Automated Adipose Tissue Segmentation using 3D Attention-Based Competitive Dense Networks and Volumetric Multi-Contrast MRI
Sevgi Gokce Kafali1,2, Shu-Fu Shih1,2, Xinzhou Li1, Shilpy Chowdhury3, Spencer Loong4, Samuel Barnes3, Zhaoping Li5, and Holden H. Wu1,2
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
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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