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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