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Noisy Labels are Treasure: Mean-Teacher-Assisted Confident Learning for Hepatic Vessel Segmentation

Part of the Lecture Notes in Computer Science book series (LNIP,volume 12901)


Manually segmenting the hepatic vessels from Computer Tomography (CT) is far more expertise-demanding and laborious than other structures due to the low-contrast and complex morphology of vessels, resulting in the extreme lack of high-quality labeled data. Without sufficient high-quality annotations, the usual data-driven learning-based approaches struggle with deficient training. On the other hand, directly introducing additional data with low-quality annotations may confuse the network, leading to undesirable performance degradation. To address this issue, we propose a novel mean-teacher-assisted confident learning framework to robustly exploit the noisy labeled data for the challenging hepatic vessel segmentation task. Specifically, with the adapted confident learning assisted by a third party, i.e., the weight-averaged teacher model, the noisy labels in the additional low-quality dataset can be transformed from ‘encumbrance’ to ‘treasure’ via progressive pixel-wise soft-correction, thus providing productive guidance. Extensive experiments using two public datasets demonstrate the superiority of the proposed framework as well as the effectiveness of each component.


  • Hepatic vessel
  • Noisy label
  • Confident learning

Z. Xu—This work was done as a research intern at Tencent Jarvis Lab.

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  1. 3DIRCADb Dataset.

  2. Ainam, J.P., Qin, K., Liu, G., Luo, G.: Sparse label smoothing regularization for person re-identification. IEEE Access 7, 27899–27910 (2019)

    CrossRef  Google Scholar 

  3. Angluin, D., Laird, P.: Learning from noisy examples. Mach. Learn. 2(4), 343–370 (1988)

    Google Scholar 

  4. 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).

    CrossRef  Google Scholar 

  5. Dou, Q., Chen, H., Jin, Y., Yu, L., Qin, J., Heng, P.-A.: 3D deeply supervised network for automatic liver segmentation from CT volumes. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 149–157. Springer, Cham (2016).

    CrossRef  Google Scholar 

  6. Duan, X., Wang, J., Leng, S., Schmidt, B., Allmendinger, T., Grant, K., Flohr, T., McCollough, C.H.: Electronic noise in CT detectors: impact on image noise and artifacts. Am. J. Roentgenology 201(4), W626–W632 (2013)

    CrossRef  Google Scholar 

  7. Goldberger, J., Ben-Reuven, E.: Training deep neural-networks using a noise adaptation layer. In: International Conference on Learning Representations (2016)

    Google Scholar 

  8. Huang, Q., Sun, J., Ding, H., Wang, X., Wang, G.: Robust liver vessel extraction using 3D U-Net with variant Dice loss function. Comput. Biology Med. 101, 153–162 (2018)

    CrossRef  Google Scholar 

  9. Jin, Q., Meng, Z., Pham, T.D., Chen, Q., Wei, L., Su, R.: DUNet: a deformable network for retinal vessel segmentation. Knowl.-Based Syst. 178, 149–162 (2019)

    CrossRef  Google Scholar 

  10. Kervadec, H., Bouchtiba, J., Desrosiers, C., Granger, E., Dolz, J., Ayed, I.B.: Boundary loss for highly unbalanced segmentation. In: International Conference on Medical Imaging with Deep Learning, pp. 285–296. PMLR (2019)

    Google Scholar 

  11. Kitrungrotsakul, T., Han, X.H., Iwamoto, Y., Foruzan, A.H., Lin, L., Chen, Y.W.: Robust hepatic vessel segmentation using multi deep convolution network. In: Medical Imaging 2017: Biomedical Applications in Molecular, Structural, and Functional Imaging, vol. 10137, p. 1013711. International Society for Optics and Photonics (2017)

    Google Scholar 

  12. Kitrungrotsakul, T., Han, X.H., Iwamoto, Y., Lin, L., Foruzan, A.H., Xiong, W., Chen, Y.W.: Vesselnet: a deep convolutional neural network with multi pathways for robust hepatic vessel segmentation. Computerized Med. Imaging Graph. 75, 74–83 (2019)

    CrossRef  Google Scholar 

  13. Li, X., Chen, H., Qi, X., Dou, Q., Fu, C.W., Heng, P.A.: H-DenseUNet: hybrid densely connected UNet for liver and tumor segmentation from CT volumes. IEEE Trans. Med. Imaging 37(12), 2663–2674 (2018)

    CrossRef  Google Scholar 

  14. Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017)

    Google Scholar 

  15. Liu, L., Tian, J., Zhong, C., Shi, Z., Xu, F.: Robust hepatic vessels segmentation model based on noisy dataset. In: Medical Imaging 2020: Computer-Aided Diagnosis, vol. 11314, p. 113140L. International Society for Optics and Photonics (2020)

