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Image registration plays an important role in comparing images.
R. P. Woods, J. C. Mazziotta, and S. R. Cherry, “MRI-PET registration with automated algorithm.” Journal of Computer Assisted Tomography , vol. 17, no. 4, pp. 536–46, 1993
1993
Earlier work this paper cites.
D. Rueckert, L. Sonoda, C. Hayes, D. Hill, M. Leach, and D. Hawkes, “Nonrigid registration using free-form deformations: application to breast MR images,” IEEE Transactions on Medical Imaging , vol. 18, no. 8, pp. 712–721, 1999
1999
Earlier work this paper cites.
H.-U. Dodt, U. Leischner, A. Schierloh, N. Jährling, C. P. Mauch, K. Deininger, J. M. Deussing, M. Eder, W. Zieglgänsberger, and K. Becker, “Ultramicroscopy: three-dimensional visualization of neuronal networks in the whole mouse brain,” Nature Methods , vol. 4, no. 4, pp. 331–336, apr 2007
2007
Earlier work this paper cites.
B. Avants, C. Epstein, M. Grossman, and J. Gee, “Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain,” Medical Image Analysis , vol. 12, no. 1, pp. 26–41, 2008
2008
Earlier work this paper cites.
S. Klein, M. Staring, K. Murphy, M. Viergever, and J. Pluim, “elastix: A Toolbox for Intensity-Based Medical Image Registration,” IEEE Transactions on Medical Imaging , vol. 29, no. 1, pp. 196–205, jan 2010
2010
Earlier work this paper cites.
M. Modat, G. R. Ridgway, Z. A. Taylor, M. Lehmann, J. Barnes, D. J. Hawkes, N. C. Fox, and S. Ourselin, “Fast free-form deformation using graphics processing units,” Computer Methods and Programs in Biomedicine , vol. 98, no. 3, pp. 278–284, 2010
2010
Earlier work this paper cites.
B. B. Avants, N. J. Tustison, G. Song, P. A. Cook, A. Klein, and J. C. Gee, “A reproducible evaluation of ANTs similarity metric performance in brain image registration,” NeuroImage , vol. 54, no. 3, pp. 2033–2044, 2011
2011
Earlier work this paper cites.
H. Hama, H. Kurokawa, H. Kawano, R. Ando, T. Shimogori, H. Noda, K. Fukami, A. Sakaue-Sawano, and A. Miyawaki, “Scale: a chemical approach for fluorescence imaging and reconstruction of transparent mouse brain,” Nature Neuroscience , vol. 14, no. 11, pp. 1481–1488, aug 2011
2011
Earlier work this paper cites.
M.-T. Ke, S. Fujimoto, and T. Imai, “SeeDB: a simple and morphology-preserving optical clearing agent for neuronal circuit reconstruction,” Nature Neuroscience , vol. 16, no. 8, pp. 1154–1161, jun 2013
2013
Earlier work this paper cites.
K. Chung and K. Deisseroth, “CLARITY for mapping the nervous system,” Nature Methods , vol. 10, no. 6, pp. 508–513, may 2013
2013
Earlier work this paper cites.
E. A. Susaki, K. Tainaka, D. Perrin, F. Kishino, T. Tawara, T. M. Watanabe, C. Yokoyama, H. Onoe, M. Eguchi, S. Yamaguchi, T. Abe, H. Kiyonari, Y. Shimizu, A. Miyawaki, H. Yokota, and H. R. Ueda, “Whole-brain imaging with single-cell resolution using chemical cocktails and computational analysis,” Cell , vol. 157, no. 3, pp. 726–739, apr 2014
2014
Cited alongside, same era.
N. Renier, Z. Wu, D. J. Simon, J. Yang, P. Ariel, and M. Tessier-Lavigne, “iDISCO: A simple, rapid method to immunolabel large tissue samples for volume imaging,” Cell , vol. 159, no. 4, pp. 896–910, 2014
2014
Cited alongside, same era.
E. A. Susaki, K. Tainaka, D. Perrin, H. Yukinaga, A. Kuno, and H. R. Ueda, “Advanced CUBIC protocols for whole-brain and whole-body clearing and imaging,” Nature Protocols , vol. 10, no. 11, pp. 1709–1727, oct 2015
2015
Cited alongside, same era.
