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

Väänänen, Sami P; Grassi, Lorenzo LU ; Venäläinen, Mikko S; Matikka, Hanna; Zheng, Yi; Jurvelin, Jukka S and Isaksson, Hanna LU (2019) In Medical Engineering & Physics 70(August 2019). p.19-28
Abstract

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... (More)

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.

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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Medical Engineering & Physics
volume
70
issue
August 2019
pages
19 - 28
publisher
Elsevier
external identifiers
  • scopus:85068391239
ISSN
1873-4030
DOI
10.1016/j.medengphy.2019.06.015
language
English
LU publication?
yes
id
ebc62e66-1af2-4a3d-a305-bb94d8f18494
date added to LUP
2019-07-14 12:41:28
date last changed
2019-08-06 03:24:25
@article{ebc62e66-1af2-4a3d-a305-bb94d8f18494,
  abstract     = {<p>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.</p>},
  author       = {Väänänen, Sami P and Grassi, Lorenzo and Venäläinen, Mikko S and Matikka, Hanna and Zheng, Yi and Jurvelin, Jukka S and Isaksson, Hanna},
  issn         = {1873-4030},
  language     = {eng},
  month        = {07},
  number       = {August 2019},
  pages        = {19--28},
  publisher    = {Elsevier},
  series       = {Medical Engineering & Physics},
  title        = {Automated segmentation of cortical and trabecular bone to generate finite element models for femoral bone mechanics},
  url          = {http://dx.doi.org/10.1016/j.medengphy.2019.06.015},
  volume       = {70},
  year         = {2019},
}