Improving bone strength prediction in human proximal femur specimens through geometrical characterization of trabecular bone microarchitecture and support vector regression

Chien-Chun Yang; Mahesh B. Nagarajan; Markus B. Huber; Julio Carballido-Gamio; Jan S. Bauer; Thomas H. Baum; Felix Eckstein; Eva-Maria Lochmüller; Sharmila Majumdar; Thomas M. Link; Axel Wismüller M.D.

doi:10.1117/1.JEI.23.1.013013

4 February 2014 Improving bone strength prediction in human proximal femur specimens through geometrical characterization of trabecular bone microarchitecture and support vector regression

Chien-Chun Yang, Mahesh B. Nagarajan, Markus B. Huber, Julio Carballido-Gamio, Jan S. Bauer, Thomas H. Baum, Felix Eckstein, Eva-Maria Lochmüller, Sharmila Majumdar, Thomas M. Link, Axel Wismüller M.D.

Author Affiliations +

Journal of Electronic Imaging, Vol. 23, Issue 1, 013013 (February 2014). https://doi.org/10.1117/1.JEI.23.1.013013

Abstract

We investigate the use of different trabecular bone descriptors and advanced machine learning techniques to complement standard bone mineral density (BMD) measures derived from dual-energy x-ray absorptiometry (DXA) for improving clinical assessment of osteoporotic fracture risk. For this purpose, volumes of interest were extracted from the head, neck, and trochanter of 146 ex vivo proximal femur specimens on multidetector computer tomography. The trabecular bone captured was characterized with (1) statistical moments of the BMD distribution, (2) geometrical features derived from the scaling index method (SIM), and (3) morphometric parameters, such as bone fraction, trabecular thickness, etc. Feature sets comprising DXA BMD and such supplemental features were used to predict the failure load (FL) of the specimens, previously determined through biomechanical testing, with multiregression and support vector regression. Prediction performance was measured by the root mean square error (RMSE); correlation with measured FL was evaluated using the coefficient of determination R ² . The best prediction performance was achieved by a combination of DXA BMD and SIM-derived geometric features derived from the femoral head (RMSE: 0.869±0.121 , R ² : 0.68±0.079 ), which was significantly better than DXA BMD alone (RMSE: 0.948±0.119 , R ² : 0.61±0.101 ) (p<10 ⁻⁴ ). For multivariate feature sets, SVR outperformed multiregression (p<0.05 ). These results suggest that supplementing standard DXA BMD measurements with sophisticated femoral trabecular bone characterization and supervised learning techniques can significantly improve biomechanical strength prediction in proximal femur specimens.

Citation Download Citation

Chien-Chun Yang, Mahesh B. Nagarajan, Markus B. Huber, Julio Carballido-Gamio, Jan S. Bauer, Thomas H. Baum, Felix Eckstein, Eva-Maria Lochmüller, Sharmila Majumdar, Thomas M. Link, and Axel Wismüller M.D. "Improving bone strength prediction in human proximal femur specimens through geometrical characterization of trabecular bone microarchitecture and support vector regression," Journal of Electronic Imaging 23(1), 013013 (4 February 2014). https://doi.org/10.1117/1.JEI.23.1.013013

Published: 4 February 2014

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available