Keywords: Segmentation, Whole Body, bone marrow fat fraction, Dixon MRI
Motivation: Robust assessment of the fat distribution in normal and abnormal bone marrow on whole-body Dixon MRI images is difficult.
Goal(s): Our goal was to develop a deep learning model to segment and quantify whole-body bone marrow and evaluate its value in a community-based study.
Approach: A three-dimensional nnU-Net model was trained with diverse bone marrow changes, tested on internal and external test sets, and applied to images from community-based populations.
Results: The model precisely segmented bone marrow and determined fat fractions, thus confirming the correlation between vertebral body BMFF and diabetes.
Impact: Our novel three-dimensional nnU-Net model for automated assessment of whole-body bone marrow fat sheds new light on the link between bone marrow adiposity and diabetes.
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