Fast and Robust Femur Segmentation from Computed Tomography Images for Patient-Specific Hip Fracture Risk Screening
This addresses the need for automated segmentation to improve patient-specific hip-fracture risk screening in osteoporosis, representing an incremental advance over manual methods.
The paper tackles the problem of automating femur segmentation from CT images for hip-fracture risk screening, proposing a deep neural network that achieves accurate and fast segmentation on a dataset of 1147 proximal femurs.
Osteoporosis is a common bone disease that increases the risk of bone fracture. Hip-fracture risk screening methods based on finite element analysis depend on segmented computed tomography (CT) images; however, current femur segmentation methods require manual delineations of large data sets. Here we propose a deep neural network for fully automated, accurate, and fast segmentation of the proximal femur from CT. Evaluation on a set of 1147 proximal femurs with ground truth segmentations demonstrates that our method is apt for hip-fracture risk screening, bringing us one step closer to a clinically viable option for screening at-risk patients for hip-fracture susceptibility.