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Semantic-Aware Generative Adversarial Nets for Unsupervised Domain Adaptation in Chest X-Ray Segmentation

  • Cheng Chen
  • Qi Dou
  • Hao Chen
  • Pheng-Ann Heng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11046)

Abstract

In spite of the compelling achievements that deep neural networks (DNNs) have made in medical image computing, these deep models often suffer from degraded performance when being applied to new test datasets with domain shift. In this paper, we present a novel unsupervised domain adaptation approach for segmentation tasks by designing semantic-aware generative adversarial networks (GANs). Specifically, we transform the test image into the appearance of source domain, with the semantic structural information being well preserved, which is achieved by imposing a nested adversarial learning in semantic label space. In this way, the segmentation DNN learned from the source domain is able to be directly generalized to the transformed test image, eliminating the need of training a new model for every new target dataset. Our domain adaptation procedure is unsupervised, without using any target domain labels. The adversarial learning of our network is guided by a GAN loss for mapping data distributions, a cycle-consistency loss for retaining pixel-level content, and a semantic-aware loss for enhancing structural information. We validated our method on two different chest X-ray public datasets for left/right lung segmentation. Experimental results show that the segmentation performance of our unsupervised approach is highly competitive with the upper bound of supervised transfer learning.

Notes

Acknowledgments

The work described in this paper was supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region (Project no. GRF 14225616) and a grant from Hong Kong Innovation and Technology Commission (Project no. ITS/426/17FP).

References

  1. 1.
    Bousmalis, K., Silberman, N., Dohan, D., et al.: Unsupervised pixel-level domain adaptation with generative adversarial networks. In: CVPR. pp. 95–104 (2017)Google Scholar
  2. 2.
    Chartsias, A., Joyce, T., Dharmakumar, R., Tsaftaris, S.A.: Adversarial image synthesis for unpaired multi-modal cardiac data. In: Tsaftaris, S.A., Gooya, A., Frangi, A.F., Prince, J.L. (eds.) SASHIMI 2017. LNCS, vol. 10557, pp. 3–13. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-68127-6_1CrossRefGoogle Scholar
  3. 3.
    Dou, Q., Ouyang, C., Chen, C., Chen, H., Heng, P.: Unsupervised cross-modality domain adaptation of convnets for biomedical image segmentations with adversarial loss. In: IJCAI, pp. 691–697 (2018)Google Scholar
  4. 4.
    Dou, Q., et al.: Automated pulmonary nodule detection via 3d convnets with online sample filtering and hybrid-loss residual learning. In: MICCAI, pp. 630–638 (2017)Google Scholar
  5. 5.
    Ganin, Y., Ustinova, E., Ajakan, H.: Domain-adversarial training of neural networks. J. Mach. Learn. Res. 17(1), 2096–2030 (2016)Google Scholar
  6. 6.
    Ghafoorian, M., et al.: Transfer learning for domain adaptation in MRI: application in brain lesion segmentation. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 516–524. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-66179-7_59CrossRefGoogle Scholar
  7. 7.
    Huo, Y., Xu, Z., Bao, S., et al.: Adversarial synthesis learning enables segmentation without target modality ground truth. arXiv preprint arXiv:1712.07695 (2017)
  8. 8.
    Jaeger, S.: Two public chest x-ray datasets for computer-aided screening of pulmonary diseases. Quant. Imaging Med. Surg. 4(6), 475 (2014)Google Scholar
  9. 9.
    Kamnitsas, K., et al.: Unsupervised domain adaptation in brain lesion segmentation with adversarial networks. In: Niethammer, M., et al. (eds.) IPMI 2017. LNCS, vol. 10265, pp. 597–609. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-59050-9_47CrossRefGoogle Scholar
  10. 10.
    Philipsen, R.H., Maduskar, P., Hogeweg, L., Melendez, J., Sánchez, C.I., van Ginneken, B.: Localized energy-based normalization of medical images: application to chest radiography. IEEE Trans. Med. Imaging 34(9), 1965–1975 (2015)CrossRefGoogle Scholar
  11. 11.
    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
  12. 12.
    Salimans, T., Goodfellow, I., et al.: Improved techniques for training gans. Adv. Neural Inf. Process. Syst. 2234–2242 (2016)Google Scholar
  13. 13.
    Shiraishi, J., Katsuragawa, S., Ikezoe, J., Matsumoto, T., Kobayashi, T.: Development of a digital image database for chest radiographs with and without a lung nodule: receiver operating characteristic analysis of radiologists’ detection of pulmonary nodules. Am. J. Roentgenol. 174(1), 71–74 (2000)CrossRefGoogle Scholar
  14. 14.
    Tzeng, E., Hoffman, J., Saenko, K., Darrell, T.: Adversarial discriminative domain adaptation. In: CVPR, pp. 2962–2971 (2017)Google Scholar
  15. 15.
    Wang, L.: Correction for variations in mri scanner sensitivity in brain studies with histogram matching. Magn. Reson. Med. 39(2), 322–327 (1998)CrossRefGoogle Scholar
  16. 16.
    Zhu, J., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: ICCV, pp. 2242–2251 (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Dept. of Computer Science and EngineeringThe Chinese University of Hong KongHong KongChina
  2. 2.Imsight Medical Technology Co., Ltd.ShenzhenChina

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