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Optimization and validation of automated hippocampal subfield segmentation across the lifespan

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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; Kühn, Simone; Lindenberger, Ulman; Raz, Naftali.

In: HUM BRAIN MAPP, Vol. 39, No. 2, 02.2018, p. 916-931.

Research output: SCORING: Contribution to journal › SCORING: Journal article › Research › peer-review

Harvard

Bender, AR, Keresztes, A, Bodammer, NC, Shing, YL, Werkle-Bergner, M, Daugherty, AM, Yu, Q, Kühn, S, Lindenberger, U & Raz, N 2018, 'Optimization and validation of automated hippocampal subfield segmentation across the lifespan', HUM BRAIN MAPP, vol. 39, no. 2, pp. 916-931. https://doi.org/10.1002/hbm.23891

APA

Bender, A. R., Keresztes, A., Bodammer, N. C., Shing, Y. L., Werkle-Bergner, M., Daugherty, A. M., Yu, Q., Kühn, S., Lindenberger, U., & Raz, N. (2018). Optimization and validation of automated hippocampal subfield segmentation across the lifespan. HUM BRAIN MAPP, 39(2), 916-931. https://doi.org/10.1002/hbm.23891

Vancouver

Bender AR, Keresztes A, Bodammer NC, Shing YL, Werkle-Bergner M, Daugherty AM et al. Optimization and validation of automated hippocampal subfield segmentation across the lifespan. HUM BRAIN MAPP. 2018 Feb;39(2):916-931. https://doi.org/10.1002/hbm.23891

Bibtex

@article{bd89150612664c9d8d0a91079a73d20e,
title = "Optimization and validation of automated hippocampal subfield segmentation across the lifespan",
abstract = "Automated segmentation of hippocampal (HC) subfields from magnetic resonance imaging (MRI) is gaining popularity, but automated procedures that afford high speed and reproducibility have yet to be extensively validated against the standard, manual morphometry. We evaluated the concurrent validity of an automated method for hippocampal subfields segmentation (automated segmentation of hippocampal subfields, ASHS; Yushkevich et al., ) using a customized atlas of the HC body, with manual morphometry as a standard. We built a series of customized atlases comprising the entorhinal cortex (ERC) and subfields of the HC body from manually segmented images, and evaluated the correspondence of automated segmentations with manual morphometry. In samples with age ranges of 6-24 and 62-79 years, 20 participants each, we obtained validity coefficients (intraclass correlations, ICC) and spatial overlap measures (dice similarity coefficient) that varied substantially across subfields. Anterior and posterior HC body evidenced the greatest discrepancies between automated and manual segmentations. Adding anterior and posterior slices for atlas creation and truncating automated output to the ranges manually defined by multiple neuroanatomical landmarks substantially improved the validity of automated segmentation, yielding ICC above 0.90 for all subfields and alleviating systematic bias. We cross-validated the developed atlas on an independent sample of 30 healthy adults (age 31-84) and obtained good to excellent agreement: ICC (2) = 0.70-0.92. Thus, with described customization steps implemented by experts trained in MRI neuroanatomy, ASHS shows excellent concurrent validity, and can become a promising method for studying age-related changes in HC subfield volumes.",
keywords = "Journal Article",
author = "Bender, {Andrew R} and Attila Keresztes and Bodammer, {Nils C} and Shing, {Yee Lee} and Markus Werkle-Bergner and Daugherty, {Ana M} and Qijing Yu and Simone K{\"u}hn and Ulman Lindenberger and Naftali Raz",
note = "{\textcopyright} 2017 Wiley Periodicals, Inc.",
year = "2018",
month = feb,
doi = "10.1002/hbm.23891",
language = "English",
volume = "39",
pages = "916--931",
journal = "HUM BRAIN MAPP",
issn = "1065-9471",
publisher = "Wiley-Liss Inc.",
number = "2",

}

RIS

TY - JOUR

T1 - Optimization and validation of automated hippocampal subfield segmentation across the lifespan

AU - Bender, Andrew R

AU - Keresztes, Attila

AU - Bodammer, Nils C

AU - Shing, Yee Lee

AU - Werkle-Bergner, Markus

AU - Daugherty, Ana M

AU - Yu, Qijing

AU - Kühn, Simone

AU - Lindenberger, Ulman

AU - Raz, Naftali

N1 - © 2017 Wiley Periodicals, Inc.

PY - 2018/2

Y1 - 2018/2

N2 - Automated segmentation of hippocampal (HC) subfields from magnetic resonance imaging (MRI) is gaining popularity, but automated procedures that afford high speed and reproducibility have yet to be extensively validated against the standard, manual morphometry. We evaluated the concurrent validity of an automated method for hippocampal subfields segmentation (automated segmentation of hippocampal subfields, ASHS; Yushkevich et al., ) using a customized atlas of the HC body, with manual morphometry as a standard. We built a series of customized atlases comprising the entorhinal cortex (ERC) and subfields of the HC body from manually segmented images, and evaluated the correspondence of automated segmentations with manual morphometry. In samples with age ranges of 6-24 and 62-79 years, 20 participants each, we obtained validity coefficients (intraclass correlations, ICC) and spatial overlap measures (dice similarity coefficient) that varied substantially across subfields. Anterior and posterior HC body evidenced the greatest discrepancies between automated and manual segmentations. Adding anterior and posterior slices for atlas creation and truncating automated output to the ranges manually defined by multiple neuroanatomical landmarks substantially improved the validity of automated segmentation, yielding ICC above 0.90 for all subfields and alleviating systematic bias. We cross-validated the developed atlas on an independent sample of 30 healthy adults (age 31-84) and obtained good to excellent agreement: ICC (2) = 0.70-0.92. Thus, with described customization steps implemented by experts trained in MRI neuroanatomy, ASHS shows excellent concurrent validity, and can become a promising method for studying age-related changes in HC subfield volumes.

AB - Automated segmentation of hippocampal (HC) subfields from magnetic resonance imaging (MRI) is gaining popularity, but automated procedures that afford high speed and reproducibility have yet to be extensively validated against the standard, manual morphometry. We evaluated the concurrent validity of an automated method for hippocampal subfields segmentation (automated segmentation of hippocampal subfields, ASHS; Yushkevich et al., ) using a customized atlas of the HC body, with manual morphometry as a standard. We built a series of customized atlases comprising the entorhinal cortex (ERC) and subfields of the HC body from manually segmented images, and evaluated the correspondence of automated segmentations with manual morphometry. In samples with age ranges of 6-24 and 62-79 years, 20 participants each, we obtained validity coefficients (intraclass correlations, ICC) and spatial overlap measures (dice similarity coefficient) that varied substantially across subfields. Anterior and posterior HC body evidenced the greatest discrepancies between automated and manual segmentations. Adding anterior and posterior slices for atlas creation and truncating automated output to the ranges manually defined by multiple neuroanatomical landmarks substantially improved the validity of automated segmentation, yielding ICC above 0.90 for all subfields and alleviating systematic bias. We cross-validated the developed atlas on an independent sample of 30 healthy adults (age 31-84) and obtained good to excellent agreement: ICC (2) = 0.70-0.92. Thus, with described customization steps implemented by experts trained in MRI neuroanatomy, ASHS shows excellent concurrent validity, and can become a promising method for studying age-related changes in HC subfield volumes.

KW - Journal Article

U2 - 10.1002/hbm.23891

DO - 10.1002/hbm.23891

M3 - SCORING: Journal article

C2 - 29171108

VL - 39

SP - 916

EP - 931

JO - HUM BRAIN MAPP

JF - HUM BRAIN MAPP

SN - 1065-9471

IS - 2

ER -