segmentation methods across ages in a pediatric sample.

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