Fetching the paper…
Reading the bibliography…
With an aim to increase the capture range and accelerate the performance of state-of-the-art inter-subject and subject-to-template 3D registration, we propose deep learning-based methods that are trained to find the 3D position of arbitrarily oriented subjects or anatomy based on slices or volumes of medical images.
S. Thesen, O. Heid, E. Mueller, and L. R. Schad, “Prospective acquisition correction for head motion with image-based tracking for real-time fMRI,” Magnetic resonance in medicine , vol. 44, no. 3, pp. 457–465, 2000
2000
Earlier work this paper cites.
D. L. Hill, P. G. Batchelor, M. Holden, and D. J. Hawkes, “Medical image registration,” Physics in medicine & biology , vol. 46, no. 3, p. R1, 2001
2001
Earlier work this paper cites.
J. P. Pluim, J. A. Maintz, and M. A. Viergever, “Mutual-information-based registration of medical images: a survey,” IEEE transactions on medical imaging , vol. 22, no. 8, pp. 986–1004, 2003
2003
Earlier work this paper cites.
P. A. Yushkevich, J. Piven, H. C. Hazlett, R. G. Smith, S. Ho, J. C. Gee, and G. Gerig, “User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability,” Neuroimage , vol. 31, no. 3, pp. 1116–1128, 2006
2006
Earlier work this paper cites.
A. Gholipour, N. Kehtarnavaz, R. Briggs, M. Devous, and K. Gopinath, “Brain functional localization: a survey of image registration techniques,” IEEE transactions on medical imaging , vol. 26, no. 4, pp. 427–451, 2007
2007
Earlier work this paper cites.
D. Q. Huynh, “Metrics for 3D rotations: Comparison and analysis,” Journal of Mathematical Imaging and Vision , vol. 35, no. 2, pp. 155–164, 2009
2009
Earlier work this paper cites.
R. Shams, P. Sadeghi, R. A. Kennedy, and R. I. Hartley, “A survey of medical image registration on multicore and the GPU,” IEEE Signal Processing Magazine , vol. 27, no. 2, pp. 50–60, 2010
2010
Earlier work this paper cites.
A. Gholipour, J. A. Estroff, and S. K. Warfield, “Robust super-resolution volume reconstruction from slice acquisitions: application to fetal brain MRI,” IEEE transactions on medical imaging , vol. 29, no. 10, pp. 1739–1758, 2010
2010
Earlier work this paper cites.
N. White, C. Roddey, A. Shankaranarayanan, E. Han, D. Rettmann, J. Santos, J. Kuperman, and A. Dale, “Promo: Real-time prospective motion correction in MRI using image-based tracking,” Magnetic Resonance in Medicine , vol. 63, no. 1, pp. 91–105, 2010
2010
Earlier work this paper cites.
A. Gholipour, M. Polak, A. van der Kouwe, E. Nevo, and S. K. Warfield, “Motion-robust MRI through real-time motion tracking and retrospective super-resolution volume reconstruction,” in Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE . IEEE, 2011, pp. 5722–5725
2011
Earlier work this paper cites.
P. Markelj, D. Tomaževič, B. Likar, and F. Pernuš, “A review of 3D/2D registration methods for image-guided interventions,” Medical image analysis , vol. 16, no. 3, pp. 642–661, 2012
2012
Earlier work this paper cites.
A. Sotiras, C. Davatzikos, and N. Paragios, “Deformable medical image registration: A survey,” IEEE transactions on medical imaging , vol. 32, no. 7, pp. 1153–1190, 2013
2013
Earlier work this paper cites.
J. Arvo, Graphics gems II . Elsevier, 2013
2013
Earlier work this paper cites.
A. Gholipour, J. A. Estroff, C. E. Barnewolt, R. L. Robertson, P. E. Grant, B. Gagoski, S. K. Warfield, O. Afacan, S. A. Connolly, J. J. Neil, A. Wolfberg, and R. V. Mulkern, “Fetal MRI: a technical update with educational aspirations,” Concepts in Magnetic Resonance Part A , vol. 43, no. 6, pp. 237–266, 2014
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 3431–3440
2015
Cited alongside, same era.
S. Tulsiani and J. Malik, “Viewpoints and keypoints,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2015, pp. 1510–1519
2015
Cited alongside, same era.
H. Su, C. R. Qi, Y. Li, and L. J. Guibas, “Render for CNN: Viewpoint estimation in images using cnns trained with rendered 3d model views,” in Proceedings of the IEEE International Conference on Computer Vision , 2015, pp. 2686–2694
2015
Cited alongside, same era.
B. Kainz, M. Steinberger, W. Wein, M. Kuklisova-Murgasova, C. Malamateniou, K. Keraudren, T. Torsney-Weir, M. Rutherford, P. Aljabar, J. V. Hajnal et al. , “Fast volume reconstruction from motion corrupted stacks of 2D slices,” IEEE transactions on medical imaging , vol. 34, no. 9, pp. 1901–1913, 2015
2015
X. Yang, R. Kwitt, M. Styner, and M. Niethammer, “Quicksilver: Fast predictive image registration–a deep learning approach,” NeuroImage , vol. 158, pp. 378–396, 2017
2017
Later among the works it cites.
R. Liao, S. Miao, P. de Tournemire, S. Grbic, A. Kamen, T. Mansi, and D. Comaniciu, “An artificial agent for robust image registration.” in AAAI , 2017, pp. 4168–4175
2017
Later among the works it cites.
