Skip to main content
SpringerLink
Log in

Quality-Aware Model Ensemble for Brain Tumor Segmentation

  • Conference paper
  • First Online:
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (BrainLes 2021)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 12963))

Included in the following conference series:

  • 1724 Accesses

Abstract

Automatic segmentation of brain tumors is still a challenging task. To improve the segmentation performance and better ensemble all the candidate models with different architectures, we proposed a three-stage model with the quality-aware model ensemble. The first stage locates the tumor with coarse segmentation, while the second stage refines the coarse segmentation in the region of interest. The last stage performs the quality-aware model ensemble with a quality score prediction net to fuse the results from the multiple outputs of sub-networks. Besides, we warp a standard SRI24 brain template to the subject image, which is a strong prior of the brain structure and symmetry. Our method shows competitive performance on the BraTS 2021 online validation dataset, obtaining an average dice similarity coefficient (DSC) of 0.911, 0.850, 0.816, and average \(95_{th}\) percentile of Hausdorff distance (HD95) of 4.58, 8.959, 10.400, for whole tumor, tumor core, and enhancing tumor, respectively.

K. Wang, H. Wang—Equally contributed.

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 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
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. Baid, U., et al.: The RSNA-ASNR-MICCAI BraTS 2021 benchmark on brain tumor segmentation and radiogenomic classification. arXiv e-prints arXiv:2107.02314, July 2021

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

    Article  Google Scholar 

  3. Bakas, S., et al.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-LGG collection. Cancer Imaging Arch. (2017). https://doi.org/10.7937/K9/TCIA.2017.GJQ7R0EF

    Article  Google Scholar 

  4. Bakas, S., et al.: Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features. Sci. Data 4(1), 170117 (2017). https://doi.org/10.1038/sdata.2017.117

    Article  Google Scholar 

  5. Carreira, J., Noland, E., Banki-Horvath, A., Hillier, C., Zisserman, A.: A short note about kinetics-600. arXiv e-prints arXiv:1808.01340, August 2018

  6. Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 424–432. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_49

    Chapter  Google Scholar 

  7. Futrega, M., Milesi, A., Marcinkiewicz, M., Ribalta, P.: Optimized U-Net for brain tumor segmentation. arXiv preprint arXiv:2110.03352 (2021)

  8. 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 

  9. Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017)

    Google Scholar 

  10. Isensee, F., Jaeger, P.F., Kohl, S.A., Petersen, J., Maier-Hein, K.H.: nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18(2), 203–211 (2021)

    Article  Google Scholar 

  11. Isensee, F., Jäger, P.F., Full, P.M., Vollmuth, P., Maier-Hein, K.H.: nnU-Net for brain tumor segmentation. In: Crimi, A., Bakas, S. (eds.) BrainLes 2020. LNCS, vol. 12659, pp. 118–132. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-72087-2_11

    Chapter  Google Scholar 

  12. Jenkinson, M., Bannister, P., Brady, M., Smith, S.: Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage 17(2), 825–841 (2002). https://doi.org/10.1006/nimg.2002.1132

    Article  Google Scholar 

  13. Jenkinson, M., Beckmann, C.F., Behrens, T.E.J., Woolrich, M.W., Smith, S.M.: FSL. NeuroImage 62(2), 782–790 (2012). https://doi.org/10.1016/j.neuroimage.2011.09.015. https://www.sciencedirect.com/science/article/pii/S1053811911010603

  14. Jenkinson, M., Smith, S.: A global optimisation method for robust affine registration of brain images. Med. Image Anal. 5(2), 143–156 (2001). https://doi.org/10.1016/S1361-8415(01)00036-6

    Article  Google Scholar 

  15. Jia, H., Cai, W., Huang, H., Xia, Y.: H\(^2\)NF-Net for brain tumor segmentation using multimodal MR imaging: 2nd place solution to BraTS challenge 2020 segmentation task. In: Crimi, A., Bakas, S. (eds.) BrainLes 2020. LNCS, vol. 12659, pp. 58–68. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-72087-2_6

