TY - GEN
T1 - Parameter sensitivity analysis in medical image registration algorithms using polynomial chaos expansions
AU - Gunay, Gokhan
AU - van der Voort, Sebastian
AU - Luu, Manh Ha
AU - Moelker, Adriaan
AU - Klein, Stefan
N1 - Funding Information: Acknowledgement. Ha Manh Luu was supported by ITEA project 13031, Benefit. Gokhan Gunay is supported by NWO-TTW project 13351, Medical Image Registration: Linking Algorithm and User. Publisher Copyright: © 2017, Springer International Publishing AG.
PY - 2017
Y1 - 2017
N2 - Medical image registration algorithms typically involve numerous user-defined ‘tuning’ parameters, such as regularization weights, smoothing parameters, etc. Their optimal settings depend on the anatomical regions of interest, image modalities, image acquisition settings, the expected severity of deformations, and the clinical requirements. Within a particular application, the optimal settings could even vary across the image pairs to be registered. It is, therefore, crucial to develop methods that provide insight into the effect of each tuning parameter in interaction with the other tuning parameters and allow a user to efficiently identify optimal parameter settings for a given pair of images. An exhaustive search over all possible parameter settings has obvious disadvantages in terms of computational costs and quickly becomes infeasible in practice when the number of tuning parameters increases, due to the curse of dimensionality. In this study, we propose a method based on Polynomial Chaos Expansions (PCE). PCE is a method for sensitivity analysis that approximates the model of interest (in our case the registration of a given pair of images) by a polynomial expansion which can be evaluated very efficiently. PCE renders this approach feasible for a large number of input parameters, by requiring only a modest number of function evaluations for model construction. Once the PCE has been constructed, the sensitivity of the registration results to changes in the parameters can be quantified, and the user can simulate registration results for any combination of input parameters in real-time. The proposed approach is evaluated on 8 pairs of liver CT scans and the results indicate that PCE is a promising method for parameter sensitivity analysis in medical image registration.
AB - Medical image registration algorithms typically involve numerous user-defined ‘tuning’ parameters, such as regularization weights, smoothing parameters, etc. Their optimal settings depend on the anatomical regions of interest, image modalities, image acquisition settings, the expected severity of deformations, and the clinical requirements. Within a particular application, the optimal settings could even vary across the image pairs to be registered. It is, therefore, crucial to develop methods that provide insight into the effect of each tuning parameter in interaction with the other tuning parameters and allow a user to efficiently identify optimal parameter settings for a given pair of images. An exhaustive search over all possible parameter settings has obvious disadvantages in terms of computational costs and quickly becomes infeasible in practice when the number of tuning parameters increases, due to the curse of dimensionality. In this study, we propose a method based on Polynomial Chaos Expansions (PCE). PCE is a method for sensitivity analysis that approximates the model of interest (in our case the registration of a given pair of images) by a polynomial expansion which can be evaluated very efficiently. PCE renders this approach feasible for a large number of input parameters, by requiring only a modest number of function evaluations for model construction. Once the PCE has been constructed, the sensitivity of the registration results to changes in the parameters can be quantified, and the user can simulate registration results for any combination of input parameters in real-time. The proposed approach is evaluated on 8 pairs of liver CT scans and the results indicate that PCE is a promising method for parameter sensitivity analysis in medical image registration.
KW - Image registration
KW - Parameter sensitivity analysis
KW - Polynomial chaos expansion
UR - http://www.scopus.com/inward/record.url?scp=85029395185&partnerID=8YFLogxK
U2 - https://doi.org/10.1007/978-3-319-66182-7_39
DO - https://doi.org/10.1007/978-3-319-66182-7_39
M3 - Conference contribution
SN - 9783319661810
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 335
EP - 343
BT - Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings
A2 - Descoteaux, Maxime
A2 - Duchesne, Simon
A2 - Franz, Alfred
A2 - Jannin, Pierre
A2 - Collins, D. Louis
A2 - Maier-Hein, Lena
PB - Springer - Verlag
T2 - 20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017
Y2 - 11 September 2017 through 13 September 2017
ER -