Task Driven Generative Modeling for Unsupervised Domain Adaptation: Application to X-ray Image Segmentation

  • Yue Zhang
  • Shun Miao
  • Tommaso Mansi
  • Rui Liao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11071)


Automatic parsing of anatomical objects in X-ray images is critical to many clinical applications in particular towards image-guided invention and workflow automation. Existing deep network models require a large amount of labeled data. However, obtaining accurate pixel-wise labeling in X-ray images relies heavily on skilled clinicians due to the large overlaps of anatomy and the complex texture patterns. On the other hand, organs in 3D CT scans preserve clearer structures as well as sharper boundaries and thus can be easily delineated. In this paper, we propose a novel model framework for learning automatic X-ray image parsing from labeled CT scans. Specifically, a Dense Image-to-Image network (DI2I) for multi-organ segmentation is first trained on X-ray like Digitally Reconstructed Radiographs (DRRs) rendered from 3D CT volumes. Then we introduce a Task Driven Generative Adversarial Network (TD-GAN) architecture to achieve simultaneous style transfer and parsing for unseen real X-ray images. TD-GAN consists of a modified cycle-GAN substructure for pixel-to-pixel translation between DRRs and X-ray images and an added module leveraging the pre-trained DI2I to enforce segmentation consistency. The TD-GAN framework is general and can be easily adapted to other learning tasks. In the numerical experiments, we validate the proposed model on 815 DRRs and 153 topograms. While the vanilla DI2I without any adaptation fails completely on segmenting the topograms, the proposed model does not require any topogram labels and is able to provide a promising average dice of \(85\%\) which achieves the same level accuracy of supervised training (88%).


Unsupervised domain adaptation Deep learning Image parsing Generative adversarial networks Task driven 


  1. 1.
    Zhu, Y., Prummer, S., Wang, P., Chen, T., Comaniciu, D., Ostermeier, M.: Dynamic layer separation for coronary DSA and enhancement in fluoroscopic sequences. In: Yang, G.-Z., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds.) MICCAI 2009. LNCS, vol. 5762, pp. 877–884. Springer, Heidelberg (2009). Scholar
  2. 2.
    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). Scholar
  3. 3.
    Albarqouni, S., Fotouhi, J., Navab, N.: X-Ray in-depth decomposition: revealing the latent structures. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 444–452. Springer, Cham (2017). Scholar
  4. 4.
    Bousmalis, K., et al.: Domain separation networks. In: NIPs (2016)Google Scholar
  5. 5.
    Tzeng, E., et al.: Adversarial discriminative domain adaptation. In: CVPR (2017)Google Scholar
  6. 6.
    Bousmalis, K., et al.: Unsupervised pixel-level domain adaptation with generative adversarial networks. In: CVPR (2017)Google Scholar
  7. 7.
    Zhu, J.Y., et al.: Unpaired image-to-image translation using cycle-consistent adversarial networks. arXiv preprint arXiv:1703.10593 (2017)
  8. 8.
    Huang, G., et al.: Densely connected convolutional networks. In: CVPR (2017)Google Scholar
  9. 9.
    Jégou, S., et al.: The one hundred layers tiramisu: fully convolutional densenets for semantic segmentation. In: CVPRW (2017)Google Scholar
  10. 10.
    He, K., et al.: Deep residual learning for image recognition. In: CVPR (2016)Google Scholar
  11. 11.
    Mirza, M., et al.: Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784 (2014)

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Yue Zhang
    • 1
    • 2
  • Shun Miao
    • 1
  • Tommaso Mansi
    • 1
  • Rui Liao
    • 1
  1. 1.Medical Imaging TechnologiesSiemens Healthineers Technology CenterPrincetonUSA
  2. 2.Department of Mathematics, Applied Mathematics and StatisticsCase Western Reserve UniversityClevelandUSA

Personalised recommendations