    Chapter  Google Scholar 

  16. Jia, H., Xia, Y., Cai, W., Huang, H.: Learning high-resolution and efficient non-local features for brain glioma segmentation in MR images. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12264, pp. 480–490. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59719-1_47

    Chapter  Google Scholar 

  17. Jiang, Z., Ding, C., Liu, M., Tao, D.: Two-stage cascaded U-Net: 1st place solution to BraTS challenge 2019 segmentation task. In: Crimi, A., Bakas, S. (eds.) BrainLes 2019. LNCS, vol. 11992, pp. 231–241. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-46640-4_22

    Chapter  Google Scholar 

  18. Li, C., et al.: Multi-parametric and multi-regional histogram analysis of MRI: modality integration reveals imaging phenotypes of glioblastoma. Eur. Radiol. 29(9), 4718–4729 (2019). https://doi.org/10.1007/s00330-018-5984-z

    Article  Google Scholar 

  19. 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 

  20. Menze, B.H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., 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 

  21. 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 

  22. 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 

  23. Ricard, D., Idbaih, A., Ducray, F., Lahutte, M., Hoang-Xuan, K., Delattre, J.Y.: Primary brain tumours in adults. Lancet 379(9830), 1984–1996 (2012)

    Article  Google Scholar 

  24. Rohlfing, T., Zahr, N.M., Sullivan, E.V., Pfefferbaum, A.: The SRI24 multichannel atlas of normal adult human brain structure. Hum. Brain Mapp. 31(5), 798–819 (2010). https://doi.org/10.1002/hbm.20906

    Article  Google Scholar 

  25. 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 

  26. Sudre, C.H., Li, W., Vercauteren, T., Ourselin, S., Jorge Cardoso, M.: Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations. In: Cardoso, M.J., et al. (eds.) DLMIA/ML-CDS -2017. LNCS, vol. 10553, pp. 240–248. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67558-9_28

    Chapter  Google Scholar 

  27. Sun, K., et al.: High-resolution representations for labeling pixels and regions. arxiv 2019. arXiv preprint arXiv:1904.04514 (2019)

  28. Wang, Y., et al.: Modality-pairing learning for brain tumor segmentation. arXiv preprint arXiv:2010.09277 (2020)

  29. Zhang, J., Lv, X., Sun, Q., Zhang, Q., Wei, X., Liu, B.: SDResU-Net: separable and dilated residual U-Net for MRI brain tumor segmentation. Curr. Med. Imaging 16(6), 720–728 (2020)

    Article  Google Scholar 

  30. Zhao, Y.-X., Zhang, Y.-M., Liu, C.-L.: Bag of tricks for 3D MRI brain tumor segmentation. In: Crimi, A., Bakas, S. (eds.) BrainLes 2019. LNCS, vol. 11992, pp. 210–220. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-46640-4_20

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China

    Kang Wang, Haoran Wang, Manning Wang, Shuo Wang & Zhijian Song

  2. Shanghai Key Lab of Medical Image Computing and Computer Assisted Intervention, Shanghai, China

    Kang Wang, Haoran Wang, Manning Wang, Shuo Wang & Zhijian Song

  3. Department of Neurosurgery, Zhongshan Hospital, Fudan University, Shanghai, China

    Zeyang Li

  4. Radiation Oncology Center, Huashan Hospital, Fudan University, Shanghai, China

    Mingyuan Pan

Authors
  1. Kang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Haoran Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zeyang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mingyuan Pan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Manning Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Shuo Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Zhijian Song
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Manning Wang , Shuo Wang or Zhijian Song .

Editor information

Editors and Affiliations

  1. Sano Centre for Computational Personaliz, Kraków, Poland

    Alessandro Crimi

  2. University of Pennsylvania, Philadelphia, PA, USA

    Spyridon Bakas

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Wang, K. et al. (2022). Quality-Aware Model Ensemble for Brain Tumor Segmentation. In: Crimi, A., Bakas, S. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2021. Lecture Notes in Computer Science, vol 12963. Springer, Cham. https://doi.org/10.1007/978-3-031-09002-8_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-09002-8_14

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-09001-1

  • Online ISBN: 978-3-031-09002-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Societies and partnerships

Search

Navigation