An Investigation of Feature-based Nonrigid Image Registration using Gaussian Process

Bayer S, Spiske U, Geimer T, Luo J, Wells III WM, Ostermeier M, Fahrig R, Arya N, Bert C, Eyüpoglu IY, Maier A (2020)

Publication Language: English

Publication Type: Conference contribution

Publication year: 2020

Publisher: Springer

Conference Proceedings Title: Algorithmen – Systeme – Anwendungen. Proceedings des Workshops

Event location: Berlin

DOI: 10.1007/978-3-658-29267-6_32

Open Access Link: https://www.researchgate.net/publication/338544598_An_Investigation_of_Feature-based_Nonrigid_Image_Registration_using_Gaussian_Process

Abstract

For a wide range of clinical applications, such as adaptive treatment planning or intraoperative image update, feature-based deformable registration (FDR) approaches are widely employed because of their simplicity and low computational complexity. FDR algorithms estimate a dense displacement field by interpolating a sparse field, which is given by the established correspondence between selected features.
In this paper, we consider the deformation field as a Gaussian Process (GP), whereas the selected features are regarded as prior information on the valid deformations. Using GP, we are able to estimate the both dense displacement field and a corresponding uncertainty map at once. Furthermore, we evaluated the performance of different hyperparameter settings for squared exponential kernels with synthetic, phantom and clinical data respectively. The quantitative comparison shows, GP-based interpolation has performance on par with state-of-the-art B-spline interpolation. The greatest clinical benefit of GP-based interpolation is that it gives a reliable estimate of the mathematical uncertainty of the calculated dense displacement map.

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Involved external institutions

Brigham and Women's Hospital (BWH) United States (USA) (US) Siemens Healthineers Germany (DE) Klinikum Region Hannover Germany (DE)

How to cite

APA:

Bayer, S., Spiske, U., Geimer, T., Luo, J., Wells III, W.M., Ostermeier, M.,... Maier, A. (2020). An Investigation of Feature-based Nonrigid Image Registration using Gaussian Process. In Springer (Eds.), Algorithmen – Systeme – Anwendungen. Proceedings des Workshops. Berlin: Springer.

MLA:

Bayer, Siming, et al. "An Investigation of Feature-based Nonrigid Image Registration using Gaussian Process." Proceedings of the Bildverarbeitung für die Medizin 2020, Berlin Ed. Springer, Springer, 2020.

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