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Spinal surgery planning necessitates automatic segmentation of vertebrae in cone-beam computed tomography (CBCT), an intraoperative imaging modality that is widely used in intervention.
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Ibragimov, B., Korez, R., et al.: Interpolation-based detection of lumbar vertebrae in ct spine images. In: Recent Advances in Computational Methods and Clinical Applications for Spine Imaging, pp. 73–84. Springer (2015)
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Ibragimov, B., Korez, R., Likar, B., Pernuš, F., Xing, L., Vrtovec, T.: Segmentation of pathological structures by landmark-assisted deformable models. IEEE Transactions on Medical Imaging 36
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Kamnitsas, K., Baumgartner, C., et al.: Unsupervised domain adaptation in brain lesion segmentation with adversarial networks. In: MICCAI. pp. 597–609. Springer (2017)
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Liu, M.Y., Breuel, T., Kautz, J.: Unsupervised image-to-image translation networks. In: Advances in Neural Information Processing Systems. pp. 700–708 (2017)
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Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: ICCV. pp. 2223–2232 (2017)
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Huang, X., Liu, M.Y., Belongie, S., Kautz, J.: Multimodal unsupervised image-to-image translation. In: ECCV. pp. 172–189 (2018)
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Lee, H.Y., Tseng, H.Y., Huang, J.B., Singh, M., Yang, M.H.: Diverse image-to-image translation via disentangled representations. In: ECCV. pp. 35–51 (2018)
Burström, G., Buerger, C., et al.: Machine learning for automated 3-dimensional segmentation of the spine and suggested placement of pedicle screws based on intraoperative cone-beam computer tomography. Journal of Neurosurgery: Spine 31
2019
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Liao, H., Lin, W.A., Zhou, S.K., Luo, J.: Adn: Artifact disentanglement network for unsupervised metal artifact reduction. IEEE Transactions on Medical Imaging 39
2019
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Park, T., Liu, M.Y., Wang, T.C., Zhu, J.Y.: Semantic image synthesis with spatially-adaptive normalization. In: CVPR. pp. 2337–2346 (2019)
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Zhou, S.K., Rueckert, D., Fichtinger, G.: Handbook of medical image computing and computer assisted intervention. Academic Press (2019)
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2018
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Tsai, Y.H., Hung, W.C., et al.: Learning to adapt structured output space for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 7472–7481 (2018)
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Zhang, Z., Yang, L., Zheng, Y.: Translating and segmenting multimodal medical volumes with cycle-and shape-consistency generative adversarial network. In: CVPR. pp. 9242–9251 (2018)
2018
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Chen, C., Dou, Q., Chen, H., Qin, J., Heng, P.A.: Unsupervised bidirectional cross-modality adaptation via deeply synergistic image and feature alignment for medical image segmentation. IEEE Transactions on Medical Imaging (2020)
2020
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2020
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Zhou, S.K., Greenspan, H., Davatzikos, C., Duncan, J.S., van Ginneken, B., Madabhushi, A., Prince, J.L., Rueckert, D., Summers, R.M.: A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises. Proceedings of the IEEE (2021)
2021
Closest in time.