Advertisement

Investigation of Feature-Based Nonrigid Image Registration Using Gaussian Process

  • Siming BayerEmail author
  • Ute Spiske
  • Jie Luo
  • Tobias Geimer
  • William M. Wells III
  • Martin Ostermeier
  • Rebecca Fahrig
  • Arya Nabavi
  • Christoph Bert
  • Ilker Eyüpoglo
  • Andreas Maier
Conference paper
  • 49 Downloads
Part of the Informatik aktuell book series (INFORMAT)

Zusammenfassung

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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Literatur

  1. 1.
    Rueckert D, Sonoda LI, Hayes C, et al. Nonrigid registration using free-form deformations: application to breast MR images. IEEE Trans Med Imaging. 1999 Aug;18(8):712-721.Google Scholar
  2. 2.
    Bayer S, Zhai Z, Strumia M, et al. Registration of vascular structures using a hybrid mixture model. Int J Comput Assist Radiol Surg. 2019 June;14.Google Scholar
  3. 3.
    Bookstein FL. Principal warps: thin-plate splines and the decomposition of deformations. IEEE Trans Pattern Anal Mach Intell. 1989 Jun;11(6):567-585.Google Scholar
  4. 4.
    Reinertsen I, Descoteaux M, Siddiqi K, et al. Validation of vessel-based registration for correction of brain shift. Med Img Anal. 2007;11(4):374–388.Google Scholar
  5. 5.
    Rasmussen CE, Williams CKI. Gaussian processes for machine learning. MIT Press; 2006.Google Scholar
  6. 6.
    Wachinger C, Golland P, Reuter M, et al. Gaussian process interpolation for uncertainty estimation in image registration. In: Proc MICCAI; 2014. p. 267–274.Google Scholar
  7. 7.
    Luo J, Toews M, Machado I, et al. A Feature-Driven active framework for Ultrasound-Based brain shift compensation. In: Proc MICCAI; 2018. p. 30–38.Google Scholar
  8. 8.
    Kallis K, Kreppner S, Lotter M, et al. Introduction of a hybrid treatment delivery system used for quality assurance in multi-catheter interstitial brachytherapy. Phys Med Biol. 2018 may;63(9).Google Scholar
  9. 9.
    Bishop CM. Pattern recognition and machine learning. Berlin: Springer; 2006.Google Scholar
  10. 10.
    Bayer S, Maier A, Ostermeier M, et al. Generation of synthetic image data for the evaluation of brain shift compensation methods. In: Proc CIGI; 2017. p. 10.Google Scholar
  11. 11.
    Bayer S, Wydra A, Zhong X, et al. An anthropomorphic deformable phantom for brain shift simulation. In: IEEE Nucl Sci Symp Conf Rec; 2018. p. 1–3.Google Scholar

Copyright information

© Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2020

Authors and Affiliations

  • Siming Bayer
    • 1
    Email author
  • Ute Spiske
    • 1
  • Jie Luo
    • 2
  • Tobias Geimer
    • 1
  • William M. Wells III
    • 2
  • Martin Ostermeier
    • 3
  • Rebecca Fahrig
    • 3
  • Arya Nabavi
    • 4
  • Christoph Bert
    • 5
  • Ilker Eyüpoglo
    • 6
  • Andreas Maier
    • 1
  1. 1.Pattern Recognition LabFAU Erlangen-NürnbergErlangen-NürnbergDeutschland
  2. 2.Brigham and Women’s HospitalHarvard Medical SchoolBostonUSA
  3. 3.Advanced TherapiesSiemens Healthare GmbHForchheimDeutschland
  4. 4.Department of NeurosurgeryKRH Klinikum NordstadtHannoverDeutschland
  5. 5.Department of Radiation TherapyUniversität Klinikum ErlangenErlangenDeutschland
  6. 6.Department of NeurosurgeryUniverisät Klinikum Erlangen,ErlangenDeutschland

Personalised recommendations