Automated segmentation of cortical and trabecular bone to generate finite element models for femoral bone mechanics

Väänänen, Sami P.<sup>1,2,3,4</sup>; Grassi, Lorenzo<sup>5</sup>; Venäläinen, Mikko S.<sup>1,6</sup>; Matikka, Hanna<sup>2</sup>; Zheng, Yi<sup>7</sup>; Jurvelin, Jukka S.<sup>1</sup>; Isaksson, Hanna<sup>5</sup>

Affiliations

¹Department of Applied Physics, University of Eastern Finland, POB 1627, FIN-70211 Kuopio, Finland

²Department of Clinical Radiology, Diagnostic Imaging Center, Kuopio University Hospital, POB 100, 70029 Kuopio, Finland

³Department of Orthopaedics, Traumatology and Hand Surgery, Kuopio University Hospital, POB 100, FIN-70029 Kuopio, Finland

⁴Department of Medical Physics, Central Finland Central Hospital, Keskussairaalantie 19, FIN-40620 Jyväskylä, Finland

⁵Department of Biomedical Engineering, Lund University, BMC D13, 221 84 Lund, Sweden

⁶Turku Bioscience Centre, University of Turku and Åbo Akademi University, Tykistökatu 6, FIN-20520 Turku, Finland

⁷Department of Physics, Technical University of Denmark, Fysikvej, building 311, 2800 Kgs. Lyngby, Denmark

Abstract and keywords

Finite element (FE) models based on quantitative computed tomography (CT) images are better predictors of bone strength than conventional areal bone mineral density measurements. However, FE models require manual segmentation of the femur, which is not clinically applicable. This study developed a method for automated FE analyses from clinical CT images. Clinical in-vivo CT images of 13 elderly female subjects were collected to evaluate the method. Secondly, proximal cadaver femurs were harvested and imaged with clinical CT (N = 17). Of these femurs, 14 were imaged with μCT and three had earlier been tested experimentally in stance-loading, while collecting surface deformations with digital image correlation. Femurs were segmented from clinical CT images using an automated method, based on the segmentation tool Stradwin. The method automatically distinguishes trabecular and cortical bone, corrects partial volume effect and generates input for FE analysis. The manual and automatic segmentations agreed within about one voxel for in-vivo subjects (0.99 ± 0.23 mm) and cadaver femurs (0.21 ± 0.07 mm). The strains from the FE predictions closely matched with the experimentally measured strains (R2 = 0.89). The method can automatically generate meshes suitable for FE analysis. The method may bring us one step closer to enable clinical usage of patient-specific FE analyses.

Keywords: Finite element modeling; Automated segmentation; Femur; Bone; Isotopology; Surface strains

Links

DOI: 10.1016/j.medengphy.2019.06.015

PubMed: 31280927

WoS: 000519205300003

History

Received: 2018-09-20

Revised: 2019-05-16

Accepted: 2019-06-23

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Cited By (3)

Year	Entry
2020	Schileo E, Pitocchi J, Falcinelli C, Taddei F. Cortical bone mapping improves finite element strain prediction accuracy at the proximal femur. Bone. July 2020;136:115348.
2023	Grassi L, Väänänen SP, Jehpsson L, Ljunggren Ö, Rosengren BE, Karlsson MK, Isaksson H. 3D finite element models reconstructed from 2D dual-energy x-ray absorptiometry (DXA) images improve hip fracture prediction compared to areal BMD in osteoporotic fractures in men (MrOS) Sweden cohort. J Bone Miner Res. September 2023;38(9):1258-1267.
2023	Branni MG. Constitutive Models of Bone: The Human Femur [PhD thesis]. Queensland University of Technology; 2023.