Advertisement

Artifact Disentanglement Network for Unsupervised Metal Artifact Reduction

  • Haofu LiaoEmail author
  • Wei-An Lin
  • Jianbo Yuan
  • S. Kevin Zhou
  • Jiebo Luo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11769)

Abstract

Current deep neural network based approaches to computed tomography (CT) metal artifact reduction (MAR) are supervised methods which rely heavily on synthesized data for training. However, as synthesized data may not perfectly simulate the underlying physical mechanisms of CT imaging, the supervised methods often generalize poorly to clinical applications. To address this problem, we propose, to the best of our knowledge, the first unsupervised learning approach to MAR. Specifically, we introduce a novel artifact disentanglement network that enables different forms of generations and regularizations between the artifact-affected and artifact-free image domains to support unsupervised learning. Extensive experiments show that our method significantly outperforms the existing unsupervised models for image-to-image translation problems, and achieves comparable performance to existing supervised models on a synthesized dataset. When applied to clinical datasets, our method achieves considerable improvements over the supervised models. The source code of this paper is publicly available at https://github.com/liaohaofu/adn.

Notes

Acknowledgement

This work was supported in part by NSF award #1722847 and the Morris K. Udall Center of Excellence in Parkinson’s Disease Research by NIH.

References

  1. 1.
    Gjesteby, L., et al.: Metal artifact reduction in CT: where are we after four decades? IEEE Access 4, 5826–5849 (2016)CrossRefGoogle Scholar
  2. 2.
    Gjesteby, L., et al.: Deep neural network for CT metal artifact reduction with a perceptual loss function. In: Proceedings of the Fifth International Conference on Image Formation in X-Ray Computed Tomography (2018)Google Scholar
  3. 3.
    Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems (2014)Google Scholar
  4. 4.
    Huang, X., Wang, J., Tang, F., Zhong, T., Zhang, Y.: Metal artifact reduction on cervical CT images by deep residual learning. Biomed. Eng. Online 17(1), 175 (2018)CrossRefGoogle Scholar
  5. 5.
    Huang, X., Liu, M.-Y., Belongie, S., Kautz, J.: Multimodal unsupervised image-to-image translation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11207, pp. 179–196. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-01219-9_11CrossRefGoogle Scholar
  6. 6.
    Lee, H.-Y., Tseng, H.-Y., Huang, J.-B., Singh, M., Yang, M.-H.: Diverse image-to-image translation via disentangled representations. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11205, pp. 36–52. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-01246-5_3CrossRefGoogle Scholar
  7. 7.
    Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-24574-4_28CrossRefGoogle Scholar
  8. 8.
    Ulyanov, D., Vedaldi, A., Lempitsky, V.S.: Deep image prior. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2018Google Scholar
  9. 9.
    Wang, J., Zhao, Y., Noble, J.H., Dawant, B.M.: Conditional generative adversarial networks for metal artifact reduction in CT images of the ear. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11070, pp. 3–11. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-00928-1_1CrossRefGoogle Scholar
  10. 10.
    Yan, K., Wang, X., Lu, L., Summers, R.M.: DeepLesion: automated mining of large-scale lesion annotations and universal lesion detection with deep learning. J. Med. Imaging (2018) Google Scholar
  11. 11.
    Zhang, Y., Yu, H.: Convolutional neural network based metal artifact reduction in X-ray computed tomography. IEEE Trans. Med. Imaging 37(6), 1370–1381 (2018)CrossRefGoogle Scholar
  12. 12.
    Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)Google Scholar
  13. 13.
    Zhu, J., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. CoRR abs/1703.10593 (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Haofu Liao
    • 1
    Email author
  • Wei-An Lin
    • 2
  • Jianbo Yuan
    • 1
  • S. Kevin Zhou
    • 3
    • 4
  • Jiebo Luo
    • 1
  1. 1.Department of Computer ScienceUniversity of RochesterRochesterUSA
  2. 2.Department of ECEUniversity of MarylandCollege ParkUSA
  3. 3.Institute of Computing TechnologyChinese Academy of SciencesBeijingChina
  4. 4.Peng Cheng LaboratoryShenzhenChina

Personalised recommendations