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

• URL: http://arxiv.org/abs/2001.05862v1
• Date: Sun, 12 Jan 2020 20:51:41 GMT
• Title: An Investigation of Feature-based Nonrigid Image Registration using Gaussian Process
• Authors: Siming Bayer, Ute Spiske, Jie Luo, Tobias Geimer, William M. Wells III, Martin Ostermeier, Rebecca Fahrig, Arya Nabavi, Christoph Bert, Ilker Eyupoglo, and Andreas Maier
• Abstract summary: We consider the deformation field as a Gaussian Process (GP) We are able to estimate the both dense displacement field and a corresponding uncertainty map at once. The greatest clinical benefit of GP-based is that it gives a reliable estimate of the mathematical uncertainty of the calculated dense displacement map.
• Score: 7.794591205048958
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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