Progress toward automatic classification of human brown adipose tissue using biomedical imaging

Aliya Gifford; Theodore F. Towse; Ronald C. Walker; Malcom J. Avison; E. Brain Welch

doi:10.1117/12.2082955

17 March 2015 Progress toward automatic classification of human brown adipose tissue using biomedical imaging

Aliya Gifford, Theodore F. Towse, Ronald C. Walker, Malcom J. Avison, E. Brain Welch

Proceedings Volume 9417, Medical Imaging 2015: Biomedical Applications in Molecular, Structural, and Functional Imaging; 94170A (2015) https://doi.org/10.1117/12.2082955
Event: SPIE Medical Imaging, 2015, Orlando, Florida, United States

Abstract

Brown adipose tissue (BAT) is a small but significant tissue, which may play an important role in obesity and the pathogenesis of metabolic syndrome. Interest in studying BAT in adult humans is increasing, but in order to quantify BAT volume in a single measurement or to detect changes in BAT over the time course of a longitudinal experiment, BAT needs to first be reliably differentiated from surrounding tissue. Although the uptake of the radiotracer ¹⁸F-Fluorodeoxyglucose (¹⁸F-FDG) in adipose tissue on positron emission tomography (PET) scans following cold exposure is accepted as an indication of BAT, it is not a definitive indicator, and to date there exists no standardized method for segmenting BAT. Consequently, there is a strong need for robust automatic classification of BAT based on properties measured with biomedical imaging. In this study we begin the process of developing an automated segmentation method based on properties obtained from fat-water MRI and PET-CT scans acquired on ten healthy adult subjects.

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Aliya Gifford, Theodore F. Towse, Ronald C. Walker, Malcom J. Avison, and E. Brain Welch "Progress toward automatic classification of human brown adipose tissue using biomedical imaging", Proc. SPIE 9417, Medical Imaging 2015: Biomedical Applications in Molecular, Structural, and Functional Imaging, 94170A (17 March 2015); https://doi.org/10.1117/12.2082955

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KEYWORDS

Tissues

Magnetic resonance imaging

Positron emission tomography

Image segmentation

Biomedical optics

Medical research

Medicine

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