An artificial agent for robust image registration

31st AAAI Conference on Artificial Intelligence, AAAI 2017

Volumn , Issue , 2017, Pages 4168-4175

  Liao, Rui ^a   Miao, Shun ^a   De Tournemire, Pierre ^a   Grbic, Sasa ^a   Kamen, Ali ^a   Mansi, Tommaso ^a   Comaniciu, Dorin ^a  

^a SIEMENS CORPORATE RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; COST FUNCTIONS; DEEP NEURAL NETWORKS; DIAGNOSIS; MEDICAL APPLICATIONS; MEDICAL IMAGING; NEURAL NETWORKS; OPTIMIZATION;

3D IMAGE REGISTRATION; CONVOLUTIONAL NEURAL NETWORK; HIERARCHICAL STRATEGIES; OPTIMAL REGISTRATION; REGISTRATION METHODS; REGISTRATION PROBLEMS; ROBUST IMAGE REGISTRATION; ROBUST OPTIMIZATION;

IMAGE REGISTRATION;

EID: 85029362924     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (37)

1. Bellman, R. 1957. A markovian decision process. Technical report, DTIC Document.
2. Besl, P. J., and McKay, N. D. 1992. Method for registration of 3-d shapes. In Robotics-DL tentative, 586-606. International Society for Optics and Photonics.
3. Brounstein, A.; Hacihaliloglu, I.; Guy, P.; Hodgson, A.; and Abugharbieh, R. 2011. Towards real-time 3d us to ct bone image registration using phase and curvature feature based gmm matching. In International Conference on Medical Image Computing and Computer-Assisted Intervention, 235-242. Springer.
4. Caicedo, J. C., and Lazebnik, S. 2015. Active object localization with deep reinforcement learning. In Proceedings of the IEEE International Conference on Computer Vision, 2488-2496.
5. Chen, T.; Kim, S.; Zhou, J.; Metaxas, D.; Rajagopal, G.; and Yue, N. 2009. 3d meshless prostate segmentation and registration in image guided radiotherapy. In International Conference on Medical Image Computing and Computer-Assisted Intervention, 43-50. Springer.
6. Fischer, P.; Dosovitskiy, A.; Ilg, E.; Häusser, P.; Hazrbas, C.; Golkov, V.; van der Smagt, P.; Cremers, D.; and Brox, T. 2015. Flownet: Learning optical flow with convolutional networks. arXiv preprint arXiv: 1504. 06852.
7. Ghesu, F. C.; Georgescu, B.; Mansi, T.; Neumann, D.; Hornegger, J.; and Comaniciu, D. 2016. An artificial agent for anatomical landmark detection in medical images. In International Conference on Medical Image Computing and Computer-Assisted Intervention, 229-237. Springer.
8. Hausknecht, M., and Stone, P. 2015. Deep reinforcement learning in parameterized action space. arXiv preprint arXiv: 1511. 04143.
9. Hinton, G. E., and Salakhutdinov, R. R. 2006. Reducing the dimensionality of data with neural networks. Science 313 (5786): 504-507.
10. Ibanez, L.; Schroeder, W.; Ng, L.; and Cates, J. 2005. The itk software guide. Technical report, U. S. National Library of Medicine.
11. Jaderberg, M.; Simonyan, K.; Zisserman, A.; et al. 2015. Spatial transformer networks. In Advances in Neural Information Processing Systems, 2017-2025.
12. James, A. P., and Dasarathy, B. V. 2014. Medical image fusion: A survey of the state of the art. Information Fusion 19: 4-19.
13. Kober, J., and Peters, J. 2010. Imitation and reinforcement learning. IEEE Robotics & Automation Magazine 17 (2): 55-62.
14. Krizhevsky, A.; Sutskever, I.; and Hinton, G. E. 2012. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems, 1097-1105.
15. Levine, S., and Koltun, V. 2013. Guided policy search. In ICML (3), 1-9.
16. Liao, R.; Zhang, L.; Sun, Y.; Miao, S.; and Chefd'Hotel, C. 2013. A review of recent advances in registration techniques applied to minimally invasive therapy. IEEE transactions on multimedia 15 (5): 983-1000.
17. Lu, Y.; Sun, Y.; Liao, R.; and Ong, S. H. 2014. A preoperative ct and non-contrast-enhanced c-arm ct registration framework for trans-catheter aortic valve implantation. Computerized Medical Imaging and Graphics 38 (8): 683-695.
18. Markelj, P.; Tomazevic, D.; Likar, B.; and Pernus, F. 2012. A review of 3d/2d registration methods for image-guided interventions. Medical image analysis 16 (3): 642-661.
