Skip to main content
SpringerLink
Log in

Two-Stage Cascaded U-Net: 1st Place Solution to BraTS Challenge 2019 Segmentation Task

  • Conference paper
  • First Online:
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (BrainLes 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11992))

Included in the following conference series:

Abstract

In this paper, we devise a novel two-stage cascaded U-Net to segment the substructures of brain tumors from coarse to fine. The network is trained end-to-end on the Multimodal Brain Tumor Segmentation Challenge (BraTS) 2019 training dataset. Experimental results on the testing set demonstrate that the proposed method achieved average Dice scores of 0.83267, 0.88796 and 0.83697, as well as Hausdorff distances (95%) of 2.65056, 4.61809 and 4.13071, for the enhancing tumor, whole tumor and tumor core, respectively. The approach won the 1st place in the BraTS 2019 challenge segmentation task, with more than 70 teams participating in the challenge.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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

References

  1. Bakas, S., et al.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-GBM collection. Cancer Imaging Archive (2017). https://doi.org/10.7937/K9/TCIA.2017.KLXWJJ1Q

  2. Bakas, S., et al.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-LGG collection. Cancer Imaging Archive 286 (2017)

    Google Scholar 

  3. Bakas, S., et al.: Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features. Sci. Data 4, 170117 (2017). https://doi.org/10.1038/sdata.2017.117

    Article  Google Scholar 

  4. Bakas, S., et al.: Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the brats challenge. arXiv preprint arXiv:1811.02629 (2018)

  5. Doersch, C.: Tutorial on variational autoencoders. arXiv preprint arXiv:1606.05908 (2016)

  6. Havaei, M., et al.: Brain tumor segmentation with deep neural networks. Med. Image Anal. 35, 18–31 (2017)

    Article  Google Scholar 

  7. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

    Google Scholar 

  8. He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 630–645. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46493-0_38

    Chapter  Google Scholar 

  9. Kamnitsas, K., et al.: Ensembles of multiple models and architectures for robust brain tumour segmentation. In: Crimi, A., Bakas, S., Kuijf, H., Menze, B., Reyes, M. (eds.) BrainLes 2017. LNCS, vol. 10670, pp. 450–462. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-75238-9_38

    Chapter  Google Scholar 

  10. Kamnitsas, K., et al.: Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med. Image Anal. 36, 61–78 (2017)

    Article  Google Scholar 

  11. Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013)

  12. Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)

    Google Scholar 

  13. Menze, B.H., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging 34(10), 1993–2024 (2015). https://doi.org/10.1109/tmi.2014.2377694

    Article  Google Scholar 

  14. Milletari, F., Navab, N., Ahmadi, S.A.: V-net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV), pp. 565–571. IEEE (2016)

    Google Scholar 

  15. Myronenko, A.: 3D MRI brain tumor segmentation using autoencoder regularization. In: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., van Walsum, T. (eds.) BrainLes 2018. LNCS, vol. 11384, pp. 311–320. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11726-9_28

    Chapter  Google Scholar 

  16. Paszke, A., et al.: Automatic differentiation in PyTorch (2017)

    Google Scholar 

  17. Pereira, S., Pinto, A., Alves, V., Silva, C.A.: Brain tumor segmentation using convolutional neural networks in mri images. IEEE Trans. Med. Imaging 35(5), 1240–1251 (2016)

    Article  Google Scholar 

  18. 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_28

    Chapter  Google Scholar 

  19. Roth, H.R., et al.: A multi-scale pyramid of 3D fully convolutional networks for abdominal multi-organ segmentation. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11073, pp. 417–425. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00937-3_48

    Chapter  Google Scholar 

  20. Shen, H., Wang, R., Zhang, J., McKenna, S.J.: Boundary-aware fully convolutional network for brain tumor segmentation. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10434, pp. 433–441. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66185-8_49

    Chapter  Google Scholar 

  21. Siskind, J.M., Pearlmutter, B.A.: Divide-and-conquer checkpointing for arbitrary programs with no user annotation. Optim. Methods Softw. 33(4–6), 1288–1330 (2018)

    Article  MathSciNet  Google Scholar 

  22. Tu, Z., Bai, X.: Auto-context and its application to high-level vision tasks and 3D brain image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 32(10), 1744–1757 (2009)

    Google Scholar 

  23. Wu, Y., He, K.: Group normalization. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11217, pp. 3–19. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01261-8_1

    Chapter  Google Scholar 

  24. Zhao, X., Wu, Y., Song, G., Li, Z., Zhang, Y., Fan, Y.: A deep learning model integrating FCNNS and CRFS for brain tumor segmentation. Med. Image Anal. 43, 98–111 (2018)

    Article  Google Scholar 

  25. Zhou, C., Chen, S., Ding, C., Tao, D.: Learning contextual and attentive information for brain tumor segmentation. In: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., van Walsum, T. (eds.) BrainLes 2018. LNCS, vol. 11384, pp. 497–507. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11726-9_44

    Chapter  Google Scholar 

  26. Zhou, C., Ding, C., Lu, Z., Wang, X., Tao, D.: One-pass multi-task convolutional neural networks for efficient brain tumor segmentation. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11072, pp. 637–645. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00931-1_73

    Chapter  Google Scholar 

  27. Zhou, C., Ding, C., Wang, X., Lu, Z., Tao, D.: One-pass multi-task networks with cross-task guided attention for brain tumor segmentation. arXiv preprint arXiv:1906.01796 (2019)

Download references

Acknowledgements

Changxing Ding was supported in part by the National Natural Science Foundation of China (Grant No.: 61702193), Science and Technology Program of Guangzhou (Grant No.: 201804010272), and the Program for Guangdong Introducing Innovative and Entrepreneurial Teams (Grant No.: 2017ZT07X183). Dacheng Tao was supported by Australian Research Council Projects (FL-170100117, DP-180103424 and LP-150100671).

Author information

Authors and Affiliations

  1. School of Electronic and Information Engineering, South China University of Technology, Guangzhou, China

    Zeyu Jiang & Changxing Ding

  2. Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong, China

    Minfeng Liu

  3. UBTECH Sydney AI Centre, SIT, FEIT, University of Sydney, Sydney, Australia

    Dacheng Tao

Authors
  1. Zeyu Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Changxing Ding
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Minfeng Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Dacheng Tao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Changxing Ding .

Editor information

Editors and Affiliations

  1. University Hospital of Zurich, Zurich, Switzerland

    Alessandro Crimi

  2. University of Pennsylvania, Philadelphia, PA, USA

    Spyridon Bakas

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

Jiang, Z., Ding, C., Liu, M., Tao, D. (2020). Two-Stage Cascaded U-Net: 1st Place Solution to BraTS Challenge 2019 Segmentation Task. In: Crimi, A., Bakas, S. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2019. Lecture Notes in Computer Science(), vol 11992. Springer, Cham. https://doi.org/10.1007/978-3-030-46640-4_22

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-46640-4_22

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-46639-8

  • Online ISBN: 978-3-030-46640-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Societies and partnerships

Search

Navigation