D. Mateus, M. Simonovsky, N. Navab, and N. Komodakis, “A Deep Metric for Multimodal Registration,” in Medical Image Computing and Computer-Assisted Intervention , vol. 9902, 2016, pp. 10–18
L. Yu, J.-Z. Cheng, Q. Dou, X. Yang, H. Chen, J. Qin, and P.-A. Heng, “Automatic 3D cardiovascular MR segmentation with densely-connected volumetric ConvNets,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2017, pp. 287–295
2017
Later among the works it cites.
Q. Dou, L. Yu, H. Chen, Y. Jin, X. Yang, J. Qin, and P.-A. Heng, “3D deeply supervised network for automated segmentation of volumetric medical images,” Medical Image Analysis , vol. 41, pp. 40–54, 2017
2017
Later among the works it cites.
G. Litjens, T. Kooi, B. E. Bejnordi, A. Arindra, A. Setio, F. Ciompi, M. Ghafoorian, J. A. W. M. V. D. Laak, B. V. Ginneken, and C. I. Sánchez, “A survey on deep learning in medical image analysis,” CoRR , vol. 42, no. December 2012, pp. 60–88, 2017
2017
Later among the works it cites.
M.-M. Rohé, M. Datar, T. Heimann, M. Sermesant, and X. Pennec, “SVF-Net: Learning deformable image registration using shape matching,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2017, pp. 266–274
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2016
Cited alongside, same era.
G. Wu, M. Kim, Q. Wang, B. C. Munsell, D. Shen, and f. t. A. D. N. Initiative, “Scalable High-Performance Image Registration Framework by Unsupervised Deep Feature Representations Learning.” IEEE Transactions on Biomedical Engineering , vol. 63, no. 7, pp. 1505–16, jul 2016
2016
Cited alongside, same era.
X. Yang, R. Kwitt, and M. Niethammer, “Fast predictive image registration,” in Deep Learning and Data Labeling for Medical Applications . Springer, 2016, pp. 48–57
2016
Cited alongside, same era.
Z. Xu, C. P. Lee, M. P. Heinrich, M. Modat, D. Rueckert, S. Ourselin, R. G. Abramson, and B. A. Landman, “Evaluation of Six Registration Methods for the Human Abdomen on Clinically Acquired CT,” IEEE Transactions on Biomedical Engineering , vol. 63, no. 8, pp. 1563–1572, aug 2016
2016
Cited alongside, same era.
L. Hammelrath, S. Škokić, A. Khmelinskii, A. Hess, N. van der Knaap, M. Staring, B. P. Lelieveldt, D. Wiedermann, and M. Hoehn, “Morphological maturation of the mouse brain: An in vivo MRI and histology investigation,” NeuroImage , vol. 125, pp. 144–152, 2016
2016
Cited alongside, same era.
F. Tatsuki, G. A. Sunagawa, S. Shi, E. A. Susaki, H. Yukinaga, D. Perrin, K. Sumiyama, M. Ukai-Tadenuma, H. Fujishima, R.-i. Ohno, D. Tone, K. L. Ode, K. Matsumoto, and H. R. Ueda, “Involvement of Ca2+-Dependent Hyperpolarization in Sleep Duration in Mammals,” Neuron , vol. 90, no. 1, pp. 70–85, 2016
2016
Cited alongside, same era.
2017
Later among the works it cites.
H. Sokooti, B. de Vos, F. Berendsen, B. P. Lelieveldt, I. Išgum, and M. Staring, “Nonrigid image registration using multi-scale 3d convolutional neural networks,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2017, pp. 232–239
2017
Later among the works it cites.
2018
Closest in time.
X. Cheng, L. Zhang, and Y. Zheng, “Deep similarity learning for multimodal medical images,” Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization , vol. 6, no. 3, pp. 248–252, apr 2018
2018
Closest in time.
2018
Closest in time.
G. Balakrishnan, A. Zhao, M. R. Sabuncu, J. Guttag, and A. V. Dalca, “An unsupervised learning model for deformable medical image registration,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . IEEE, 2018, pp. 9252–9260
2018
Closest in time.