Abstract #2668

Deep learning based fully automated screening pipeline for abnormal bone density using a short lumbar Dixon sequence

Shenglan Chen¹, Yinxia Zhao², Xintao Zhang², Tianyun Zhao¹, Mario Serrano-Sosa¹, Xiaodong Zhang², and Chuan Huang^1,3,4

¹Biomedical Engineering, Stony Brook University, Stony Brook, NY, United States, ²Radiology, The Third Affiliated Hospital of Southern Medical University (Orthopaedic Hospital of Guangdong Province), Guangzhou, China, ³Radiology, Stony Brook Medicine, Stony Brook, NY, United States, ⁴Psychiatry, Stony Brook Medicine, Stony Brook, NY, United States

Bone marrow fat fraction (BMFF) has been recognized as one of the quantitative image biomarkers to identify abnormal bone density using modified Dixon sequence. However, this method requires manual segmentation which limits its adoption in clinical practice. In this study, we developed a fully automated radiomics pipeline using deep learning based segmentation and validated its performance comparable to manual segmentation. This finding will facilitate the clinical utility of the entire pipeline as a screening tool for early detection of abnormal bone density.

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