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

doi:10.1080/21681163.2022.2068160

arXiv · 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

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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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Pall Asgeir Bjornsson, Alexander Baker, Ingmar Fleps, Yves Pauchard, Halldor Palsson, Stephen J. Ferguson, Sigurdur Sigurdsson, Vilmundur Gudnason, Benedikt Helgason, Lotta Maria Ellingsen. 2022-04-20. Fast and Robust Femur Segmentation from Computed Tomography Images for Patient-Specific Hip Fracture Risk Screening. https://doi.org/10.1080/21681163.2022.2068160

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