Automated 3D Volume Segmentation of Subcutaneous and Visceral Abdominal Fat Using Fat-Water Imaging

Motivation: The study is driven by the need to accurately quantify abdominal fat, particularly visceral fat, to better understand its link with metabolic diseasesGoal(s): This research aims to develop a robust, automated 3D segmentation method for distinguishing and quantifying Subcutaneous and Visceral Fat from volumetric fat-water MRI images. Approach: Utilizing Fat-Water Imaging combined with 3D whole volume segmentation and morphology provides improved quantification. Results: The new method yielded more reliable and consistent fat compartment segmentation across subjects, outperforming the prior 2D segmentation techniques and showing promise for aiding the study of metabolic disorders. Impact: The study's automated 3D Fat-Water image segmentation technique aids the assessment of abdominal fat, enabling clinicians and researchers to efficiently study and evaluate metabolic disease risk and progression.