19. Matsopoulos, G. K.; Mouravliansky, N. A.; Delibasis, K. K.; and Nikita, K. S. 1999. Automatic retinal image registration scheme using global optimization techniques. IEEE Transactions on Information Technology in Biomedicine 3 (1): 47-60.
20. Mattes, D.; Haynor, D. R.; Vesselle, H.; Lewellyn, T. K.; and Eubank, W. 2001. Nonrigid multimodality image registration. In Medical Imaging 2001, 1609-1620. International Society for Optics and Photonics.
21. Miao, S.; Liao, R.; Pfister, M.; Zhang, L.; and Ordy, V. 2013. System and method for 3-d/3-d registration between noncontrast-enhanced cbct and contrast-enhanced ct for abdominal aortic aneurysm stenting. In International Conference on Medical Image Computing and Computer-Assisted Intervention, 380-387. Springer.
22. Miao, S.; Wang, Z. J.; and Liao, R. 2016. A cnn regression approach for real-time 2d/3d registration. IEEE transactions on medical imaging 35 (5): 1352-1363.
23. Mnih, V.; Kavukcuoglu, K.; Silver, D.; Rusu, A. A.; Veness, J.; Bellemare, M. G.; Graves, A.; Riedmiller, M.; Fidjeland, A. K.; Ostrovski, G.; et al. 2015. Humanlevel control through deep reinforcement learning. Nature 518 (7540): 529-533.
24. Murphy, K.; Van Ginneken, B.; Reinhardt, J. M.; Kabus, S.; Ding, K.; Deng, X.; Cao, K.; Du, K.; Christensen, G. E.; Garcia, V.; et al. 2011. Evaluation of registration methods on thoracic ct: the empire10 challenge. IEEE transactions on medical imaging 30 (11): 1901-1920.
25. Neumann, D.; Grbíc, S.; John, M.; Navab, N.; Hornegger, J.; and Ionasec, R. 2015. Probabilistic sparse matching for robust 3d/3d fusion in minimally invasive surgery. IEEE transactions on medical imaging 34 (1): 49-60.
26. Neumann, D.; Mansi, T.; Itu, L.; Georgescu, B.; Kayvanpour, E.; Sedaghat-Hamedani, F.; Amr, A.; Haas, J.; Katus, H.; Meder, B.; et al. 2016. A self-taught artificial agent for multi-physics computational model personalization. Medical image analysis.
27. Oliveira, F. P., and Tavares, J. M. R. 2014. Medical image registration: a review. Computer methods in biomechanics and biomedical engineering 17 (2): 73-93.
28. Razlighi, Q. R.; Kehtarnavaz, N.; and Yousefi, S. 2013. Evaluating similarity measures for brain image registration. Journal of visual communication and image representation 24 (7): 977-987.
29. Rios, L. M., and Sahinidis, N. V. 2013. Derivative-free optimization: a review of algorithms and comparison of software implementations. Journal of Global Optimization 56 (3): 1247-1293.
30. Rouet, J.-M.; Jacq, J.-J.; and Roux, C. 2000. Genetic algorithms for a robust 3-d mr-ct registration. IEEE transactions on information technology in biomedicine 4 (2): 126-136.
31. Silver, D.; Huang, A.; Maddison, C. J.; Guez, A.; Sifre, L.; Van Den Driessche, G.; Schrittwieser, J.; Antonoglou, I.; Panneershelvam, V.; Lanctot, M.; et al. 2016. Mastering the game of go with deep neural networks and tree search. Nature 529 (7587): 484-489.
32. Simonyan, K.; Vedaldi, A.; and Zisserman, A. 2013. Deep inside convolutional networks: Visualising image classification models and saliency maps. arXiv preprint arXiv: 1312. 6034.
33. Snoek, J.; Larochelle, H.; and Adams, R. P. 2012. Practical Bayesian optimization of machine learning algorithms. In Advances in neural information processing systems, 2951-2959.
34. Thévenaz, P., and Unser, M. 2000. Optimization of mutual information for multiresolution image registration. IEEE transactions on image processing 9 (12): 2083-2099.
35. Wohlhart, P., and Lepetit, V. 2015. Learning descriptors for object recognition and 3d pose estimation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 3109-3118.
36. Wu, G.; Kim, M.-J.; Wang, Q.; Munsell, B.; and Shen, D. 2015. Scalable high performance image registration framework by unsupervised deep feature representations learning.
37. Yuan, Y.-x. 2015. Recent advances in trust region algorithms. Mathematical Programming 151 (1): 249-281.