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Parameter Sensitivity Analysis in Medical Image Registration Algorithms Using Polynomial Chaos Expansions

  • Gokhan GunayEmail author
  • Sebastian van der Voort
  • Manh Ha Luu
  • Adriaan Moelker
  • Stefan Klein
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10433)

Abstract

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.

Keywords

Polynomial chaos expansion Parameter sensitivity analysis Image registration 

Notes

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.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Gokhan Gunay
    • 1
    Email author
  • Sebastian van der Voort
    • 1
  • Manh Ha Luu
    • 1
  • Adriaan Moelker
    • 2
  • Stefan Klein
    • 1
  1. 1.Biomedical Imaging Group Rotterdam, Departments of Radiology and Medical InformaticsErasmus MCRotterdamThe Netherlands
  2. 2.Department of Radiology and Nuclear MedicineErasmus University Medical CenterRotterdamThe Netherlands

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