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

Autor:	Bjornsson, Pall Asgeir, Baker, Alexander, Fleps, Ingmar, Pauchard, Yves, Palsson, Halldor, Ferguson, Stephen J., Sigurdsson, Sigurdur, Gudnason, Vilmundur, Helgason, Benedikt, Ellingsen, Lotta Maria
Rok vydání:	2022
Předmět:	Electrical Engineering and Systems Science - Image and Video Processing Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
DOI:	10.1080/21681163.2022.2068160
Popis:	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. Comment: This article has been accepted for publication in Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, published by Taylor & Francis
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2204.09575 Zobrazit plný text záznamu View this record from Arxiv