title: Statistical shape and appearance models for fast and automated estimation of proximal femur fracture load using 2D finite element models.
abstract: Estimating the risk of osteoporotic fractures is an important diagnostic step that needs to be taken before medicinal treatment. Densitometry-based criteria are normally used in clinical practice for this purpose. However, densitometry-based techniques could not explain all low-energy fractures. As patient-specific finite element (FE) models allow for consideration of other parameters (e.g. load conditions) that are known to be associated with fracture, they are considered promising candidates for more accurate fracture risk estimation. Nevertheless, they are often time consuming, expensive, and complex to build and may need the type of expertise that is not normally available in clinical settings. In this study, we report the development of an automated platform for estimating proximal femur fracture loads using patient-specific 2D FE models generated using dual-energy x-ray absorptiometry (DXA) scans. First, a statistical shape and appearance model (SSAM) is built using DXA scans of patients screened for osteoporosis following a low energy fracture. SSAM is then used together with Active Appearance Models (AAM) for automated segmentation of the proximal femur from new unseen DXA scans. The mean point-to-curve error of the automated procedure, i.e. 1.2-1.4mm, is shown to be only slightly larger than the intra-observer variability of manual segmentation, i.e. 1.0mm. Moreover, the developed platform automatically meshes the segmented shape, assigns density-based mechanical properties, assigns loads and boundary conditions, submits the 2D FE model for solution, and performs post-processing of the 2D FE simulation data to determine fracture loads. The fracture loads predicted using the manually generated and automatically generated 2D FE models are shown to be very close with a mean difference of around 8.8%. Repeated measures ANOVA showed no significant differences between the fracture loads calculated using FE models manually generated by three independent observers and those calculated using the automatically generated FE models (p>0.05).
authors: Department of Biomechanical Engineering, Delft University of Technology (TU Delft), Mekelweg 2, Delft 2628 CD, The Netherlands.
authors: Nazli
authors: N
authors: Sarkalkan
authors: Department of Orthopedics, Erasmus University Medical Center, Rotterdam, The Netherlands.
authors: J H
authors: JH
authors: Waarsing
authors: Department of Orthopedics, Erasmus University Medical Center, Rotterdam, The Netherlands.
authors: P K
authors: PK
authors: Bos
authors: Department of Biomechanical Engineering, Delft University of Technology (TU Delft), Mekelweg 2, Delft 2628 CD, The Netherlands; Department of Orthopedics and Department of Rheumatology, University Medical Center Utrecht, Heidelberglaan100, 3584CX Utrecht, The Netherlands.
authors: H
authors: H
authors: Weinans
authors: Department of Biomechanical Engineering, Delft University of Technology (TU Delft), Mekelweg 2, Delft 2628 CD, The Netherlands. Electronic address: a.a.zadpoor@tudelft.nl.
authors: A A
authors: AA
authors: Zadpoor
mesh_terms: D015502:Absorptiometry, Photon; D001331:Automation; D005260:Female; D005264:Femoral Fractures / Q000000981:diagnostic imaging*; D020342:Finite Element Analysis; D006801:Humans; D008297:Male; D008875:Middle Aged; D058866:Osteoporotic Fractures / Q000000981:diagnostic imaging; D066230:Patient-Specific Modeling; D013314:Stress, Mechanical; D013997:Time Factors
keywords: Automated 2D FE modeling.; Fracture risk estimation; Osteoporotic fracture; Patient-specific 2D FE models; Statistical shape models