Skip to main content

Uncertainty-Aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation

  • Conference paper
  • First Online:
Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 (MICCAI 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11765))

Abstract

Training deep convolutional neural networks usually requires a large amount of labeled data. However, it is expensive and time-consuming to annotate data for medical image segmentation tasks. In this paper, we present a novel uncertainty-aware semi-supervised framework for left atrium segmentation from 3D MR images. Our framework can effectively leverage the unlabeled data by encouraging consistent predictions of the same input under different perturbations. Concretely, the framework consists of a student model and a teacher model, and the student model learns from the teacher model by minimizing a segmentation loss and a consistency loss with respect to the targets of the teacher model. We design a novel uncertainty-aware scheme to enable the student model to gradually learn from the meaningful and reliable targets by exploiting the uncertainty information. Experiments show that our method achieves high performance gains by incorporating the unlabeled data. Our method outperforms the state-of-the-art semi-supervised methods, demonstrating the potential of our framework for the challenging semi-supervised problems.

Code is available in https://github.com/yulequan/UA-MT.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    http://atriaseg2018.cardiacatlas.org/.

References

  1. Bai, W., et al.: Semi-supervised learning for network-based cardiac MR image segmentation. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10434, pp. 253–260. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66185-8_29

    Chapter  Google Scholar 

  2. Baur, C., Albarqouni, S., Navab, N.: Semi-supervised deep learning for fully convolutional networks. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 311–319. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66179-7_36

    Chapter  Google Scholar 

  3. Chartsias, A., et al.: Factorised Spatial representation learning: application in semi-supervised myocardial segmentation. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11071, pp. 490–498. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00934-2_55

    Chapter  Google Scholar 

  4. Chen, C., Bai, W., Rueckert, D.: Multi-task learning for left atrial segmentation on GE-MRI. arXiv preprint arXiv:1810.13205 (2018)

  5. Cui, W., et al.: Semi-supervised brain lesion segmentation with an adapted mean teacher model. In: Chung, A.C.S., Gee, J.C., Yushkevich, P.A., Bao, S. (eds.) IPMI 2019. LNCS, vol. 11492, pp. 554–565. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-20351-1_43

    Chapter  Google Scholar 

  6. Dong, N., Kampffmeyer, M., Liang, X., Wang, Z., Dai, W., Xing, E.: Unsupervised domain adaptation for automatic estimation of cardiothoracic ratio. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11071, pp. 544–552. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00934-2_61

    Chapter  Google Scholar 

  7. Ganaye, P.-A., Sdika, M., Benoit-Cattin, H.: Semi-supervised learning for segmentation under semantic constraint. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11072, pp. 595–602. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00931-1_68

    Chapter  Google Scholar 

  8. Kendall, A., Gal, Y.: What uncertainties do we need in Bayesian deep learning for computer vision? In: NIPS, pp. 5574–5584 (2017)

    Google Scholar 

  9. Laine, S., Aila, T.: Temporal ensembling for semi-supervised learning. arXiv preprint (2016)

    Google Scholar 

  10. Li, X., Yu, L., Chen, H., Fu, C.W., Heng, P.A.: Semi-supervised skin lesion segmentation via transformation consistent self-ensembling model. BMVC (2018)

    Google Scholar 

  11. Milletari, F., Navab, N., Ahmadi, S.A.: V-Net: fully convolutional neural networks for volumetric medical image segmentation. In: 3DV, pp. 565–571 (2016)

    Google Scholar 

  12. Nie, D., Gao, Y., Wang, L., Shen, D.: ASDNet: attention based semi-supervised deep networks for medical image segmentation. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11073, pp. 370–378. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00937-3_43

    Chapter  Google Scholar 

  13. Perone, C.S., Cohen-Adad, J.: Deep semi-supervised segmentation with weight-averaged consistency targets. In: Stoyanov, D., et al. (eds.) DLMIA/ML-CDS -2018. LNCS, vol. 11045, pp. 12–19. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00889-5_2

    Chapter  Google Scholar 

  14. Tarvainen, A., Valpola, H.: Mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results. In: NIPS (2017)

    Google Scholar 

  15. Xiong, Z., Fedorov, V.V., Fu, X., Cheng, E., Macleod, R., Zhao, J.: Fully automatic left atrium segmentation from late gadolinium enhanced magnetic resonance imaging using a dual fully convolutional neural network. TMI 38(2), 515–524 (2019)

    Google Scholar 

  16. Yang, X., Bian, C., Yu, L., Ni, D., Heng, P.-A.: Hybrid loss guided convolutional networks for whole heart parsing. In: Pop, M., et al. (eds.) STACOM 2017. LNCS, vol. 10663, pp. 215–223. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-75541-0_23

    Chapter  Google Scholar 

  17. Yu, L., et al.: Automatic 3D cardiovascular MR segmentation with densely-connected volumetric convnets. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10434, pp. 287–295. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66185-8_33

    Chapter  Google Scholar 

  18. Zhang, Y., Yang, L., Chen, J., Fredericksen, M., Hughes, D.P., Chen, D.Z.: Deep adversarial networks for biomedical image segmentation utilizing unannotated images. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 408–416. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66179-7_47

    Chapter  Google Scholar 

  19. Zhou, Y., et al.: Semi-supervised multi-organ segmentation via multi-planar co-training. arXiv preprint arXiv:1804.02586 (2018)

Download references

Acknowledgments

The work was partially supported by HK RGC TRS project T42-409/18-R, HK RGC project (Project No. CUHK 14225616), and in part by the T Stone Robotics Institute, The Chinese University of Hong Kong.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Lequan Yu .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Yu, L., Wang, S., Li, X., Fu, CW., Heng, PA. (2019). Uncertainty-Aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11765. Springer, Cham. https://doi.org/10.1007/978-3-030-32245-8_67

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-32245-8_67

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-32244-1

  • Online ISBN: 978-3-030-32245-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics