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Glioma Segmentation and a Simple Accurate Model for Overall Survival Prediction

  • Evan Gates
  • J. Gregory Pauloski
  • Dawid Schellingerhout
  • David FuentesEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11384)

Abstract

Brain tumor segmentation is a challenging task necessary for quantitative tumor analysis and diagnosis. We apply a multi-scale convolutional neural network based on the DeepMedic to segment glioma subvolumes provided in the 2018 MICCAI Brain Tumor Segmentation Challenge. We go on to extract intensity and shape features from the images and cross-validate machine learning models to predict overall survival. Using only the mean FLAIR intensity, nonenhancing tumor volume, and patient age we are able to predict patient overall survival with reasonable accuracy.

Keywords

Glioblastoma Segmentation Neural network Quantitative imaging 

References

  1. 1.
    Stupp, R., et al.: The European organisation for research and treatment of cancer brain tumor and radiotherapy groups, and “the national cancer institute of canada clinical trials group”. radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N. Engl. J. Med. 352(10), 987–996 (2005)Google Scholar
  2. 2.
    Shipitsin, M., et al.: Molecular definition of breast tumor heterogeneity. Cancer cell 11(3), 259–273 (2007)CrossRefGoogle Scholar
  3. 3.
    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_28CrossRefGoogle Scholar
  4. 4.
    Menze, B.H., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging 34(10), 1993–2024 (2015)CrossRefGoogle Scholar
  5. 5.
    Bakas, S., et al.: Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features. Sci. Data 4, 170117 (2017)CrossRefGoogle Scholar
  6. 6.
    Bakas, S., et al.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-GBM collection, The Cancer Imaging Archive (2017).  https://doi.org/10.7937/K9/TCIA.2017.KLXWJJ1Q
  7. 7.
    Bakas, S., et al.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-LGG collection, The Cancer Imaging Archive (2017).  https://doi.org/10.7937/K9/TCIA.2017.GJQ7R0EF
  8. 8.
    Bakas, S., Reyes, M., 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)
  9. 9.
    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)CrossRefGoogle Scholar
  10. 10.
    Leung, K.K., et al.: Alzheimer’s disease neuroimaging initiative. robust atrophy rate measurement in alzheimer’s disease using multi-site serial MRI: tissue-specific intensity normalization and parameter selection. NeuroImage 50(2), 516–523 (2010)CrossRefGoogle Scholar
  11. 11.
    van Griethuysen, J.J.M., et al.: Computational radiomics system to decode the radiographic phenotype. Cancer Res. 77(21), e104 LP–e107 (2017)CrossRefGoogle Scholar
  12. 12.
    Haralick, R., Shanmugan, K., Dinstein, I.: Textural features for image classification (1973)Google Scholar
  13. 13.
    Akaike, H.: A new look at the statistical model identification. IEEE Trans. Autom. Control 19(6), 716–723 (1974)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Kursa, M.B., Rudnicki, W.R., et al.: Feature selection with the Boruta package. J. Stat. Softw. 36(11), 1–13 (2010)CrossRefGoogle Scholar
  15. 15.
    Kamnitsas, K., et al.: Ensembles of multiple models and architectures for robust brain tumour segmentation. CoRR, abs/1711.01468 (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Evan Gates
    • 1
  • J. Gregory Pauloski
    • 1
  • Dawid Schellingerhout
    • 2
  • David Fuentes
    • 1
    Email author
  1. 1.Department of Imaging PhysicsUniversity of Texas MD Anderson Cancer CenterHoustonUSA
  2. 2.Department of Cancer Systems Imaging and Diagnostic RadiologyUniversity of Texas MD Anderson Cancer CenterHoustonUSA

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