Fast and robust femur segmentation from computed tomography images for patient-specific hip fracture risk screening. Issue 2 (4th March 2023)

• Record Type: Journal Article
• Authors: Bjornsson, Pall Asgeir; Baker, Alexander; Fleps, Ingmar; Pauchard, Yves; Palsson, Halldor; Ferguson, Stephen J.; Sigurdsson, Sigurdur; Gudnason, Vilmundur; Helgason, Benedikt; Ellingsen, Lotta Maria
• Is Part Of: Computer methods in biomechanics and biomedical engineering. Volume 11:Issue 2(2023)
• Page Start: 253
• Page End: 265
• Publication Date: 2023-03-04
• Subjects: Computed tomography -- femur -- segmentation -- convolutional neural networks -- osteoporosis; Imaging systems in biology -- Periodicals; Imaging systems in medicine -- Periodicals; Biomechanics -- Data processing -- Periodicals; Biomedical engineering -- Periodicals; 616.0757
• DOI: 10.1080/21681163.2022.2068160
• Languages: English
• ISSNs: 2168-1163

Abstract

ABSTRACT: Osteoporosis 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.