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Magnetic resonance imaging (MRI) is being increasingly utilized to assess, diagnose, and plan treatment for a variety of diseases.
E. F. Jackson, L. E. Ginsberg, D. F. Schomer, and N. E. Leeds, “A review of MRI pulse sequences and techniques in neuroimaging,” Surgical Neurology , vol. 47, no. 2, pp. 185–199, Feb 1997
1997
Earlier work this paper cites.
M. Jenkinson, M. Pechaud, S. Smith, and Others, “BET2: MR-based estimation of brain, skull and scalp surfaces,” in Eleventh annual meeting of the organization for human brain mapping , vol. 17. Toronto., 2005, p. 167
2005
Earlier work this paper cites.
Y. Bengio, J. Louradour, R. Collobert, and J. Weston, “Curriculum learning,” in Proceedings of the 26th Annual International Conference on Machine Learning . ACM, 2009, pp. 41–48
2009
Earlier work this paper cites.
S. Klein et al. , “Elastix: a toolbox for intensity-based medical image registration,” IEEE transactions on medical imaging , vol. 29, no. 1, pp. 196–205, 2010
2010
Earlier work this paper cites.
J. E. Iglesias et al. , “Is synthesizing MRI contrast useful for inter-modality analysis?” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2013, pp. 631–638
2013
Earlier work this paper cites.
D. H. Ye et al. , “Modality propagation: coherent synthesis of subject-specific scans with data-driven regularization,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2013, pp. 606–613
2013
Earlier work this paper cites.
A. Jog, S. Roy, A. Carass, and J. L. Prince, “Magnetic resonance image synthesis through patch regression,” in Proceedings - International Symposium on Biomedical Imaging , vol. 2013. NIH Public Access, Dec 2013, pp. 350–353
2013
Earlier work this paper cites.
A. Jog, A. Carass, D. L. Pham, and J. L. Prince, “Random forest FLAIR reconstruction from T1, T2, and Pd-weighted MRI,” in Proceedings - International Symposium on Biomedical Imaging . IEEE, 2014, pp. 1079–1082
2014
Earlier work this paper cites.
I. Goodfellow et al. , “Generative adversarial nets,” in Advances in Neural Information Processing Systems , 2014, pp. 2672–2680
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
J. Gauthier, “Conditional generative adversarial nets for convolutional face generation,” Class Project for Stanford CS231N: Convolutional Neural Networks for Visual Recognition, Winter semester , vol. 2014, no. 5, p. 2, 2014
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
G. van Tulder and M. de Bruijne, “Why does synthesized data improve multi-sequence classification?” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2015, pp. 531–538
2015
Earlier work this paper cites.
A. Jog, A. Carass, D. L. Pham, and J. L. Prince, “Tree-encoded conditional random fields for image synthesis,” in International Conference on Information Processing in Medical Imaging . Springer, 2015, pp. 733–745
2015
Earlier work this paper cites.
H. Van Nguyen, K. Zhou, and R. Vemulapalli, “Cross-domain synthesis of medical images using efficient location-sensitive deep network,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2015, pp. 677–684
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional Networks for Biomedical Image Segmentation,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, May 2015, pp. 234–241
2015
Earlier work this paper cites.
B. H. Menze et al. , “The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS),” IEEE Transactions on Medical Imaging , vol. 34, no. 10, pp. 1993–2024, Oct 2015. [Online]. Available: http://ieeexplore.ieee.org/document/6975210/
2015
Cited alongside, same era.
C. Bowles et al. , “Pseudo-healthy image synthesis for white matter lesion segmentation,” in Simulation and Synthesis in Medical Imaging . Springer International Publishing, Oct 2016, pp. 87–96
2016
Cited alongside, same era.
M. Havaei, N. Guizard, N. Chapados, and Y. Bengio, “HeMIS: Hetero-Modal Image Segmentation,” in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016 . Springer International Publishing, 2016, pp. 469–477
2016
Cited alongside, same era.
S. Pereira, A. Pinto, V. Alves, and C. A. Silva, “Brain tumor segmentation using convolutional neural networks in MRI images,” IEEE Transactions on Medical Imaging , vol. 35, no. 5, pp. 1240–1251, 2016
2016
Cited alongside, same era.
O. Maier et al. , “ISLES 2015-A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI,” Medical Image Analysis , vol. 35, pp. 250–269, 2017
2017
Later among the works it cites.
T. Varsavsky et al. , “PIMMS: Permutation Invariant Multi-modal Segmentation,” in Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support . Springer International Publishing, 2018, pp. 201–209
2018
Later among the works it cites.
