Skip to main content
View expanded cover

International MICCAI Brainlesion Workshop

BrainLes 2018: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries pp 3–12Cite as

Segmentation of Brain Tumors and Patient Survival Prediction: Methods for the BraTS 2018 Challenge

Part of the Lecture Notes in Computer Science book series (LNIP,volume 11384)

Abstract

Brain tumor localization and segmentation is an important step in the treatment of brain tumor patients. It is the base for later clinical steps, e.g., a possible resection of the tumor. Hence, an automatic segmentation algorithm would be preferable, as it does not suffer from inter-rater variability. On top, results could be available immediately after the brain imaging procedure. Using this automatic tumor segmentation, it could also be possible to predict the survival of patients. The BraTS 2018 challenge consists of these two tasks: tumor segmentation in 3D-MRI images of brain tumor patients and survival prediction based on these images. For the tumor segmentation, we utilize a two-step approach: First, the tumor is located using a 3D U-net. Second, another 3D U-net – more complex, but with a smaller output size – detects subtle differences in the tumor volume, i.e., it segments the located tumor into tumor core, enhanced tumor, and peritumoral edema.

The survival prediction of the patients is done with a rather simple, yet accurate algorithm which outperformed other tested approaches on the train set when thoroughly cross-validated. This finding is consistent with our performance on the test set - we achieved 3rd place in the survival prediction task of the BraTS Challenge 2018.

Keywords

  • BraTS 2018
  • Brain tumor
  • Automatic segmentation
  • Survival prediction
  • Deep learning

This is a preview of subscription content, access via your institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-11726-9_1
  • Chapter length: 10 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   79.99
Price excludes VAT (USA)
  • ISBN: 978-3-030-11726-9
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   99.99
Price excludes VAT (USA)
Learn about institutional subscriptions
Fig. 1.
Fig. 2.
Fig. 3.

References

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

    Google Scholar 

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

    Google Scholar 

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

    CrossRef  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

  5. 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

    CrossRef  Google Scholar 

  6. Isensee, F., Kickingereder, P., Wick, W., Bendszus, M., Maier-Hein, K.H.: Brain tumor segmentation and radiomics survival prediction: contribution to the BRATS 2017 challenge. CoRR abs/1802.10508 (2018). http://arxiv.org/abs/1802.10508

  7. Isensee, F., Kickingereder, P., Wick, W., Bendszus, M., Maier-Hein, K.H.: No new-net. CoRR abs/1809.10483 (2018). http://arxiv.org/abs/1809.10483

  8. Isensee, F., et al.: nnU-Net: self-adapting framework for U-Net-based medical image segmentation. CoRR abs/1809.10486 (2018). http://arxiv.org/abs/1809.10486

  9. Kamnitsas, K., et al.: Ensembles of multiple models and architectures for robust brain tumour segmentation. In: Crimi, et al. [6], pp. 450–462. https://doi.org/10.1007/978-3-319-75238-9_38

    CrossRef  Google Scholar 

  10. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. CoRR abs/1412.6980 (2014). http://arxiv.org/abs/1412.6980

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

    CrossRef  Google Scholar 

  12. Milletari, F., Navab, N., Ahmadi, S.: V-Net: Fully convolutional neural networks for volumetric medical image segmentation. CoRR abs/1606.04797 (2016). http://arxiv.org/abs/1606.04797

  13. Paszke, A., et al.: Automatic differentiation in PyTorch. In: NIPS-W (2017)

    Google Scholar 

  14. 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, part III. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28

    CrossRef  Google Scholar 

  15. Ulyanov, D., Vedaldi, A., Lempitsky, V.S.: Instance normalization: the missing ingredient for fast stylization. CoRR abs/1607.08022 (2016). http://arxiv.org/abs/1607.08022

  16. Wang, G., Li, W., Ourselin, S., Vercauteren, T.: Automatic brain tumor segmentation using cascaded anisotropic convolutional neural networks. In: Crimi, et al. [6], pp. 178–190. https://doi.org/10.1007/978-3-319-75238-9_16

    CrossRef  Google Scholar 

  17. Yang, T.L., Ou, Y.N., Huang, T.Y.: Automatic segmentation of brain tumor from MR images using SegNet: selection of training data sets. In: Crimi, et al. [6], pp. 450–462. https://doi.org/10.1007/978-3-319-75238-9

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Imaging & Computer Vision, RWTH Aachen University, Aachen, Germany

    Leon Weninger, Oliver Rippel, Simon Koppers & Dorit Merhof

Authors
  1. Leon Weninger
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Oliver Rippel
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Simon Koppers
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Dorit Merhof
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Leon Weninger .

Editor information

Editors and Affiliations

  1. University Hospital of Zurich, Zürich, Switzerland

    Alessandro Crimi

  2. University of Pennsylvania, Philadelphia, PA, USA

    Spyridon Bakas

  3. University Medical Center Utrecht, Utrecht, The Netherlands

    Hugo Kuijf

  4. National Cancer Institute, Bethesda, MD, USA

    Farahani Keyvan

  5. University of Bern, Bern, Switzerland

    Mauricio Reyes

  6. Erasmus University Medical Center, Rotterdam, The Netherlands

    Theo van Walsum

Rights and permissions

Reprints and Permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Weninger, L., Rippel, O., Koppers, S., Merhof, D. (2019). Segmentation of Brain Tumors and Patient Survival Prediction: Methods for the BraTS 2018 Challenge. In: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., van Walsum, T. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2018. Lecture Notes in Computer Science(), vol 11384. Springer, Cham. https://doi.org/10.1007/978-3-030-11726-9_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-11726-9_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-11725-2

  • Online ISBN: 978-3-030-11726-9

  • eBook Packages: Computer ScienceComputer Science (R0)