Abstract #3487

Fully Automatic Proximal Femur Segmentation in MR Images using 3D Convolutional Neural Networks

Siyuan Xiang¹, Gregory Chang², Stephen Honig³, Kyunghyun Cho⁴, and Cem M. Deniz^5,6

¹Center for Data Science, New York University, New York, NY, United States, ²Department of Radiology, Center for Musculoskeletal Care, New York University Langone Medical Center, New York, NY, United States, ³Osteoporosis Center, Hospital for Joint Diseases, New York University Langone Medical Center, New York, NY, United States, ⁴Courant Institute of Mathematical Science & Center for Data Science, New York University, New York, NY, United States, ⁵Department of Radiology, Center for Advanced Imaging Innovation and Research (CAI2R) and Bernard and Irene Schwartz Center for Biomedical Imaging, New York University Langone Medical Center, New York, NY, United States, ⁶The Sackler Institute of Graduate Biomedical Sciences, New York University School of Medicine, New York, NY, United States

MRI has been successfully used in structural imaging of trabecular bone micro architecture in vivo. In this project, we develop supervised convolutional neural network for automatically segmental proximal femur from structural MR images. We found that the proposed method provides accurate segmentation without any post-processing, bringing trabecular bone micro architecture analysis closer to clinical practice.

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