Abstract #0071

Fully Convolutional Networks for Adipose Tissue Segmentation Using Free-Breathing Abdominal MRI in Healthy and Overweight Children

Sevgi Gokce Kafali^1,2, Shu-Fu Shih^1,2, Xinzhou Li^1,2, Tess Armstrong¹, Karrie V. Ly³, Shahnaz Ghahremani¹, Kara L. Calkins³, and Holden H. Wu^1,2

¹Radiological Sciences, University of California, Los Angeles, Los Angeles, CA, United States, ²Bioengineering, University of California, Los Angeles, Los Angeles, CA, United States, ³Pediatrics, University of California, Los Angeles, Los Angeles, CA, United States

The volume and fat content of subcutaneous and visceral adipose tissue (SAT and VAT) have strong associations with metabolic diseases in overweight children. Currently, the gold standard to measure the SAT/VAT content is manual annotation, which is time-consuming. Although several studies showed promising results using machine and deep learning to segment SAT and VAT in adults, there is a lack of research on deep learning-based SAT and VAT segmentation in children. Here, we investigated the performance of 3 deep learning network architectures to segment SAT and VAT in children.

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