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Abstract #4012

Semi-automatic segmentation analysis of adipose tissue in thigh and lower leg to assess the fat infiltration in Type 2 Diabetes Mellitus

Sunil K. Valaparla 1,2 , Qi Peng 3 , Feng Gao 1 , Timothy Q. Duong 1 , and Geoffrey D. Clarke 1,2

1 Research Imaging Institute, University of Texas Health Science Center at San Antonio, San Antonio, Texas, United States, 2 Radiology, University of Texas Health Science Center at San Antonio, San Antonio, Texas, United States, 3 Department of Radiology, Albert Einstein College of Medicine, Bronx, NY, United States

ype 2 Diabetes Mellitus (T2DM) has been associated with increased amount and distribution of intermuscular adipose tissue (IMAT). This study evaluated a fuzzy clustering (FCT) segmentation algorithm in investigating differences in distribution of IMAT and SAT in T1-weighted thigh and lower leg images between T2DM and controls. T-test showed no statistical significance between T2DM and Controls for thigh SAT and IMAT and for lower leg SAT but was significant for IMAT. FCT algorithm with low computational complexity and processing time enables effective characterization of muscular fat in MR images and can be used to assess IMAT for large-scale clinical studies.

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