Paper ID: 2204.09575

Fast and Robust Femur Segmentation from Computed Tomography Images for Patient-Specific Hip Fracture Risk Screening

Pall Asgeir Bjornsson, Alexander Baker, Ingmar Fleps, Yves Pauchard, Halldor Palsson, Stephen J. Ferguson, Sigurdur Sigurdsson, Vilmundur Gudnason, Benedikt Helgason, Lotta Maria Ellingsen

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.

Submitted: Apr 20, 2022