A. Chartsias, T. Joyce, M. V. Giuffrida, and S. A. Tsaftaris, “Multimodal MR Synthesis via Modality-Invariant Latent Representation,” IEEE Transactions on Medical Imaging , vol. 37, no. 3, pp. 803–814, Mar 2018
2018
Later among the works it cites.
H. Chen et al. , “VoxResNet: Deep voxelwise residual networks for brain segmentation from 3D MR images,” NeuroImage , vol. 170, pp. 446–455, 2018
2018
Later among the works it cites.
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S. Roy et al. , “Patch based synthesis of whole head MR images: Application to EPI distortion correction,” in International Workshop on Simulation and Synthesis in Medical Imaging . Springer, 2016, pp. 146–156
2016
Cited alongside, same era.
Y. Huang, L. Beltrachini, L. Shao, and A. F. Frangi, “Geometry regularized joint dictionary learning for cross-modality image synthesis in magnetic resonance imaging,” in International Workshop on Simulation and Synthesis in Medical Imaging . Springer, 2016, pp. 118–126
2016
Cited alongside, same era.
V. Sevetlidis, M. V. Giuffrida, and S. A. Tsaftaris, “Whole image synthesis using a deep encoder-decoder network,” in International Workshop on Simulation and Synthesis in Medical Imaging . Springer, 2016, pp. 127–137
2016
Cited alongside, same era.
S. Reed et al. , “Generative adversarial text to image synthesis,” Proceedings of The 33rd International Conference on Machine Learning , pp. 1060–1069, 2016
2016
Cited alongside, same era.
2016
Cited alongside, same era.
2016
Cited alongside, same era.
J. Johnson, A. Alahi, and L. Fei-Fei, “Perceptual losses for real-time style transfer and super-resolution,” in European conference on computer vision . Springer, 2016, pp. 694–711
2016
Cited alongside, same era.
S. Bakas et al. , “2017 International MICCAI BraTS Challenge,” 2017
2017
Cited alongside, same era.
F. Isensee et al. , “Brain Tumor Segmentation and Radiomics Survival Prediction : Contribution to the BRATS 2017 Challenge,” in Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries . Springer International Publishing, 2018, pp. 287–297
2018
Later among the works it cites.
G. Wang, W. Li, S. Ourselin, and T. Vercauteren, “Automatic brain tumor segmentation using cascaded anisotropic convolutional neural networks,” in Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries . Springer International Publishing, 2018, pp. 178–190
2018
Later among the works it cites.
J. Dolz et al. , “HyperDense-Net: A hyper-densely connected CNN for multi-modal image segmentation,” IEEE Transactions on Medical Imaging , Apr 2018
2018
Later among the works it cites.
S. Olut, Y. H. Sahin, U. Demir, and G. Unal, “Generative adversarial training for MRA image synthesis using multi-contrast MRI,” in International Workshop on PRedictive Intelligence In MEdicine . Springer, 2018, pp. 147–154
2018
Later among the works it cites.
R. Mehta and T. Arbel, “RS-Net: Regression-Segmentation 3D CNN for Synthesis of Full Resolution Missing Brain MRI in the Presence of Tumours,” in International Workshop on Simulation and Synthesis in Medical Imaging . Springer, 2018, pp. 119–129
2018
Later among the works it cites.
S. Kazeminia et al. , “GANs for medical image analysis,” arXiv preprint arXiv:1809.06222 , 2018
2018
Later among the works it cites.
Y. Xue et al. , “SegAN: Adversarial network with multi-scale L1 loss for medical image segmentation,” Neuroinformatics , vol. 16, no. 3-4, pp. 383–392, 2018
2018
Later among the works it cites.
S. Izadi, Z. Mirikharaji, J. Kawahara, and G. Hamarneh, “Generative adversarial networks to segment skin lesions,” in 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018) . IEEE, 2018, pp. 881–884
2018
Later among the works it cites.
A. Bentaieb and G. Hamarneh, “Adversarial Stain Transfer for Histopathology Image Analysis,” IEEE Transactions on Medical Imaging , vol. 37, no. 3, pp. 792–802, Mar 2018
2018
Later among the works it cites.
2018
Later among the works it cites.
M. Salem et al. , “Multiple Sclerosis Lesion Synthesis in MRI using an encoder-decoder U-NET,” IEEE Access , vol. 7, pp. 25 171–25 184, 2019
2019
Closest in time.
B. Yu et al. , “Ea-GANs: Edge-aware Generative Adversarial Networks for Cross-modality MR Image Synthesis,” IEEE Transactions on Medical Imaging , pp. 1–1, 2019
2019
Closest in time.
S. Ul et al. , “Image Synthesis in Multi-Contrast MRI with Conditional Generative Adversarial Networks,” IEEE Transactions on Medical Imaging , pp. 1–1, 2019
2019
Closest in time.