The DOI matches its Crossref record; 26 of the 30 references it cites were found in a registry or an index.
Dilated FCN for Multi-Agent 2D/3D Medical Image Registration
Open accessAbstract
2D/3D image registration to align a 3D volume and 2D X-ray images is a challenging problem due to its ill-posed nature and various artifacts presented in 2D X-ray images. In this paper, we propose a multi-agent system with an auto attention mechanism for robust and efficient 2D/3D image registration. Specifically, an individual agent is trained with dilated Fully Convolutional Network (FCN) to perform registration in a Markov Decision Process (MDP) by observing a local region, and the final action is then taken based on the proposals from multiple agents and weighted by their corresponding confidence levels. The contributions of this paper are threefold. First, we formulate 2D/3D registration as a MDP with observations, actions, and rewards properly defined with respect to X-ray imaging systems. Second, to handle various artifacts in 2D X-ray images, multiple local agents are employed efficiently via FCN-based structures, and an auto attention mechanism is proposed to favor the proposals from regions with more reliable visual cues. Third, a dilated FCN-based training mechanism is proposed to significantly reduce the Degree of Freedom in the simulation of registration environment, and drastically improve training efficiency by an order of magnitude compared to standard CNN-based training method. We demonstrate that the proposed method achieves high robustness on both spine cone beam Computed Tomography data with a low signal-to-noise ratio and data from minimally invasive spine surgery where severe image artifacts and occlusions are presented due to metal screws and guide wires, outperforming other state-of-the-art methods (single agent-based and optimization-based) by a large margin.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Indeed, among the seven studies that trained deep CNNs on synthetic data and evaluated in a way that allowed for comparisons between synthetic and real data performance, we found quite substantial performance drops (Miao et al., 2018; Bier et al., 2019; Gao et al., 2020c; Doerr et al., 2020; Gu et al., 2020; Guan et al., 2020; Li et al., 2020).”
From The Impact of Machine Learning on 2D/3D Registration for Image-Guided Interventions: A Systematic Review and Perspective (2021) · cites this paper as Miao et al., 2018
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
A fast and elitist multiobjective genetic algorithm: NSGA-II
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002) · IEEE Transactions on Evolutionary Computation · doi:10.1109/4235.996017 · Cited by 50,053
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834
Multimodality image registration by maximization of mutual information
Maes, F., Collignon, A., Vandermeulen, D., Marchal, G., & Suetens, P. (1997) · IEEE Transactions on Medical Imaging · doi:10.1109/42.563664 · Cited by 4,547
Flownet: Learning optical flow with convolutional networks
Dosovitskiy, A., Fischer, P., Ilg, E., Hausser, P., Hazirbas, C., Golkov, V., Smagt, P. V. D., Cremers, D., & Brox, T. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.316 · Cited by 4,313
Self-citationFlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
Ilg, E., Mayer, N., Saikia, T., Keuper, M., Dosovitskiy, A., & Brox, T. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.179 · Cited by 3,177
Fully Convolutional Networks for Semantic SegmentationPreprint
Long, J., Shelhamer, E., & Darrell, T. J. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1411.4038 · Cited by 2,490
The Essential Physics of Medical Imaging
Bushberg, J. T., Seibert, J. A., Leidholdt, E. M., Boone, J. M., & Goldschmidt, E. J. (2003) · Medical Physics · doi:10.1118/1.1585033 · Cited by 2,088
A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II
Deb, K. (2002) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.6487416 · Cited by 1,552
DeepFlow: Large Displacement Optical Flow with Deep Matching
Weinzaepfel, P., Revaud, J., Harchaoui, Z., & Schmid, C. (2013) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.175 · Cited by 1,057
Multi-modality image registration by maximization of mutual information
Maes, F., Collignon, A. M. F., Vandermeulen, D., Marchal, G., & Suetens, P. (1996) · IEEE Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA) · doi:10.1109/mmbia.1996.534053 · Cited by 809
Flagged first, then by citations
4 of the 30 references couldn’t be matched to a known paper.
Research works printed but not linked: 3
- Jaderberg, M.; Simonyan, K.; Zisserman, A.; et al. 2015. Spatial transformer networks. In Advances in Neural Infor- mation Processing Systems, 2017-2025.
- Kruger, J., and Westermann, R. 2003. Acceleration tech- niques for gpu-based volume rendering. In Proceedings of the 14th IEEE Visualization 2003 (VIS'03), 38. IEEE Com- puter Society.
