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Cited by 21Medical Image Segmentation Techniques3D Shape Modeling and AnalysisAdvanced Radiotherapy Techniques

Deep Iterative 2D/3D Registration

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Jaganathan, Srikrishna, Wang, Jian, Borsdorf, Anja, Shetty, Karthik, Maier, Andreas·2021·Lecture Notes in Computer Science
DOI 10.1007/978-3-030-87202-1_37
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  • Recent approaches train neural networks to segment intraoperative X-rays and localize predefined landmarks from segmentation masks.

    “Recent approaches train neural networks to segment intraoperative X-rays and localize predefined landmarks from the segmentation masks [17, 25, 27, 37, 39].”

    From Intraoperative 2D/3D Image Registration via Differentiable X-Ray Rendering (2024) · cites this paper as [27]

    Section 2.1 (p. 2) describes a deep-learning registration framework that takes a fluoroscopic X-ray image as input and estimates correspondences. It does not describe segmenting the X-ray or localizing predefined landmarks from segmentation masks; the mask in Section 2.3 (p. 5) is used to calculate optical-flow loss at projected contour points. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

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Research works printed but not linked: 18

  1. [1]1. Dosovitskiy, A., Fischer, P., Ilg, E., Hausser, P., Hazirbas, C., Golkov, V., Van Der Smagt, P., Cremers, D., Brox, T.: Flownet: Learning optical flow with convolutional networks. In: Proceedings of the IEEE international conference on computer vision. pp. 2758-2766 (2015)
  2. [3]3. Grimm, M., Esteban, J., Unberath, M., Navab, N.: Pose-dependent weights and domain randomization for fully automatic x-ray to ct registration. arXiv preprint arXiv:2011.07294 (2020) · arXiv
  3. [4]4. Hur, J., Roth, S.: Iterative residual refinement for joint optical flow and occlusion estimation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 5754-5763 (2019)
  4. [5]5. Jaganathan, S., Wang, J., Borsdorf, A., Maier, A.: Learning the update operator for 2d/3d image registration. In: Bildverarbeitung f¨ ur die Medizin 2021. pp. 117-122. Springer Fachmedien Wiesbaden, Wiesbaden (2021)
  5. [6]6. Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014) · arXiv
  6. [7]7. Van de Kraats, E.B., Penney, G.P., Tomaˇ zeviˇ c, D., van Walsum, T., Niessen, W.J.: Standardized evaluation of 2d-3d registration. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 574-581. Springer (2004)
  7. [8]8. Liao, H., Lin, W.A., Zhang, J., Zhang, J., Luo, J., Zhou, S.K.: Multiview 2d/3d rigid registration via a point-of-interest network for tracking and triangulation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 12638-12647 (2019)
  8. [9]9. Markelj, P., Tomaˇ zeviˇ c, D., Likar, B., Pernuˇ s, F.: A review of 3d/2d registration methods for image-guided interventions. Medical image analysis 16 (3), 642-661 (2012)
  9. [11]11. Miao, S., Wang, Z.J., Zheng, Y., Liao, R.: Real-time 2d/3d registration via cnn regression. In: 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI). pp. 1430-1434. IEEE (2016)
  10. [13]13. Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: An imperative style, highperformance deep learning library. arXiv preprint arXiv:1912.01703 (2019) · arXiv
  11. [15]15. Qi, C.R., Su, H., Mo, K., Guibas, L.J.: Pointnet: Deep learning on point sets for 3d classification and segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 652-660 (2017)
  12. [16]16. Qi, C.R., Yi, L., Su, H., Guibas, L.J.: Pointnet++: Deep hierarchical feature learning on point sets in a metric space. arXiv preprint arXiv:1706.02413 (2017) · arXiv
  13. [17]17. Schaffert, R., Wang, J., Fischer, P., Borsdorf, A., Maier, A.: Learning an attention model for robust 2-d/3-d registration using point-to-plane correspondences. IEEE transactions on medical imaging 39 (10), 3159-3174 (2020)
  14. [18]18. Schaffert, R., Wang, J., Fischer, P., Maier, A., Borsdorf, A.: Robust multi-view 2d/3-d registration using point-to-plane correspondence model. IEEE transactions on medical imaging 39 (1), 161-174 (2019)
  15. [19]19. Schaffert, R., Weiß, M., Wang, J., Borsdorf, A., Maier, A.: Learning-based correspondence estimation for 2-d/3-d registration. In: Bildverarbeitung f¨ ur die Medizin 2020, pp. 222-228. Springer (2020)
  16. [20]20. Teed, Z., Deng, J.: Raft: Recurrent all-pairs field transforms for optical flow. In: European Conference on Computer Vision. pp. 402-419. Springer (2020)
  17. [22]22. Wang, J., Schaffert, R., Borsdorf, A., Heigl, B., Huang, X., Hornegger, J., Maier, A.: Dynamic 2-d/3-d rigid registration framework using point-to-plane correspondence model. IEEE transactions on medical imaging 36 (9), 1939-1954 (2017)
  18. [23]23. Zhong, Z., Zheng, L., Kang, G., Li, S., Yang, Y.: Random erasing data augmentation. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 34, pp. 13001-13008 (2020)

Cited by

Sorted by citations

Authors
Jaganathan, S., Wang, J., Borsdorf, A., Shetty, K., & Maier, A.
Affiliations
  • Friedrich-Alexander-Universität Erlangen-Nürnberg (DE)
  • Siemens Healthineers (Germany) (DE)
Corresponding author
Jaganathan, Srikrishna
ORCID
Maier, Andreas
Venue
Lecture Notes in Computer Science
Publisher
Springer International Publishing
Published
2021
Pages
383-392
ISSN
  • 0302-9743 (ISSN-L)
  • 1611-3349
DOI
10.1007/978-3-030-87202-1_37
OpenAlex
W3186060996
Cited by
21
References
3
Citations by year
2026: 2 · 2025: 7 · 2024: 4 · 2023: 4 · 2022: 4
FWCI
0.98
Type
conference paper
Language
en
Open access
Licence
Locations
  • Lecture notes in computer science (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
  • Physical Sciences › Engineering › Computational Mechanics › 3D Shape Modeling and Analysis
  • Physical Sciences › Physics and Astronomy › Radiation › Advanced Radiotherapy Techniques
Keywords
2D-3D registration, iterative registration, optical flow, residual refinement, end-to-end learning, registration accuracy, reprojection error

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