S. Mahendran, H. Ali, and R. Vidal, “3d pose regression using convolutional neural networks,” in IEEE International Conference on Computer Vision , vol. 1, no. 2, 2017, p. 4
2017
Later among the works it cites.
B. Marami, S. S. M. Salehi, O. Afacan, B. Scherrer, C. K. Rollins, E. Yang, J. A. Estroff, S. K. Warfield, and A. Gholipour, “Temporal slice registration and robust diffusion-tensor reconstruction for improved fetal brain structural connectivity analysis,” NeuroImage , vol. 156, pp. 475–488, 2017
2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
A. Kendall, M. Grimes, and R. Cipolla, “Posenet: A convolutional network for real-time 6-dof camera relocalization,” in Computer Vision (ICCV), 2015 IEEE International Conference on . IEEE, 2015, pp. 2938–2946
2015
Cited alongside, same era.
H. Greenspan, B. van Ginneken, and R. M. Summers, “Guest editorial deep learning in medical imaging: Overview and future promise of an exciting new technique,” IEEE Transactions on Medical Imaging , vol. 35, no. 5, pp. 1153–1159, 2016
2016
Cited alongside, same era.
M. Simonovsky, B. Gutiérrez-Becker, D. Mateus, N. Navab, and N. Komodakis, “A deep metric for multimodal registration,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2016, pp. 10–18
2016
Cited alongside, same era.
G. Wu, M. Kim, Q. Wang, B. C. Munsell, and D. Shen, “Scalable high-performance image registration framework by unsupervised deep feature representations learning,” IEEE Transactions on Biomedical Engineering , vol. 63, no. 7, pp. 1505–1516, July 2016
2016
Cited alongside, same era.
S. Miao, Z. J. Wang, Y. Zheng, and R. Liao, “Real-time 2D/3D registration via cnn regression,” in Biomedical Imaging (ISBI), 2016 IEEE 13th International Symposium on . IEEE, 2016, pp. 1430–1434
2016
Cited alongside, same era.
S. Miao, Z. J. Wang, and R. Liao, “A cnn regression approach for real-time 2D/3D registration,” IEEE transactions on medical imaging , vol. 35, no. 5, pp. 1352–1363, 2016
2016
Cited alongside, same era.
J. Wu, T. Xue, J. J. Lim, Y. Tian, J. B. Tenenbaum, A. Torralba, and W. T. Freeman, “Single image 3d interpreter network,” in European Conference on Computer Vision . Springer, 2016, pp. 365–382
2016
Cited alongside, same era.
B. Marami, B. Scherrer, O. Afacan, B. Erem, S. K. Warfield, and A. Gholipour, “Motion-robust diffusion-weighted brain MRI reconstruction through slice-level registration-based motion tracking,” IEEE transactions on medical imaging , vol. 35, no. 10, pp. 2258–2269, 2016
2016
Cited alongside, same era.
B. Hou, A. Alansary, S. McDonagh, A. Davidson, M. Rutherford, J. V. Hajnal, D. Rueckert, B. Glocker, and B. Kainz, “Predicting slice-to-volume transformation in presence of arbitrary subject motion,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2017, pp. 296–304
2017
Later among the works it cites.
E. Hughes, L. C. Grande, M. Murgasova, J. Hutter, A. Price, A. S. Gomes, J. Allsop, J. Steinweg, N. Tusor, J. Wurie et al. , “The developing human connectome: announcing the first release of open access neonatal brain imaging,” Organization for Human Brain Mapp , pp. 25–29, 2017
2017
Later among the works it cites.
S. S. M. Salehi, D. Erdogmus, and A. Gholipour, “Auto-context convolutional neural network (auto-net) for brain extraction in magnetic resonance imaging,” IEEE transactions on medical imaging , vol. 36, no. 11, pp. 2319–2330, 2017
2017
Later among the works it cites.
A. Gholipour, C. K. Rollins, C. Velasco-Annis, A. Ouaalam, A. Akhondi-Asl, O. Afacan, C. M. Ortinau, S. Clancy, C. Limperopoulos, E. Yang et al. , “A normative spatiotemporal MRI atlas of the fetal brain for automatic segmentation and analysis of early brain growth,” Scientific reports , vol. 7, no. 1, p. 476, 2017
2017
Later among the works it cites.
P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros, “Image-to-image translation with conditional adversarial networks,” arXiv preprint , 2017
2017
Later among the works it cites.
2017
Later among the works it cites.
G. Pavlakos, X. Zhou, A. Chan, K. G. Derpanis, and K. Daniilidis, “6-dof object pose from semantic keypoints,” in Robotics and Automation (ICRA), 2017 IEEE International Conference on . IEEE, 2017, pp. 2011–2018
2018
Closest in time.
B. Hou, B. Khanal, A. Alansary, S. McDonagh, A. Davidson, M. Rutherford, J. V. Hajnal, D. Rueckert, B. Glocker, and B. Kainz, “3D reconstruction in canonical co-ordinate space from arbitrarily oriented 2D images,” IEEE Transactions on Medical Imaging , 2018
2018
Closest in time.
A. I. Namburete, W. Xie, M. Yaqub, A. Zisserman, and J. A. Noble, “Fully-automated alignment of 3D fetal brain ultrasound to a canonical reference space using multi-task learning,” Medical Image Analysis , 2018
2018
Closest in time.
S. S. M. Salehi, S. R. Hashemi, C. Velasco-Annis, A. Ouaalam, J. A. Estroff, D. Erdogmus, S. K. Warfield, and A. Gholipour, “Real-time automatic fetal brain extraction in fetal mri by deep learning,” in 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018) , April 2018, pp. 720–724
2018
Closest in time.