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Abstract #2701

Automated Technique for the Segmentation of Deep and Superficial Subcutaneous Adipose Tissues: Association with Insulin Sensitivity in Normal and Overweight Chinese Men

Suresh Anand Sadananthan1, 2, Bhanu Prakash K.N. 3, Melvin K-S Leow1, 4, ChinMeng Khoo5, Kavita Venkataraman2, Eric Khoo Yin Hao5, Lee Yung Seng1, 6, Peter Gluckman1, Tai E Shyong1, 5, Sendhil S. Velan1, 7

1Singapore Institute for Clinical Sciences, A*STAR, Singapore; 2Department of Obstetrics & Gynaecology, National University of Singapore, Singapore; 3Singapore Bioimaging Consortium, A*STAR, Singapore; 4Department of Endocrinology, Tan Tock Seng Hospital, Singapore; 5Department of Medicine, National University of Singapore, Singapore; 6Department of Pediatrics, National University of Singapore, Singapore; 7Clinical Imaging Research Centre, A*STAR-NUS, Singapore

Obesity is associated with increased insulin resistance, a risk factor for type 2 diabetes or cardiovascular disease. Accumulation of fat in different depots may have different effects on insulin resistance. Visceral adipose tissue (VAT) is thought to have greater impact on insulin resistance than subcutaneous fat. More recently, it has been observed that subcutaneous adipose tissue (SAT) has two sub-compartments, deep SAT (DSAT) and superficial SAT (SSAT)), which may have different effects on insulin resistance. While there are many automated methods to accurately segment SAT and VAT, there is currently no technique to separate DSAT and SSAT. We have implemented a fully automated approach to segment DSAT and SSAT and evaluated it on normal and overweight Chinese adults.