- Van Laarhoven, P. J., and Aarts, E. H. 1987. Simulated an- nealing. In Simulated annealing: Theory and applications. Springer. 7-15.
Cited by
Sorted by citations
Items 101–113 of 113
3D shape instantiation for intra-operative navigation from a single 2D projection
Zhou, X.-Y. (2019) · Spiral (Imperial College London) · doi:10.25560/80269 · Cited by 1
Robust medical image registration and motion modeling based on machine learning
Krebs, J. (2020) · doi:10.70675/37c1e63dz3235z4166zaab5za0916ff7856b · Cited by 1
Liao, H., Lin, W.-A., Zhang, J., Zhang, J., Luo, J., & Zhou, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1903.03896
A Rigid Registration Method in TEVARPreprint
Li, M., Lin, C., Wu, H. Y., Li, J., & Cao, H. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.14273
Crossmodal Matching Transformer based X-ray and CT image registration for TEVAR
Li, M., Lin, C., Shu, L., Pu, X., Chen, Y., Wu, H. Y., Li, J., & Cao, H. (2021) · International Conference on Biomedical Signal and Image Processing (ICBIP) · doi:10.1145/3484424.3484438
A Novel Two-Stage Framework for 2D/3D Registration in Neurological Interventions
Huang, D.-X., Zhou, X.-H., Xie, X., Liu, S.-Q., Feng, Z.-Q., Hao, J., Hou, Z., Ma, N., & Yan, L. (2022) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio55434.2022.10011812
A Self-supervised 3D/2D Registration Method for Incomplete DSA Vessels
Xu, Y., Cai, M., Li, Y., Li, N., Ren, L., & Xia, K. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-25191-7_2
CT-X-Ray Registration Via Spatial-Projective Dual Transformer Network Fused With Target Detection
Zhang, Z., Ai, D., Geng, H., & Yang, J. (2022) · International Conference on Bioinformatics and Biomedical Science (ICBBS) · doi:10.1145/3571532.3571546
Reliable 2D–3D Registration in Dynamic Stereo-Radiography With Energy Barrier Constraints
Burton, W. S., Myers, C. A., Clary, C. W., & Rullkoetter, P. J. (2024) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2024.3522200
Masked Registration and Autoencoding of CT Images for Predictive Tibia Reconstruction
Zhou, H., Aßmann, C., Bejaoui, A., Tzschätzsch, H., Heyland, M., Zierke, J. N., Tuttle, N., Hölzl, S., Auer, T. A., Back, D. A., et al. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-05472-2_11
MF-PEAR-net: a multi-scale feature guided project-&-excite affine registration network for CT images
Bbosa, R., Liu, F., Efio-Akolly, K., Salem, O. A. M., & Chen, Y. P. (2026) · Multimedia Systems · doi:10.1007/s00530-025-02194-6
Modelling C-arm fluoroscopy and operating table kinematics via machine learning
Jaheen, F., Gutta, V., & Fallavollita, P. (2026) · Frontiers in Robotics and AI · doi:10.3389/frobt.2025.1691576
Artificial Intelligence in Orthodontics: Part 1—Basic Concepts
Sohrabniya, F., Naveed, K., Mohammad‐Rahimi, H., Woolley, J., Haghighat, S., Stoustrup, P. B., & Pauwels, R. (2026) · Orthodontics and Craniofacial Research · doi:10.1111/ocr.70179
Metadata
- Authors
- Miao, S., Piat, S., Fischer, P., Tuysuzoglu, A., Mewes, P., Mansi, T., & Liao, R.
- Affiliations
- Siemens Healthcare (United States) (US)
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2018-04-26
- Volume
- 32
- Issue
- 1
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W2962759210
- Cited by
- 113
- References
- 30
- Citations by year
- 2026: 3 · 2025: 5 · 2024: 12 · 2023: 18 · 2022: 17 · 2021: 29 · 2020: 21 · 2019: 6 · 2018: 2
- FWCI
- 4.37
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Proceedings of the AAAI Conference on Artificial Intelligence (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Engineering › Biomedical Engineering › Medical Imaging and Analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- 2D-3D registration, multi-agent reinforcement learning, Markov decision process, X-ray images, cone-beam computed tomography, spinal surgery, metal artifact reduction, occlusion handling, minimally invasive spine surgery, low signal-to-noise ratio
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
