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Abstract #4313

Segmentation model quantifying bone marrow fat on whole-body Dixon MRI reveals association between vertebral fat and diabetes

Shengqian Huang1, Qin Wang1, Fuze Cong1, Jinxia Zhu2, Zhengyu Jin1, and Huadan Xue1
1Department of Radiology, Peking Union Medical College Hospital, Beijing, China, 2MR Research Collaboration, Siemens Healthineers, Beijing, China

Synopsis

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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