Predicting the biomechanical strength of proximal femur specimens with Minkowski functionals and support vector regression

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

doi:10.1117/12.2041782

13 March 2014 Predicting the biomechanical strength of proximal femur specimens with Minkowski functionals and support vector regression

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

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Proceedings Volume 9038, Medical Imaging 2014: Biomedical Applications in Molecular, Structural, and Functional Imaging; 903810 (2014) https://doi.org/10.1117/12.2041782
Event: SPIE Medical Imaging, 2014, San Diego, California, United States

Abstract

Regional trabecular bone quality estimation for purposes of femoral bone strength prediction is important for improving the clinical assessment of osteoporotic fracture risk. In this study, we explore the ability of 3D Minkowski Functionals derived from multi-detector computed tomography (MDCT) images of proximal femur specimens in predicting their corresponding biomechanical strength. MDCT scans were acquired for 50 proximal femur specimens harvested from human cadavers. An automated volume of interest (VOI)-fitting algorithm was used to define a consistent volume in the femoral head of each specimen. In these VOIs, the trabecular bone micro-architecture was characterized by statistical moments of its BMD distribution and by topological features derived from Minkowski Functionals. A linear multiregression analysis and a support vector regression (SVR) algorithm with a linear kernel were used to predict the failure load (FL) from the feature sets; the predicted FL was compared to the true FL determined through biomechanical testing. The prediction performance was measured by the root mean square error (RMSE) for each feature set. The best prediction result was obtained from the Minkowski Functional surface used in combination with SVR, which had the lowest prediction error (RMSE = 0.939 ± 0.345) and which was significantly lower than mean BMD (RMSE = 1.075 ± 0.279, p<0.005). Our results indicate that the biomechanical strength prediction can be significantly improved in proximal femur specimens with Minkowski Functionals extracted from on MDCT images used in conjunction with support vector regression.

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Chien-Chun Yang, Mahesh B. Nagarajan, Markus B. Huber, Julio Carballido-Gamio, Jan S. Bauer, Thomas Baum, Felix Eckstein, Eva-Maria Lochmüller, Thomas M. Link, and Axel Wismüller "Predicting the biomechanical strength of proximal femur specimens with Minkowski functionals and support vector regression", Proc. SPIE 9038, Medical Imaging 2014: Biomedical Applications in Molecular, Structural, and Functional Imaging, 903810 (13 March 2014); https://doi.org/10.1117/12.2041782

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