Validation of volumetric and single-slice MRI adipose analysis using a novel fully automated segmentation method

Bryan T. Addeman, Shelby Kutty, Thomas G. Perkins, Abraam S. Soliman, Curtis N. Wiens, Colin M. McCurdy, Melanie D. Beaton, Robert A. Hegele, Charles A. McKenzie

Research output: Contribution to journalArticle

28 Citations (Scopus)

Abstract

Purpose: To validate a fully automated adipose segmentation method with magnetic resonance imaging (MRI) fat fraction abdominal imaging. We hypothesized that this method is suitable for segmentation of subcutaneous adipose tissue (SAT) and intra-abdominal adipose tissue (IAAT) in a wide population range, easy to use, works with a variety of hardware setups, and is highly repeatable.

Materials and Methods: Analysis was performed comparing precision and analysis time of manual and automated segmentation of single-slice imaging, and volumetric imaging (78-88 slices). Volumetric and singleslice data were acquired in a variety of cohorts (body mass index [BMI] 15.6-41.76) including healthy adult volunteers, adolescent volunteers, and subjects with nonalcoholic fatty liver disease and lipodystrophies. A subset of healthy volunteers was analyzed for repeatability in the measurements.

Results: The fully automated segmentation was found to have excellent agreement with manual segmentation with no substantial bias across all study cohorts. Repeatability tests showed a mean coefficient of variation of 1.2±0.6% for SAT, and 2.7±2.2% for IAAT. Analysis with automated segmentation was rapid, requiring 2 seconds per slice compared with 8 minutes per slice with manual segmentation.

Conclusion: We demonstrate the ability to accurately and rapidly segment regional adipose tissue using fat fraction maps across a wide population range, with varying hardware setups and acquisition methods.

Original languageEnglish (US)
Pages (from-to)233-241
Number of pages9
JournalJournal of Magnetic Resonance Imaging
Volume41
Issue number1
DOIs
StatePublished - Jan 1 2015

Fingerprint

Magnetic Resonance Imaging
Intra-Abdominal Fat
Subcutaneous Fat
Healthy Volunteers
Lipodystrophy
Abdominal Fat
Population
Adipose Tissue
Volunteers
Body Mass Index
Cohort Studies
Fats

Keywords

  • Abdominal fat
  • Image processing
  • Intra-abdominal fat
  • Software
  • Subcutaneous fat
  • Visceral adipose tissue

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

Cite this

Validation of volumetric and single-slice MRI adipose analysis using a novel fully automated segmentation method. / Addeman, Bryan T.; Kutty, Shelby; Perkins, Thomas G.; Soliman, Abraam S.; Wiens, Curtis N.; McCurdy, Colin M.; Beaton, Melanie D.; Hegele, Robert A.; McKenzie, Charles A.

In: Journal of Magnetic Resonance Imaging, Vol. 41, No. 1, 01.01.2015, p. 233-241.

Research output: Contribution to journalArticle

Addeman, BT, Kutty, S, Perkins, TG, Soliman, AS, Wiens, CN, McCurdy, CM, Beaton, MD, Hegele, RA & McKenzie, CA 2015, 'Validation of volumetric and single-slice MRI adipose analysis using a novel fully automated segmentation method', Journal of Magnetic Resonance Imaging, vol. 41, no. 1, pp. 233-241. https://doi.org/10.1002/jmri.24526
Addeman, Bryan T. ; Kutty, Shelby ; Perkins, Thomas G. ; Soliman, Abraam S. ; Wiens, Curtis N. ; McCurdy, Colin M. ; Beaton, Melanie D. ; Hegele, Robert A. ; McKenzie, Charles A. / Validation of volumetric and single-slice MRI adipose analysis using a novel fully automated segmentation method. In: Journal of Magnetic Resonance Imaging. 2015 ; Vol. 41, No. 1. pp. 233-241.
@article{166a348c228f4192a5c87e4cbb32f9df,
title = "Validation of volumetric and single-slice MRI adipose analysis using a novel fully automated segmentation method",
abstract = "Purpose: To validate a fully automated adipose segmentation method with magnetic resonance imaging (MRI) fat fraction abdominal imaging. We hypothesized that this method is suitable for segmentation of subcutaneous adipose tissue (SAT) and intra-abdominal adipose tissue (IAAT) in a wide population range, easy to use, works with a variety of hardware setups, and is highly repeatable.Materials and Methods: Analysis was performed comparing precision and analysis time of manual and automated segmentation of single-slice imaging, and volumetric imaging (78-88 slices). Volumetric and singleslice data were acquired in a variety of cohorts (body mass index [BMI] 15.6-41.76) including healthy adult volunteers, adolescent volunteers, and subjects with nonalcoholic fatty liver disease and lipodystrophies. A subset of healthy volunteers was analyzed for repeatability in the measurements.Results: The fully automated segmentation was found to have excellent agreement with manual segmentation with no substantial bias across all study cohorts. Repeatability tests showed a mean coefficient of variation of 1.2±0.6{\%} for SAT, and 2.7±2.2{\%} for IAAT. Analysis with automated segmentation was rapid, requiring 2 seconds per slice compared with 8 minutes per slice with manual segmentation.Conclusion: We demonstrate the ability to accurately and rapidly segment regional adipose tissue using fat fraction maps across a wide population range, with varying hardware setups and acquisition methods.",
keywords = "Abdominal fat, Image processing, Intra-abdominal fat, Software, Subcutaneous fat, Visceral adipose tissue",
author = "Addeman, {Bryan T.} and Shelby Kutty and Perkins, {Thomas G.} and Soliman, {Abraam S.} and Wiens, {Curtis N.} and McCurdy, {Colin M.} and Beaton, {Melanie D.} and Hegele, {Robert A.} and McKenzie, {Charles A.}",
year = "2015",
month = "1",
day = "1",
doi = "10.1002/jmri.24526",
language = "English (US)",
volume = "41",
pages = "233--241",
journal = "Journal of Magnetic Resonance Imaging",
issn = "1053-1807",
publisher = "John Wiley and Sons Inc.",
number = "1",

}

TY - JOUR

T1 - Validation of volumetric and single-slice MRI adipose analysis using a novel fully automated segmentation method

AU - Addeman, Bryan T.

AU - Kutty, Shelby

AU - Perkins, Thomas G.

AU - Soliman, Abraam S.

AU - Wiens, Curtis N.

AU - McCurdy, Colin M.

AU - Beaton, Melanie D.

AU - Hegele, Robert A.

AU - McKenzie, Charles A.

PY - 2015/1/1

Y1 - 2015/1/1

N2 - Purpose: To validate a fully automated adipose segmentation method with magnetic resonance imaging (MRI) fat fraction abdominal imaging. We hypothesized that this method is suitable for segmentation of subcutaneous adipose tissue (SAT) and intra-abdominal adipose tissue (IAAT) in a wide population range, easy to use, works with a variety of hardware setups, and is highly repeatable.Materials and Methods: Analysis was performed comparing precision and analysis time of manual and automated segmentation of single-slice imaging, and volumetric imaging (78-88 slices). Volumetric and singleslice data were acquired in a variety of cohorts (body mass index [BMI] 15.6-41.76) including healthy adult volunteers, adolescent volunteers, and subjects with nonalcoholic fatty liver disease and lipodystrophies. A subset of healthy volunteers was analyzed for repeatability in the measurements.Results: The fully automated segmentation was found to have excellent agreement with manual segmentation with no substantial bias across all study cohorts. Repeatability tests showed a mean coefficient of variation of 1.2±0.6% for SAT, and 2.7±2.2% for IAAT. Analysis with automated segmentation was rapid, requiring 2 seconds per slice compared with 8 minutes per slice with manual segmentation.Conclusion: We demonstrate the ability to accurately and rapidly segment regional adipose tissue using fat fraction maps across a wide population range, with varying hardware setups and acquisition methods.

AB - Purpose: To validate a fully automated adipose segmentation method with magnetic resonance imaging (MRI) fat fraction abdominal imaging. We hypothesized that this method is suitable for segmentation of subcutaneous adipose tissue (SAT) and intra-abdominal adipose tissue (IAAT) in a wide population range, easy to use, works with a variety of hardware setups, and is highly repeatable.Materials and Methods: Analysis was performed comparing precision and analysis time of manual and automated segmentation of single-slice imaging, and volumetric imaging (78-88 slices). Volumetric and singleslice data were acquired in a variety of cohorts (body mass index [BMI] 15.6-41.76) including healthy adult volunteers, adolescent volunteers, and subjects with nonalcoholic fatty liver disease and lipodystrophies. A subset of healthy volunteers was analyzed for repeatability in the measurements.Results: The fully automated segmentation was found to have excellent agreement with manual segmentation with no substantial bias across all study cohorts. Repeatability tests showed a mean coefficient of variation of 1.2±0.6% for SAT, and 2.7±2.2% for IAAT. Analysis with automated segmentation was rapid, requiring 2 seconds per slice compared with 8 minutes per slice with manual segmentation.Conclusion: We demonstrate the ability to accurately and rapidly segment regional adipose tissue using fat fraction maps across a wide population range, with varying hardware setups and acquisition methods.

KW - Abdominal fat

KW - Image processing

KW - Intra-abdominal fat

KW - Software

KW - Subcutaneous fat

KW - Visceral adipose tissue

UR - http://www.scopus.com/inward/record.url?scp=84919390509&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84919390509&partnerID=8YFLogxK

U2 - 10.1002/jmri.24526

DO - 10.1002/jmri.24526

M3 - Article

VL - 41

SP - 233

EP - 241

JO - Journal of Magnetic Resonance Imaging

JF - Journal of Magnetic Resonance Imaging

SN - 1053-1807

IS - 1

ER -