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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.
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“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
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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
A Review of 3d/2d Registration Methods for Image-Guided Interventions
Markelj, P., Tomaževič, D., Likar, B., & Pernuš, F. (2012) · Medical Image Analysis · doi:10.1016/j.media.2010.03.005 · Cited by 746
A CNN Regression Approach for Real-Time 2D/3D Registration
Miao, S., Wang, Z. J., & Liao, R. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2521800 · Cited by 514
Self-citationLearning descriptors for object recognition and 3D pose estimation
Wohlhart, P., & Lepetit, V. (2015) · 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.2015.7298930 · Cited by 458
An artificial agent for robust image registration
Liao, R., Miao, S., De Tournemire, P., Grbic, S., Kamen, A., Mansi, T., & Comaniciu, D. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.11230 · Cited by 199
Self-citationL1 rotation averaging using the Weiszfeld algorithm
Hartley, R. I., Aftab, K., & Trumpf, J. (2011) · doi:10.1109/cvpr.2011.5995745 · Cited by 179
De Silva, T., Uneri, A., Ketcha, M. D., Reaungamornrat, S., Kleinszig, G., Vogt, S., Aygun, N., Lo, S.-F., Wolinsky, J.-P., & Siewerdsen, J. H. (2016) · Physics in Medicine & Biology · doi:10.1088/0031-9155/61/8/3009 · Cited by 107
Learning-based deformable registration of MR brain images
Wu, G., Qi, F., & Shen, D. G. (2006) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2006.879320 · Cited by 106
Monitoring tumor motion by real time 2D/3D registration during radiotherapy
Gendrin, C., Furtado, H., Weber, C., Bloch, C., Figl, M., Pawiro, S. A., Bergmann, H., Stock, M., Gabor Fichtinger, A., Georg, D., et al. (2011) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2011.07.031 · Cited by 87
2D/3D image registration using regression learning
Chou, C.-R., Frederick, C. B., Mageras, G. S., Chang, S., & Pizer, S. M. (2013) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2013.02.009 · Cited by 62
Effective Intensity-Based 2D/3D Rigid Registration between Fluoroscopic X-Ray and CT
Knaan, D., & Joskowicz, L. (2003) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-39899-8_44 · Cited by 43
Uneri, A., Silva, T. D., Goerres, J., Jacobson, M. W., Ketcha, M. D., Reaungamornrat, S., Kleinszig, G., Vogt, S. K., Khanna, A. J., Osgood, G. M., et al. (2017) · Physics in Medicine and Biology · doi:10.1088/1361-6560/aa62c5 · Cited by 42
Registration-by-regression of coronary CTA and X-ray angiography
Gouveia, A., Metz, C., Freire, L., Almeida, P., & Klein, S. (2017) · Comput. methods Biomech. Biomed. Eng. Imaging Vis. · doi:10.1080/21681163.2015.1054520 · Cited by 23
Miao, S., Liao, R., & Zheng, Y. (2011) · IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI) · doi:10.1109/isbi.2011.5872620 · Cited by 22
Self-citationSegmentation of X-ray Images by 3D-2D Registration Based on Multibody Physics
Schmid, J., & Chênes, C. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-16808-1_45 · Cited by 22
Zheng, J., Miao, S., & Liao, R. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-66185-8_73 · Cited by 21
Self-citationA global optimization strategy for 3D-2D registration of vascular images
Lau, K. K., & Chung, A. C. S. (2006) · British Machine Vision Conference (BMVC) · doi:10.5244/c.20.51 · Cited by 12
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
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Image Matching from Handcrafted to Deep Features: A Survey
Ma, J.-Y., Jiang, X., Fan, A., Jiang, J., & Yan, J. (2020) · International Journal of Computer Vision · doi:10.1007/s11263-020-01359-2 · Cited by 977
Deep learning in medical image registration: a review
Fu, Y., Lei, Y., Wang, T., Curran, W. J., Liu, T., & Yang, X. (2020) · Physics in Medicine & Biology · doi:10.1088/1361-6560/ab843e · Cited by 661
A review of multimodal image matching: Methods and applications
Jiang, X., Ma, J., Xiao, G., Shao, Z., & Guo, X. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.02.012 · Cited by 561
Artificial intelligence in radiation oncology
Huynh, E., Hosny, A., Guthier, C. V., Bitterman, D. S., Petit, S., Haas-Kogan, D. A., Kann, B. H., Aerts, H. J. W. L., & Mak, R. H. (2020) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-020-0417-8 · Cited by 454
Survey on deep learning for radiotherapy
Meyer, P., Noblet, V., Mazzara, C., & Lallement, A. (2018) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2018.05.018 · Cited by 302
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
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