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Proceedings Paper

An automated segmentation for direct assessment of adipose tissue distribution from thoracic and abdominal Dixon-technique MR images
Author(s): Jason E. Hill; Maria Fernandez-Del-Valle; Ryan Hayden; Sunanda Mitra
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Paper Abstract

Magnetic Resonance Imaging (MRI) and Magnetic Resonance Spectroscopy (MRS) together have become the gold standard in the precise quantification of body fat. The study of the quantification of fat in the human body has matured in recent years from a simplistic interest in the whole-body fat content to detailing regional fat distributions. The realization that body-fat, or adipose tissue (AT) is far from being a mere aggregate mass or deposit but a biologically active organ in and of itself, may play a role in the association between obesity and the various pathologies that are the biggest health issues of our time. Furthermore, a major bottleneck in most medical image assessments of adipose tissue content and distribution is the lack of automated image analysis. This motivated us to develop a proper and at least partially automated methodology to accurately and reproducibly determine both body fat content and distribution in the human body, which is to be applied to cross-sectional and longitudinal studies. The AT considered here is located beneath the skin (subcutaneous) as well as around the internal organs and between muscles (visceral and inter-muscular). There are also special fat depots on and around the heart (pericardial) as well as around the aorta (peri-aortic). Our methods focus on measuring and classifying these various AT deposits in the human body in an intervention study that involves the acquisition of thoracic and abdominal MR images via a Dixon technique.

Paper Details

Date Published: 24 February 2017
PDF: 11 pages
Proc. SPIE 10133, Medical Imaging 2017: Image Processing, 1013315 (24 February 2017); doi: 10.1117/12.2254481
Show Author Affiliations
Jason E. Hill, Texas Tech Univ. (United States)
Maria Fernandez-Del-Valle, Southern Illinois Univ. Edwardsville (United States)
Ryan Hayden, Texas Tech Univ. (United States)
Sunanda Mitra, Texas Tech Univ. (United States)

Published in SPIE Proceedings Vol. 10133:
Medical Imaging 2017: Image Processing
Martin A. Styner; Elsa D. Angelini, Editor(s)

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