Abstract #0540

Automated Abdominal Adipose Tissue Segmentation and Quantification in Children using Free-Breathing Dixon MRI and 3D Neural Networks

Sevgi Gokce Kafali^1,2, Timothy R. Adamos³, Kelsey Kuwahara⁴, Ashley M. Dong³, Jessica Li¹, Wenqi Zhou^1,2, Shu-Fu Shih¹, Timoteo I Delgado^1,5, 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, ⁴Cognitive Science, University of California, Los Angeles, Los Angeles, CA, United States, ⁵Physics and Biology in Medicine, University of California, Los Angeles, Los Angeles, CA, United States

Synopsis

Keywords: Body, Pediatric, AI-Based Segmentation

Motivation: The volume and fat content of subcutaneous and visceral adipose tissue (SAT, VAT) are associated with risk for cardiometabolic diseases. Although limited studies automatically segmented SAT, VAT on MRI in children, they targeted narrow age ranges and used conventional sequences that are sensitive to motion artifacts.

Goal(s): To automatically segment and quantify the volume and fat fraction of abdominal SAT, VAT in children using motion-robust free-breathing radial Dixon MRI and state-of-the-art 3D neural networks.

Approach: We enrolled 134 children (6-18 years), then trained 3D nnU-Net and Swin U-Net Transformer.

Results: Both networks yielded accurate volume and fat fraction quantification with promising segmentation performance.

Impact: We developed 3D neural networks to automatically segment and quantify abdominal subcutaneous and visceral adipose tissue (SAT, VAT) characteristics in children (6-18 years) using free-breathing MRI. These methods can be used to study risk factors for cardiometabolic diseases in children.

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