Zusammenfassung
In many minimally invasive procedures, image guidance using a C-arm system is utilized. To enhance the guidance, information from pre-operative 3-D images can be overlaid on top of the 2-D fluoroscopy and 2-D/3-D image registration techniques are used to ensure an accurate overlay. Despite decades of research, achieving a highly reliable registration remains challenging. In this paper, we propose a learning-based correspondence estimation, which focuses on contour points and can be used in combination with the point-to-plane correspondence model-based registration. When combined with classical correspondence estimation in a refinement step, the method highly increases the robustness, leading to a capture range of 36mm and a success rate of 98.5%, compared to 14mm and 71.9% for the purely classical approach, while maintaining a high accuracy of 0.430.08mm of mean re-projection distance.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Literatur
Markelj P, Tomaževič D, Likar B, et al. A review of 3D/2D registration methods for image-guided interventions. Med Image Anal. 2010;16(3):642–661.
Maier A, Syben C, Lasser T, et al. A gentle introduction to deep learning in medical image processing. Zeitschrift für Medizinische Physik. 2019;29(2):86–101.
Miao S, Piat S, Fischer P, et al. Dilated FCN for multi-agent 2D/3D medical image registration. In: AAAI; 2018. p. 4694–4701.
Liao H, Lin WA, Zhang J, et al. Multiview 2D/3D rigid registration via a pointof-interest network for tracking and triangulation (POINT2). arXiv:190303896v3;.
Wang J, Schaffert R, Borsdorf A, et al. Dynamic 2-D/3-D rigid registration framework using point-to-plane correspondence model. IEEE Trans Med Imaging. 2017;36(9):1939–1954.
Schaffert R, Wang J, Fischer P, et al. Metric-Driven learning of correspondence weighting for 2-D/3-D image registration. In: GCPR; 2018. p. 140–152.
Wang J. Robust 2-D/3D registration for real-time patient motion compensation. FAU Erlangen-Nürnberg; to appear 2020.
Dosovitskiy A, Fischer P, Ilg E, et al. Flownet: learning optical flow with convolutional networks. In: IEEE ICCV; 2015. p. 2758–2766.
van de Kraats EB, Penney GP, Tomaževič D, et al. Standardized evaluation methodology for 2-D-3-D registration. IEEE Trans Med Imaging. 2005;24(9).
Mitrovič U, Špiclin Ž, Likar B, et al. 3D-2D registration of cerebral angiograms: a method and evaluation on clinical images. IEEE Trans Med Imaging. 2013;32(8):1550–1563.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature
About this paper
Cite this paper
Schaffert, R., Weiß, M., Wang, J., Borsdorf, A., Maier, A. (2020). Learning-Based Correspondence Estimation for 2-D/3-D Registration. In: Tolxdorff, T., Deserno, T., Handels, H., Maier, A., Maier-Hein, K., Palm, C. (eds) Bildverarbeitung für die Medizin 2020. Informatik aktuell. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-29267-6_50
Download citation
DOI: https://doi.org/10.1007/978-3-658-29267-6_50
Published:
Publisher Name: Springer Vieweg, Wiesbaden
Print ISBN: 978-3-658-29266-9
Online ISBN: 978-3-658-29267-6
eBook Packages: Computer Science and Engineering (German Language)