Metric-Driven Learning of Correspondence Weighting for 2-D/3-D Image Registration
Schaffert R, Wang J, Fischer P, Borsdorf A, Maier A (2019)
Publication Type: Conference contribution
Publication year: 2019
Journal
Publisher: Springer Verlag
Book Volume: 11269 LNCS
Pages Range: 140-152
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Stuttgart
ISBN: 9783030129385
DOI: 10.1007/978-3-030-12939-2_11
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 ± 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.
Authors with CRIS profile
Roman Schaffert
Lehrstuhl für Informatik 14 (Bild- und Sprachverarbeitung)
Andreas Maier
Lehrstuhl für Informatik 5 (Mustererkennung)
How to cite
APA:
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 Thomas Brox, Mario Fritz, Andrés Bruhn (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 140-152). Stuttgart, DE: Springer Verlag.
MLA:
Schaffert, Roman, et al. "Metric-Driven Learning of Correspondence Weighting for 2-D/3-D Image Registration." Proceedings of the 40th German Conference on Pattern Recognition, GCPR 2018, Stuttgart Ed. Thomas Brox, Mario Fritz, Andrés Bruhn, Springer Verlag, 2019. 140-152.
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