Abstract
Registration of pre-operative 3-D volumes to intra-operative 2-D X-ray images is important in minimally invasive medical procedures. Rigid registration can be performed by estimating a global rigid motion that optimizes the alignment of local correspondences. However, inaccurate correspondences challenge the registration performance. To minimize their influence, we estimate optimal weights for correspondences using PointNet. We train the network directly with the criterion to minimize the registration error. We propose an objective function which includes point-to-plane correspondence-based motion estimation and projection error computation, thereby enabling the learning of a weighting strategy that optimally fits the underlying formulation of the registration task in an end-to-end fashion. For single-vertebra registration, we achieve an accuracy of \(0.74\pm 0.26\) mm and highly improved robustness. The success rate is increased from 79.3% to 94.3% and the capture range from 3 mm to 13 mm.
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References
Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 6, 679–698 (1986)
Elliott, D.L.: A better activation function for artificial neural networks. Technical report (1993)
Feng, Y., Huang, X., Shi, L., Yang, Y., Suykens, J.A.: Learning with the maximum correntropy criterion induced losses for regression. J. Mach. Learn. Res. 16, 993–1034 (2015)
Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd edn, p. 200. Cambridge University Press, Cambridge (2003)
van de Kraats, E.B., Penney, G.P., Tomaževič, D., van Walsum, T., Niessen, W.J.: Standardized evaluation methodology for 2-D-3-D registration. IEEE Trans. Med. imaging 24(9), 1177–1189 (2005)
Kubias, A., Deinzer, F., Feldmann, T., Paulus, D., Schreiber, B., Brunner, T.: 2D/3D image registration on the GPU. Pattern Recogn. Image Anal. 18(3), 381–389 (2008)
Maier, A., et al.: Precision learning: towards use of known operators in neural networks. arXiv preprint arXiv:1712.00374v3 (2017)
Markelj, P., Tomaževič, D., Likar, B., Pernuš, F.: A review of 3D/2D registration methods for image-guided interventions. Med. Image Anal. 16(3), 642–661 (2012)
Miao, S., et al.: Dilated FCN for multi-agent 2D/3D medical image registration. In: AAAI Conference on Artificial Intelligence (AAAI), pp. 4694–4701 (2018)
Mitrović, U., Špiclin, Ž., Likar, B., Pernuš, F.: 3D–2D registration of cerebral angiograms: a method and evaluation on clinical images. IEEE Trans. Med. Imaging 32(8), 1550–1563 (2013)
Qi, C.R., Su, H., Mo, K., Guibas, L.J.: PointNet: deep learning on point sets for 3D classification and segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 77–85 (2017)
Schaffert, R., Wang, J., Fischer, P., Borsdorf, A., Maier, A.: Multi-view depth-aware rigid 2-D/3-D registration. In: IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) (2017)
Schmid, J., Chênes, C.: Segmentation of X-ray images by 3D-2D registration based on multibody physics. In: Cremers, D., Reid, I., Saito, H., Yang, M.-H. (eds.) ACCV 2014. LNCS, vol. 9004, pp. 674–687. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-16808-1_45
Wang, J., Borsdorf, A., Heigl, B., Köhler, T., Hornegger, J.: Gradient-based differential approach for 3-D motion compensation in interventional 2-D/3-D image fusion. In: International Conference on 3D Vision (3DV), pp. 293–300 (2014)
Wang, J., et al.: Dynamic 2-D/3-D rigid registration framework using point-to-plane correspondence model. IEEE Trans. Med. Imaging 36(9), 1939–1954 (2017)
Yi, K.M., Trulls, E., Ono, Y., Lepetit, V., Salzmann, M., Fua, P.: Learning to find good correspondences. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2666–2674 (2018)
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Schaffert, R., Wang, J., Fischer, P., Borsdorf, A., Maier, A. (2019). Metric-Driven Learning of Correspondence Weighting for 2-D/3-D Image Registration. In: Brox, T., Bruhn, A., Fritz, M. (eds) Pattern Recognition. GCPR 2018. Lecture Notes in Computer Science(), vol 11269. Springer, Cham. https://doi.org/10.1007/978-3-030-12939-2_11
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DOI: https://doi.org/10.1007/978-3-030-12939-2_11
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