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

• URL: http://arxiv.org/abs/2204.09575v1
• Date: Wed, 20 Apr 2022 16:16:16 GMT
• Title: Fast and Robust Femur Segmentation from Computed Tomography Images for Patient-Specific Hip Fracture Risk Screening
• Authors: Pall Asgeir Bjornsson, Alexander Baker, Ingmar Fleps, Yves Pauchard, Halldor Palsson, Stephen J. Ferguson, Sigurdur Sigurdsson, Vilmundur Gudnason, Benedikt Helgason, Lotta Maria Ellingsen
• Abstract summary: We propose a deep neural network for fully automated, accurate, and fast segmentation of the proximal femur from CT. 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.
• Score: 48.46841573872642
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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