Skip to main content

Robust Medical Image Segmentation from Non-expert Annotations with Tri-network

  • Conference paper
  • First Online:
Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 (MICCAI 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12264))


Deep convolutional neural networks (CNNs) have achieved commendable results on a variety of medical image segmentation tasks. However, CNNs usually require a large amount of training samples with accurate annotations, which are extremely difficult and expensive to obtain in medical image analysis field. In practice, we notice that the junior trainees after training can label medical images in some medical image segmentation applications. These non-expert annotations are more easily accessible and can be regarded as a source of weak annotation to guide network learning. In this paper, we propose a novel Tri-network learning framework to alleviate the problem of insufficient accurate annotations in medical segmentation tasks by utilizing the non-expert annotations. To be specific, we maintain three networks in our framework, and each pair of networks alternatively select informative samples for the third network learning, according to the consensus and difference between their predictions. The three networks are jointly optimized in such a collaborative manner. We evaluated our method on real and simulated non-expert annotated datasets. The experiment results show that our method effectively mines informative information from the non-expert annotations for improved segmentation performance and outperforms other competing methods.

T. Zhang and L. Yu—Equal contribution.

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

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
USD 119.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 159.99
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


  1. Arpit, D., et al.: A closer look at memorization in deep networks 2017. arXiv preprint arXiv:1706.05394 (1938)

  2. Ching, T., et al.: Opportunities and obstacles for deep learning in biology and medicine. J. Roy. Soc. Interf. 15(141), 20170387 (2018)

    Article  Google Scholar 

  3. Dgani, Y., Greenspan, H., Goldberger, J.: Training a neural network based on unreliable human annotation of medical images. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp. 39–42. IEEE (2018)

    Google Scholar 

  4. Han, B., et al.: Co-teaching: robust training of deep neural networks with extremely noisy labels. In: Advances in Neural Information Processing Systems, pp. 8527–8537 (2018)

    Google Scholar 

  5. Jiang, L., Zhou, Z., Leung, T., Li, L.J., Fei-Fei, L.: MentorNet: learning data-driven curriculum for very deep neural networks on corrupted labels. arXiv preprint arXiv:1712.05055 (2017)

  6. Kohli, M.D., Summers, R.M., Geis, J.R.: Medical image data and datasets in the era of machine learning–whitepaper from the 2016 C-MIMI meeting dataset session. J. Digit. Imaging 30(4), 392–399 (2017)

    Article  Google Scholar 

  7. Lehtinen, J., et al.: Noise2noise: learning image restoration without clean data. arXiv preprint arXiv:1803.04189 (2018)

  8. Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017)

    Article  Google Scholar 

  9. Ma, X., et al.: Dimensionality-driven learning with noisy labels. arXiv preprint arXiv:1806.02612 (2018)

  10. Mirikharaji, Z., Yan, Y., Hamarneh, G.: Learning to segment skin lesions from noisy annotations. In: Wang, Q., et al. (eds.) DART/MIL3ID - 2019. LNCS, vol. 11795, pp. 207–215. Springer, Cham (2019).

    Chapter  Google Scholar 

  11. Patrini, G., Rozza, A., Krishna Menon, A., Nock, R., Qu, L.: Making deep neural networks robust to label noise: a loss correction approach. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1944–1952 (2017)

    Google Scholar 

  12. 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).

    Chapter  Google Scholar 

  13. Shen, D., Wu, G., Suk, H.I.: Deep learning in medical image analysis. Ann. Rev. Biomed. Eng. 19, 221–248 (2017)

    Article  Google Scholar 

  14. Shiraishi, J., et al.: Development of a digital image database for chest radiographs with and without a lung nodule. Am. J. Roentgenol. 174(1), 71–74 (2000)

    Article  Google Scholar 

  15. Tanaka, D., Ikami, D., Yamasaki, T., Aizawa, K.: Joint optimization framework for learning with noisy labels. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5552–5560 (2018)

    Google Scholar 

  16. Xue, C., Dou, Q., Shi, X., Chen, H., Heng, P.A.: Robust learning at noisy labeled medical images: applied to skin lesion classification. In: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), pp. 1280–1283. IEEE (2019)

    Google Scholar 

  17. Zhang, C., Bengio, S., Hardt, M., Recht, B., Vinyals, O.: Understanding deep learning requires rethinking generalization. arXiv preprint arXiv:1611.03530 (2016)

  18. Zhu, H., Shi, J., Wu, J.: Pick-and-learn: automatic quality evaluation for noisy-labeled image segmentation. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11769, pp. 576–584. Springer, Cham (2019).

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Shi Gu .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zhang, T., Yu, L., Hu, N., Lv, S., Gu, S. (2020). Robust Medical Image Segmentation from Non-expert Annotations with Tri-network. In: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12264. Springer, Cham.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-59718-4

  • Online ISBN: 978-3-030-59719-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics