Automated quantification of muscle and fat in the thigh from water-, fat- and non-suppressed MR images

Sokratis Makrogiannis, Suraj Serai, Kenneth W. Fishbein, Willie Laney, Catherine Schreiber, William B. Ershler, Luigi Ferrucci, Richard G. Spencer

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A key parameter in metabolic and pathologic studies is the estimation of body tissue distribution. This is a laborious and operator-dependent process. In this work we introduce an unsupervised muscle and fat quantification algorithm based on water only, fat only and water-and-fat MRI images of the mid-thigh area. We first use parametric deformable models to segment the subcutaneous fat and then apply centroid clustering in the feature domain defined by the voxel intensities in water only and fat only images to detect the inter-muscular fat, muscle and bone. This tissue decomposition permits the computation of volumetric and area measures of fat and muscle. We tested the proposed method on 9 participants and validated these measures against values obtained from a semi-manual clinician-driven analysis of single-slice mid-thigh CT images of the same participants. Our approach was found to be statistically consistent with the semi-manual reference method, and was able to address inter-participant anatomic variability and intensity inhomogeneity effects.

Original languageEnglish (US)
Title of host publication10th IEEE International Conference on Bioinformatics and Bioengineering 2010, BIBE 2010
Pages52-57
Number of pages6
DOIs
StatePublished - 2010
Externally publishedYes
Event10th IEEE International Conference on Bioinformatics and Bioengineering, BIBE-2010 - Philadelphia, PA, United States
Duration: May 31 2010Jun 3 2010

Other

Other10th IEEE International Conference on Bioinformatics and Bioengineering, BIBE-2010
CountryUnited States
CityPhiladelphia, PA
Period5/31/106/3/10

Fingerprint

Thigh
Oils and fats
Muscle
Fats
Muscles
Water
Tissue
Subcutaneous Fat
Tissue Distribution
Cluster Analysis
Magnetic resonance imaging
Bone
Bone and Bones
Decomposition

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics

Cite this

Makrogiannis, S., Serai, S., Fishbein, K. W., Laney, W., Schreiber, C., Ershler, W. B., ... Spencer, R. G. (2010). Automated quantification of muscle and fat in the thigh from water-, fat- and non-suppressed MR images. In 10th IEEE International Conference on Bioinformatics and Bioengineering 2010, BIBE 2010 (pp. 52-57). [5521714] https://doi.org/10.1109/BIBE.2010.18

Automated quantification of muscle and fat in the thigh from water-, fat- and non-suppressed MR images. / Makrogiannis, Sokratis; Serai, Suraj; Fishbein, Kenneth W.; Laney, Willie; Schreiber, Catherine; Ershler, William B.; Ferrucci, Luigi; Spencer, Richard G.

10th IEEE International Conference on Bioinformatics and Bioengineering 2010, BIBE 2010. 2010. p. 52-57 5521714.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Makrogiannis, S, Serai, S, Fishbein, KW, Laney, W, Schreiber, C, Ershler, WB, Ferrucci, L & Spencer, RG 2010, Automated quantification of muscle and fat in the thigh from water-, fat- and non-suppressed MR images. in 10th IEEE International Conference on Bioinformatics and Bioengineering 2010, BIBE 2010., 5521714, pp. 52-57, 10th IEEE International Conference on Bioinformatics and Bioengineering, BIBE-2010, Philadelphia, PA, United States, 5/31/10. https://doi.org/10.1109/BIBE.2010.18
Makrogiannis S, Serai S, Fishbein KW, Laney W, Schreiber C, Ershler WB et al. Automated quantification of muscle and fat in the thigh from water-, fat- and non-suppressed MR images. In 10th IEEE International Conference on Bioinformatics and Bioengineering 2010, BIBE 2010. 2010. p. 52-57. 5521714 https://doi.org/10.1109/BIBE.2010.18
Makrogiannis, Sokratis ; Serai, Suraj ; Fishbein, Kenneth W. ; Laney, Willie ; Schreiber, Catherine ; Ershler, William B. ; Ferrucci, Luigi ; Spencer, Richard G. / Automated quantification of muscle and fat in the thigh from water-, fat- and non-suppressed MR images. 10th IEEE International Conference on Bioinformatics and Bioengineering 2010, BIBE 2010. 2010. pp. 52-57
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