Keywords: Endocrine, Body, 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 diseases
Goal(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.
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