eprintid: 2601
rev_number: 9
eprint_status: archive
userid: 366
dir: disk0/00/00/26/01
datestamp: 2017-11-01 21:25:33
lastmod: 2017-11-01 21:25:33
status_changed: 2017-11-01 21:25:33
type: conference_item
metadata_visibility: show
creators_name: Gunay, Gokhan
creators_name: van der Voort, Sebastian
creators_name: Luu, Manh Ha
creators_name: Moelker, Adriaan
creators_name: Klein, Stefan
creators_id: g.gunay@erasmusmc.nl
creators_id: s.vandervoort@erasmusmc.nl
creators_id: halm@vnu.edu.vn
creators_id: a.moelker@erasmusmc.nl
creators_id: s.klein@erasmusmc.nl
title: Parameter Sensitivity Analysis in Medical Image Registration Algorithms Using Polynomial Chaos Expansions
ispublished: pub
subjects: ElectronicsandComputerEngineering
divisions: fac_fet
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.
date: 2017
date_type: published
publisher: Springer International Publishing
official_url: https://doi.org/10.1007/978-3-319-66182-7_39
full_text_status: none
pres_type: paper
place_of_pub: Cham
pagerange: 335-343
event_title: International Conference on Medical Image Computing and Computer-Assisted Intervention 2017
event_type: conference
refereed: TRUE
citation:   Gunay, Gokhan and van der Voort, Sebastian and Luu, Manh Ha and Moelker, Adriaan and Klein, Stefan  (2017) Parameter Sensitivity Analysis in Medical Image Registration Algorithms Using Polynomial Chaos Expansions.  In: International Conference on Medical Image Computing and Computer-Assisted Intervention 2017.