| Autor: |
Bjornsson, Pall Asgeir, Baker, Alexander, Fleps, Ingmar, Pauchard, Yves, Palsson, Halldor, Ferguson, Stephen J., Sigurdsson, Sigurdur, Gudnason, Vilmundur, Helgason, Benedikt, Ellingsen, Lotta Maria |
| Zdroj: |
Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization; March 2023, Vol. 11 Issue: 2 p253-265, 13p |
| Abstrakt: |
ABSTRACTOsteoporosis is a common bone disease that increases the risk of bone fracture. Hip-fracture risk screening methods based on flnite 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. |
| Databáze: |
Supplemental Index |
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