Abstract #0553

Automated Adipose Tissue Segmentation using 3D Attention-Based Competitive Dense Networks and Volumetric Multi-Contrast MRI

Sevgi Gokce Kafali^1,2, Shu-Fu Shih^1,2, Xinzhou Li¹, Shilpy Chowdhury³, Spencer Loong⁴, Samuel Barnes³, Zhaoping Li⁵, 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, ³Radiology, Loma Linda University Medical Center, Loma Linda, CA, United States, ⁴Psychology, Loma Linda University School of Mental Health, Loma Linda, CA, United States, ⁵Medicine, University of California, Los Angeles, Los Angeles, CA, United States

Synopsis

Subcutaneous and visceral adipose tissue (SAT/VAT) are potential biomarkers to detect future risks of metabolic diseases. However, the current standard for analysis relies on manual annotations that require expert knowledge and are time-consuming. Previous neural networks for automatically segmenting adipose tissue had suboptimal performance for VAT. This work developed a new 3D attention-based competitive dense network to rapidly (84 ms/slice) and accurately segment SAT/VAT in adults with obesity by leveraging multi-contrast MRI inputs and considering the complex VAT features. The new network achieved high Dice scores (>0.96) and accurate volume measurements (difference<1.6%) for SAT/VAT with respect to manual annotations.

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