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

被引:9
|
作者
Schlichting, Margaret L. [1 ,4 ]
Mack, Michael L. [1 ,4 ]
Guarino, Katharine F. [1 ]
Preston, Alison R. [1 ,2 ,3 ]
机构
[1] Univ Texas Austin, Ctr Learning & Memory, Austin, TX 78712 USA
[2] Univ Texas Austin, Dept Psychol, Austin, TX 78712 USA
[3] Univ Texas Austin, Dept Neurosci, Austin, TX 78712 USA
[4] Univ Toronto, Dept Psychol, 100 St George St,4th Floor, Toronto, ON M5S 3G3, Canada
基金
美国国家科学基金会; 加拿大创新基金会; 美国国家卫生研究院; 加拿大自然科学与工程研究理事会;
关键词
Development; Structural MRI; Volume; High-resolution MRI; Reliability; MEDIAL TEMPORAL-LOBE; AUTOMATIC SEGMENTATION; EPISODIC MEMORY; DEVELOPMENTAL DIFFERENCES; PREFRONTAL CORTEX; VOLUME; CHILDHOOD; BRAIN; MRI; CHILDREN;
D O I
10.1016/j.neuroimage.2019.01.051
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
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.
引用
收藏
页码:49 / 67
页数:19
相关论文
共 50 条
  • [1] Optimization and validation of automated hippocampal subfield segmentation across the lifespan
    Bender, Andrew R.
    Keresztes, Attila
    Bodammer, Nils C.
    Shing, Yee Lee
    Werkle-Bergner, Markus
    Daugherty, Ana M.
    Yu, Qijing
    Kuehn, Simone
    Lindenberger, Ulman
    Raz, Naftali
    HUMAN BRAIN MAPPING, 2018, 39 (02) : 916 - 931
  • [2] Benchmarking Human Performance in Semi-Automated Image Segmentation
    Eramian, Mark
    Power, Christopher
    Rau, Stephen
    Khandelwal, Pulkit
    INTERACTING WITH COMPUTERS, 2020, 32 (03) : 233 - 245
  • [3] A Comparison of Semi-Automated Brown Adipose Tissue Segmentation Methods
    Lee, Min-Young
    Crandall, John
    Kasal, Krystal
    Wahl, Richard
    JOURNAL OF NUCLEAR MEDICINE, 2020, 61
  • [4] Automated hippocampal unfolding for morphometry and subfield segmentation with HippUnfold
    DeKraker, Jordan
    Haast, Roy A. M.
    Yousif, Mohamed D.
    Karat, Bradley
    Lau, Jonathan C.
    Kohler, Stefan
    Khan, Ali R.
    ELIFE, 2022, 11
  • [5] Test-retest reliability of FreeSurfer automated hippocampal subfield segmentation within and across scanners
    Brown, Emma M.
    Pierce, Meghan E.
    Clark, Dustin C.
    Fischl, Bruce R.
    Iglesias, Juan E.
    Milberg, William P.
    McGlinchey, Regina E.
    Salat, David H.
    NEUROIMAGE, 2020, 210
  • [6] Automated Hippocampal Subfield Segmentation in Amnestic Mild Cognitive Impairments
    Lim, Hyun Kook
    Hong, Seung Chul
    Jung, Won Sang
    Ahn, Kook Jin
    Won, Wang Youn
    Hahn, Changtae
    Kim, In Seong
    Lee, Chang Uk
    DEMENTIA AND GERIATRIC COGNITIVE DISORDERS, 2012, 33 (05) : 327 - 333
  • [7] Automated Hippocampal Subfield Segmentation at 7T MRI
    Wisse, L. E. M.
    Kuijf, H. J.
    Honingh, A. M.
    Wang, H.
    Pluta, J. B.
    Das, S. R.
    Wolk, D. A.
    Zwanenburg, J. J. M.
    Yushkevich, P. A.
    Geerlings, M. I.
    AMERICAN JOURNAL OF NEURORADIOLOGY, 2016, 37 (06) : 1050 - 1057
  • [8] Comparison of Hippocampal Subfield Segmentation Agreement between 2 Automated Protocols across the Adult Life Span
    Samara, A.
    Raji, C. A.
    Li, Z.
    Hershey, T.
    AMERICAN JOURNAL OF NEURORADIOLOGY, 2021, 42 (10) : 1783 - 1789
  • [9] CBCT segmentation of the mandibular canal with both semi-automated and fully automated methods: A systematic review
    Barnes, Neil Abraham
    Sharath, S.
    Dkhar, Winniecia
    Chhaparwal, Yogesh
    Nayak, Kaushik
    CLINICAL EPIDEMIOLOGY AND GLOBAL HEALTH, 2024, 29
  • [10] Semi-automated segmentation of microbes in color images
    Reddy, CK
    Liu, FI
    Dazzo, FB
    COLOR IMAGING VIII: PROCESSING, HARDCOPY, AND APPLICATIONS, 2003, 5008 : 548 - 559