    Google Scholar 

  16. Livne, M., Rieger, J., Aydin, O.U., Taha, A.A., Akay, E.M., Kossen, T., Sobesky, J., Kelleher, J.D., Hildebrand, K., Frey, D., et al.: A U-Net deep learning framework for high performance vessel segmentation in patients with cerebrovascular disease. Frontiers Neurosci. 13, 97 (2019)

    CrossRef  Google Scholar 

  17. Northcutt, C.G., Jiang, L., Chuang, I.L.: Confident learning: estimating uncertainty in dataset labels. arXiv preprint arXiv:1911.00068 (2019)

  18. Ren, M., Zeng, W., Yang, B., Urtasun, R.: Learning to reweight examples for robust deep learning. In: International Conference on Machine Learning, pp. 4334–4343. PMLR (2018)

    Google Scholar 

  19. 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).

    CrossRef  Google Scholar 

  20. Sato, Y., Nakajima, S., Shiraga, N., Atsumi, H., Yoshida, S., Koller, T., Gerig, G., Kikinis, R.: Three-dimensional multi-scale line filter for segmentation and visualization of curvilinear structures in medical images. Med. Image Anal. 2(2), 143–168 (1998)

    CrossRef  Google Scholar 

  21. Simpson, A.L., et al.: A large annotated medical image dataset for the development and evaluation of segmentation algorithms. arXiv preprint arXiv:1902.09063 (2019)

  22. Tarvainen, A., Valpola, H.: Mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results. In: Advances in Neural Information Processing Systems, pp. 1195–1204 (2017)

    Google Scholar 

  23. Wang, S., Cao, S., Chai, Z., Wei, D., Ma, K., Wang, L., Zheng, Y.: Conquering data variations in resolution: a slice-aware multi-branch decoder network. IEEE Trans. Med. Imaging 39(12), 4174–4185 (2020)

    CrossRef  Google Scholar 

  24. Wang, Y., Zhang, Y., Tian, J., Zhong, C., Shi, Z., Zhang, Y., He, Z.: Double-uncertainty weighted method for semi-supervised learning. In: International Conference on Medical Image Computing and Computer Assisted Intervention, pp. 542–551. Springer (2020)

    Google Scholar 

  25. Yu, L., Wang, S., Li, X., Fu, C.-W., Heng, P.-A.: Uncertainty-aware self-ensembling model for semi-supervised 3D left atrium segmentation. In: Shen, D., Liu, T., Peters, T.M., Staib, L.H., Essert, C., Zhou, S., Yap, P.-T., Khan, A. (eds.) MICCAI 2019. LNCS, vol. 11765, pp. 605–613. Springer, Cham (2019).

    CrossRef  Google Scholar 

  26. Zhang, M., Gao, J., Lyu, Z., Zhao, W., Wang, Q., Ding, W., Wang, S., Li, Z., Cui, S.: Characterizing label errors: confident learning for noisy-labeled image segmentation. In: Martel, A.L., Abolmaesumi, P., Stoyanov, D., Mateus, D., Zuluaga, M.A., Zhou, S.K., Racoceanu, D., Joskowicz, L. (eds.) MICCAI 2020. LNCS, vol. 12261, pp. 721–730. Springer, Cham (2020).

    CrossRef  Google Scholar 

  27. Zhou, T., Ruan, S., Canu, S.: A review: Deep learning for medical image segmentation using multi-modality fusion. Array 3, 100004 (2019)

    Google Scholar 

  28. Zhu, H., Shi, J., Wu, J.: Pick-and-learn: automatic quality evaluation for noisy-labeled image segmentation. In: Shen, D., Liu, T., Peters, T.M., Staib, L.H., Essert, C., Zhou, S., Yap, P.-T., Khan, A. (eds.) MICCAI 2019. LNCS, vol. 11769, pp. 576–584. Springer, Cham (2019).

    CrossRef  Google Scholar 

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This project was partly supported by the National Natural Science Foundation of China (Grant No. 41876098), the National Key R&D Program of China (Grant No. 2020AAA0108303), Shenzhen Science and Technology Project (Grant No. JCYJ20200109143041798), Key-Area Research and Development Program of Guangdong Province, China (No. 2018B010111001), and the Scientific and Technical Innovation 2030- “New Generation Artificial Intelligence” Project (No. 2020AAA0104100).

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Correspondence to Donghuan Lu or Xiu Li .

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Xu, Z. et al. (2021). Noisy Labels are Treasure: Mean-Teacher-Assisted Confident Learning for Hepatic Vessel Segmentation. In: de Bruijne, M., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science(), vol 12901. Springer, Cham.

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