Fully automated large-scale assessment of visceral and subcutaneous abdominal adipose tissue by magnetic resonance imaging

T. H. Liou, W. P. Chan, L. C. Pan, P. W. Lin, P. Chou, Chi-Hsien Chen

研究成果: 雜誌貢獻文章

51 引文 (Scopus)

摘要

Objective: To describe and evaluate a fully automated method for characterizing abdominal adipose tissue from magnetic resonance (MR) transverse body scans. Methods: Four MR pulse sequences were applied: SE, FLAIR, STIR, and FRFSE. On 39 subjects, each abdomen was traversed by 15 contiguous transaxial images. The total abdominal adipose tissue (TAAT) was calculated from thresholds obtained by slice histogram analysis. The same thresholds were also used in the manual volume calculation of TAAT, subcutaneous abdominal adipose tissue (SAAT) and visceral abdominal adipose tissue (VAAT). Image segmentation methods, including edge detection, mathematical morphology, and knowledge-based curve fitting, were used to automatically separate SAAT from VAAT in various 'nonstandard' cases such as those with heterogeneous magnetic fields and movement artefacts. Results: The percentage root mean squared errors of the method for SAAT and VAAT ranged from 1.0 to 2.7% for the four sequences. It took approximately 7 and 15 min to complete the 15-slice volume estimation of the three adipose tissue classes using automated and manual methods, respectively. Conclusion: The results demonstrate that the proposed method is robust and accurate. Although the separation of SAAT and VAAT is not always perfect, this method could be especially helpful in dealing with large amounts of data such as in epidemiological studies.
原文英語
頁(從 - 到)844-852
頁數9
期刊International Journal of Obesity
30
發行號5
DOIs
出版狀態已發佈 - 五月 2006

指紋

Abdominal Subcutaneous Fat
Abdominal Fat
magnetic resonance imaging
adipose tissue
Magnetic Resonance Imaging
Intra-Abdominal Fat
Magnetic Resonance Spectroscopy
Magnetic Fields
methodology
Abdomen
Artifacts
Adipose Tissue
Epidemiologic Studies
magnetic fields
abdomen
epidemiological studies

ASJC Scopus subject areas

  • Medicine (miscellaneous)
  • Public Health, Environmental and Occupational Health
  • Endocrinology
  • Food Science
  • Endocrinology, Diabetes and Metabolism

引用此文

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abstract = "Objective: To describe and evaluate a fully automated method for characterizing abdominal adipose tissue from magnetic resonance (MR) transverse body scans. Methods: Four MR pulse sequences were applied: SE, FLAIR, STIR, and FRFSE. On 39 subjects, each abdomen was traversed by 15 contiguous transaxial images. The total abdominal adipose tissue (TAAT) was calculated from thresholds obtained by slice histogram analysis. The same thresholds were also used in the manual volume calculation of TAAT, subcutaneous abdominal adipose tissue (SAAT) and visceral abdominal adipose tissue (VAAT). Image segmentation methods, including edge detection, mathematical morphology, and knowledge-based curve fitting, were used to automatically separate SAAT from VAAT in various 'nonstandard' cases such as those with heterogeneous magnetic fields and movement artefacts. Results: The percentage root mean squared errors of the method for SAAT and VAAT ranged from 1.0 to 2.7{\%} for the four sequences. It took approximately 7 and 15 min to complete the 15-slice volume estimation of the three adipose tissue classes using automated and manual methods, respectively. Conclusion: The results demonstrate that the proposed method is robust and accurate. Although the separation of SAAT and VAAT is not always perfect, this method could be especially helpful in dealing with large amounts of data such as in epidemiological studies.",
keywords = "Abdominal fat volume, Image segmentation, Knowledge-based curve fitting, Magnetic resonance imaging (MRI), Mathematical morphology",
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T1 - Fully automated large-scale assessment of visceral and subcutaneous abdominal adipose tissue by magnetic resonance imaging

AU - Liou, T. H.

AU - Chan, W. P.

AU - Pan, L. C.

AU - Lin, P. W.

AU - Chou, P.

AU - Chen, Chi-Hsien

PY - 2006/5

Y1 - 2006/5

N2 - Objective: To describe and evaluate a fully automated method for characterizing abdominal adipose tissue from magnetic resonance (MR) transverse body scans. Methods: Four MR pulse sequences were applied: SE, FLAIR, STIR, and FRFSE. On 39 subjects, each abdomen was traversed by 15 contiguous transaxial images. The total abdominal adipose tissue (TAAT) was calculated from thresholds obtained by slice histogram analysis. The same thresholds were also used in the manual volume calculation of TAAT, subcutaneous abdominal adipose tissue (SAAT) and visceral abdominal adipose tissue (VAAT). Image segmentation methods, including edge detection, mathematical morphology, and knowledge-based curve fitting, were used to automatically separate SAAT from VAAT in various 'nonstandard' cases such as those with heterogeneous magnetic fields and movement artefacts. Results: The percentage root mean squared errors of the method for SAAT and VAAT ranged from 1.0 to 2.7% for the four sequences. It took approximately 7 and 15 min to complete the 15-slice volume estimation of the three adipose tissue classes using automated and manual methods, respectively. Conclusion: The results demonstrate that the proposed method is robust and accurate. Although the separation of SAAT and VAAT is not always perfect, this method could be especially helpful in dealing with large amounts of data such as in epidemiological studies.

AB - Objective: To describe and evaluate a fully automated method for characterizing abdominal adipose tissue from magnetic resonance (MR) transverse body scans. Methods: Four MR pulse sequences were applied: SE, FLAIR, STIR, and FRFSE. On 39 subjects, each abdomen was traversed by 15 contiguous transaxial images. The total abdominal adipose tissue (TAAT) was calculated from thresholds obtained by slice histogram analysis. The same thresholds were also used in the manual volume calculation of TAAT, subcutaneous abdominal adipose tissue (SAAT) and visceral abdominal adipose tissue (VAAT). Image segmentation methods, including edge detection, mathematical morphology, and knowledge-based curve fitting, were used to automatically separate SAAT from VAAT in various 'nonstandard' cases such as those with heterogeneous magnetic fields and movement artefacts. Results: The percentage root mean squared errors of the method for SAAT and VAAT ranged from 1.0 to 2.7% for the four sequences. It took approximately 7 and 15 min to complete the 15-slice volume estimation of the three adipose tissue classes using automated and manual methods, respectively. Conclusion: The results demonstrate that the proposed method is robust and accurate. Although the separation of SAAT and VAAT is not always perfect, this method could be especially helpful in dealing with large amounts of data such as in epidemiological studies.

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KW - Knowledge-based curve fitting

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KW - Mathematical morphology

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